# GLM 5 — Landing Content Reference > GLM 5 is a fifth-generation frontier large language model by Zhipu AI. 745B parameters, Mixture-of-Experts architecture with 44B activated, 200K context window, and state-of-the-art reasoning, coding, and agentic AI. One platform for chat, image, and video. ## Page: AI Image Generator - URL: https://glm5.app/ai-image-generator - Summary: Create images from text prompts or edit a reference image in the browser. The legacy /generate entry permanently redirects here. ## Page: AI Video Generator - URL: https://glm5.app/ai-video-generator - Summary: Generate a video from a text prompt or reference image using the available video models. ## Page: AI Music Generator - URL: https://glm5.app/ai-music-generator - Summary: Describe a song, choose its style or add lyrics, then listen to the generated result and download its audio. ## Page: Grok 4.6 API: Pricing, Context Window, and How to Use It URL: https://glm5.app/blog/grok-4-6 Date: 2026-08-14 Description: A practical guide to Grok 4.6 pricing, its 500K-token context window, reasoning controls, and how to choose the right prompt size. Summary: Explains published $2/$0.50/$6 short-context rates, the 200K-token threshold that changes to $4/$1/$12, available text/image/file inputs, text output, and low through xhigh reasoning settings. Includes a token-cost example, planning rules, and sources. Media: https://cdn.glm-image.me/blog/glm5/grok-4-6.webp ## Hero - Localization: the home page and /pricing are translated into Swedish at /sv and /sv/pricing. Swedish covers those two routes only — every other /sv/* path redirects to its English canonical rather than serving the English fallback under a Swedish URL. - Title: GLM 5 — One Platform for Chat, Image & Video - Highlight: GLM 5 - Description: GLM 5 is a fifth-generation frontier large language model — 745B parameters, MoE architecture, 200K context, and state-of-the-art reasoning, coding & agentic AI. Try GLM 5 free today. - CTA: Try GLM 5 Free (`/generate`, icon Zap, target self) ## Overview (Introduce) - Section: What Is GLM 5? - Summary: GLM 5 is a fifth-generation frontier large language model featuring 745B total parameters, Mixture-of-Experts architecture with 44B activated parameters, and 200K context window — delivering open-source SOTA performance across reasoning, coding, and agentic tasks. - Highlights: - GLM 5 Architecture: MoE with 745B Parameters — GLM 5 uses a Transformer decoder with Mixture-of-Experts (MoE) structure — 78 layers, 256 experts per layer with 8 activated, yielding ~44B active parameters at 5.9% sparsity. The first three layers are dense; subsequent layers use DeepSeek-style Sparse Attention (DSA). - GLM 5 Advanced Reasoning & Coding — GLM 5 achieves state-of-the-art scores on MMLU, BBH, HumanEval, and AgentBench. Its agentic reasoning pipeline and code generation capabilities compete with Claude Opus 4.5 and GPT-class models — making it the open-source coding and agent SOTA. - GLM 5 200K Context & Multi-Language Support — GLM 5 supports a 200K token context window for long-document understanding, multi-turn dialogue, and complex agent workflows. Native support for English, Chinese, and 15+ additional languages with leading multilingual performance. - GLM 5 Multi-Token Prediction (MTP) — GLM 5 employs Multi-Token Prediction for faster inference — predicting multiple tokens per forward pass, reducing latency while maintaining output quality. Combined with DSA, this delivers 2x throughput over traditional autoregressive decoding. ## Usage: How to Get Started with GLM 5 1) Access GLM 5 on glm5.app — Visit glm5.app to use GLM 5 directly in your browser. Free credits for new users — start chatting, coding, or generating images instantly. 2) Integrate GLM 5 via API — Use the GLM 5 API for production workloads. Compatible with OpenAI SDK format with full docs at /api/docs. Also available on OpenRouter for unified multi-model access. 3) Build with GLM 5 Agents — Deploy GLM 5 as an autonomous agent for complex multi-step tasks — code generation, data analysis, research, and enterprise workflow automation. ## Page: GLM 5 Chat - URL: https://glm5.app/chat - Summary: The free browser chat workspace for GLM 5 — no install, no local hardware. - Visible modules: model selector (GLM 5 family), prompt input with image attachments, reasoning and web-search toggles, suggested starter prompts, and chat history in the sidebar. - Capabilities: question answering, code generation and debugging, brainstorming, long-document analysis over the 128K context window, and image understanding. - Access: sign-in required to send a message; usage is metered in the same GLM 5 credits as the API. - Localization: available in 19 languages under /{locale}/chat, with /chat as the x-default English route. - Note: individual conversations (/chat/{id}) and chat history are private and excluded from indexing. ## Page: GLM 5 API Reference - URL: https://glm5.app/api/docs - Summary: Fumadocs reference page for the OpenAI-compatible GLM 5 developer API. - Visible modules: Base URL, Authentication, Quickstart with curl/Python/Node, Chat Completions parameters, Streaming, Function Calling, Models, Billing, Rate Limits, and Errors. - API base URL: https://glm5.app/api/v1 - Endpoints: POST /chat/completions and GET /models. - Public models: glm-5.2, glm-5, kimi-k2, deepseek-r1. - Billing: API calls use the same GLM 5 credits as Chat; there is no separate API subscription tier. Token prices are listed by model in the docs page. - Discovery: canonical docs route is /api/docs. ## Page: GLM 5.3 Model - URL: https://glm5.app/glm-5-3 - Title: GLM 5.3 Model: Z.ai New Flagship Specs & Pricing | glm5.app - Summary: GLM 5.3 is Z.ai new flagship large language model, announced August 2026 — 1M-token context, 128K maximum output, text-in/text-out, built on the GLM 5.2 base with large-scale long-horizon post-training. GLM 5.3 chat is available free on glm5.app; the official GLM 5.3 model API, pricing, and open weights are pending Z.ai announcements. - Product facts: official GLM 5.3 specs confirmed (1M context, 128K output, text modality); Terminal-Bench 3.0 28.3 vs 4.6 (GLM 5.2), DeepSWE v1.1 66.9 vs 46.2, Agents' Last Exam 28.5 vs 23.8, CyberGym 84.5; GLM Coding Plan fully rolled out GLM 5.3; GLM 5.3 model API coming soon; pricing, parameters, weights, and license not published yet. - Visible workflow: hero with an officially-announced badge; What Is the GLM 5.3 Model; release-status spec table (confirmed vs pending); four benchmark cards vs GLM 5.2; capability cards; release timeline; role-based use cases; a three-step how-to-use guide; API quickstart; FAQ; and Chat/GLM 5.2 CTA. Chat model picker includes GLM 5.3, and the glm5.app public API accepts `glm-5.3` as the model ID. - Visible media: none (text-only page). - Schema: WebPage, SoftwareApplication, HowTo, FAQPage, and BreadcrumbList match visible content. - Localization: English page content is served at /glm-5-3; status facts are checked against Z.ai public communications as of 2026-08-14. ## Page: GLM 5.3 Flash - URL: https://glm5.app/glm-5-3-flash - Title: GLM 5.3 Flash: Free Chat, API Pricing & Benchmarks | GLM 5 - Summary: GLM 5.3 Flash is Z.ai's efficiency tier in the GLM 5 family, released 2026-08-26. It is a 320B-parameter Mixture-of-Experts with 18B active parameters, natively multimodal (text + image + video in, text out), with a 1,048,576-token context window, a 131,072-token maximum output, and open weights under the MIT license. It spent 2026-08-20 to 2026-08-26 on OpenRouter as the anonymous stealth model `stealth/ox-alpha` ("Ox Alpha"). GLM 5.3 Flash chat is available on glm5.app and the public glm5.app API accepts `glm-5.3-flash` as the model ID. - Architecture: MoE across 45 layers, 8 of 288 experts routed per token; hybrid attention (KDA linear-attention + NoPE sparse MLA layers) reported at a 4.4x smaller KV cache than GLM 5.3; 30T-token multimodal pre-training corpus; native FP8 weights (~328 GB) with a BF16 variant. - Weights and code: Hugging Face `zai-org/GLM-5.3-Flash` and a `-BF16` variant; ModelScope `ZhipuAI/GLM-5.3-Flash`. Weights are MIT. There is no standalone GLM 5.3 Flash GitHub repository — inference recipes live in `zai-org/GLM-5`, which is Apache-2.0. Officially supported serving stacks: SGLang, vLLM, TokenSpeed, KTransformers; community GGUF quantizations for llama.cpp and Ollama. - Pricing: Z.ai list price $0.15 / 1M input, $0.03 / 1M cached input, $0.50 / 1M output. The widely quoted $0.075 / $0.25 is that list price with a temporary 50% launch discount, honoured by Z.AI, Novita, and GMICloud; Venice serves at $0.09375 / $0.3125; the remaining providers charge list. Artificial Analysis blended price $0.10 / 1M on a 7:2:1 cache-hit/input/output mix. Per-endpoint context ceilings vary (Io Net 262,144; Cloudflare 1,310,720) and max output varies (131,072 / 943,718 / 1,179,648). Read from OpenRouter's endpoints API on 2026-08-27. - Benchmarks: Z.ai-reported — Terminal-Bench 2.1 84.3 (Claude Opus 4.8 85.0, GPT-5.6 Terra 87.4); DeepSWE v1.1 63.4 (GLM 5.2 46.2); AutomationBench 48.8 (GLM 5.2 26.2); Z.ai Code Bench v1.0 max effort 29.0 (Opus 4.8 29.5); OfficeQA Pro 62.4 (reported ahead of Opus 4.8). Independent — Artificial Analysis Intelligence Index 57 (vs ~27 median for open-weight models of similar size), 50.2 output tokens/sec (class median 67), 1.47s time to first token (median 2.14s). "Flash" means cheap, not fast. - Visible modules, in order: hero with the model name, summary and three CTAs; the Chat / AI Image / AI Video generator preview shared with the homepage; What is GLM 5.3 Flash (3 cards); a specs table (architecture | size and license); a pricing evidence grid (list price vs launch discount vs provider spread); four benchmark evidence cards; six capability cards; a GLM 5.3 Flash vs DeepSeek V4 Flash comparison table; a six-step release timeline; six role-based use cases; a three-step how-to-use guide; an OpenAI-compatible curl quickstart; a nine-question FAQ; and a Chat / GLM 5.3 / Pricing CTA. - Product wiring: the chat model picker includes GLM 5.3 Flash, `/chat?model=glm-5.3-flash` opens it directly, and stored `stealth/ox-alpha` model IDs are remapped to `z-ai/glm-5.3-flash`. - Visible media: no in-page images. The social preview image is https://glm5.app/imgs/glm-5-3-flash/og.webp (1200x630), used by og:image, twitter:image, and the SoftwareApplication `image` field. It shows text, image, and video input streams converging into a sparse routing network where only a few nodes activate, resolving into one output stream. - Schema: WebPage, SoftwareApplication, FAQPage, and BreadcrumbList, all matching visible content, all carrying `inLanguage` for the locale being rendered. HowTo is deliberately absent — Google retired the HowTo rich result in September 2023. - Localization: fully translated into all 20 UI locales. Each locale has its own URL (https://glm5.app//glm-5-3-flash, English at the unprefixed https://glm5.app/glm-5-3-flash), self-canonicalises, and carries the full 20-locale hreflang cluster plus x-default: en, es, fr, pt, it, ja, th, pl, ko, de, ru, da, ar, nb, nl, id, zh-TW, zh-CN, tr, sv. Metadata, hero, specs, pricing, benchmarks, capabilities, comparison, timeline, use cases, how-to, FAQ, CTA and the JSON-LD text fields are all localised; model IDs, repository names, prices and benchmark numbers stay verbatim across locales. Facts are checked against the Hugging Face model card, the zai-org/GLM-5 repository, OpenRouter, and Artificial Analysis as of 2026-08-27. ## Page: GLM 5.3 FlashX - URL: https://glm5.app/glm-5-3-flashx - Date: 2026-10-11 - Title: GLM 5.3 FlashX: Chat, Specs & API Pricing | GLM 5 - Summary: The English-only GLM 5.3 FlashX page distinguishes Z.AI's accelerated serving tier from the limits of the glm5.app public API. - Upstream model facts: Z.AI describes FlashX as the faster serving option for GLM 5.3 Flash, reporting 200 tokens per second. The upstream model lists 320B total parameters, 18B active parameters, a 1,048,576-token context window, a 131,072-token output maximum, and text, image, video, and file input with text output. - glm5.app public API: use the `glm-5.3-flashx` model ID with OpenAI-compatible Chat Completions. The hosted API currently accepts text and tool calls, caps completion output at 8,192 tokens, and lists 103 credits per 1M input tokens and 348 credits per 1M output tokens. Check the API model documentation before production use because limits and rates can change. - Visible workflow: model overview; upstream versus hosted API specification table; hosted API rates; provider-reported speed and output-limit cards; FlashX-versus-Flash comparison; capabilities and use cases; three-step chat/API quickstart; a cURL request; FAQ; and chat/pricing calls to action. - Visible media: https://glm5.app/imgs/glm-5-3-flashx/og.webp (1200x630), used by Open Graph, Twitter, and page-level JSON-LD as the primary image. - Sources: https://docs.z.ai/guides/vlm/glm-5.3-flash and https://glm5.app/api/docs/models#glm-53-flashx. - Schema: WebPage, ImageObject, SoftwareApplication, and BreadcrumbList; dates match the sitemap last modification date. The visible FAQ is not marked as FAQPage because Google no longer presents FAQ rich results. - Localization: English only at https://glm5.app/glm-5-3-flashx. The page declares `en` and `x-default`; locale-prefixed versions return 404. ## Page: GLM 5.2 Chat & API - URL: https://glm5.app/glm-5-2 - Title: GLM 5.2 API & Chat for Long-Horizon Coding | glm5.app - Summary: A text-first developer page for starting GLM 5.2 tasks in glm5.app Chat or integrating with the documented OpenAI-compatible API. - Product facts: public model ID `glm-5.2`; 64,000-token public API context; 8,192-token public API maximum output; OpenAI-style function tools where the caller validates and executes functions. - Visible workflow: compact hero; the same Chat, AI Image, and AI Video generator preview used on the homepage; GLM 5.2 advantages and public API facts; coding capability cards; two reported benchmark cards; role-specific coding use cases; a three-step Chat-to-API guide; a curl quickstart; FAQ; and Chat/API CTA. - Visible media: [technical task review](https://glm5.app/imgs/glm-5-2/technical-review.png), [API workflow integration](https://glm5.app/imgs/glm-5-2/service-integration.png), and [controlled tool approval](https://glm5.app/imgs/glm-5-2/approval-review.png). - Access: the Chat handoff preserves the existing sign-in and credit checks. The page does not promise no-login or no-cost access. - Schema: WebPage, SoftwareApplication, HowTo, FAQPage, and BreadcrumbList match visible content. - Localization: English page content is served at /glm-5-2; do not infer capabilities from unverified upstream or local-deployment material. ## Page: GLM 5.5 Coming Soon - URL: https://glm5.app/glm-5-5 - Title: GLM 5.5 Free: Track the Z.ai Model Release Now | glm5.app - Summary: A coming-soon release tracker for the anticipated GLM 5.5 model. As of August 2026 Z.ai has not announced GLM 5.5, its release date, size, or pricing; the page separates confirmed facts (GLM 5.2 is the latest flagship, launched June 16, 2026) from community expectations and routes users to free GLM 5.2 access today. - Product facts: no GLM 5.5 model ID, specs, pricing, or weights confirmed; GLM 5.2 open-source under MIT on Hugging Face; GLM 5.2 API about $1.40 input / $4.40 output per 1M tokens; free GLM 5.2 credits on glm5.app. - Visible workflow: hero with a "coming soon" badge; What Is the GLM 5.5 Model?; release-status spec table (confirmed vs expected); expectation cards; release-date and news timeline; role-based use cases; a three-step how-to-follow guide; FAQ; and Chat/GLM 5.2 CTA. No fake generator, no fabricated specs, no media. - Schema: WebPage, HowTo, FAQPage, and BreadcrumbList match visible content; no SoftwareApplication or ImageObject (capability not live, no media). - Localization: English page content is served at /glm-5-5; Simplified Chinese at /zh-CN/glm-5-5 and /zh-TW/glm-5-5 (zh-CN and zh-TW share the same Chinese copy); status facts are checked against Z.ai public communications as of 2026-08-13. ## Features & Capabilities - Advanced Reasoning — GLM 5 excels at multi-step logical reasoning, mathematical problem-solving, and complex analytical tasks with chain-of-thought capability. Icon: RiFileImageFill. - Agentic AI Workflows — GLM 5 powers autonomous agent pipelines — tool use, function calling, multi-turn planning, and self-correction for complex real-world tasks. Icon: RiQuillPenFill. - Code Generation — State-of-the-art coding performance on HumanEval and BigCodeBench. GLM 5 generates, debugs, and refactors code across 50+ programming languages. Icon: RiTerminalBoxFill. - Creative Writing — GLM 5 produces high-quality long-form content, marketing copy, technical documentation, and creative fiction with nuanced style control. Icon: RiGalleryFill. - 128K Context Window — Process entire codebases, research papers, and legal documents in a single prompt. GLM 5's 128K context enables true long-document understanding. Icon: RiComputerFill. - Image Generation — GLM 5 ecosystem includes Seedream 5.0 for photorealistic 2K image generation — text-to-image, image editing, and multi-subject composition. Icon: RiShareCircleFill. ## Testimonials - Jordan Vance (ML Engineer): "GLM 5's coding capability rivals the best. My code review and generation pipeline runs 3x faster with the 128K context window." - Chen Wei (Full-Stack Developer): "Switched from GPT to GLM 5 for our AI agent. The function calling and tool use are more reliable, and the Chinese language support is unmatched." - David Park (Startup CTO): "The agent capabilities and code generation quality match top proprietary models at half the cost." - Sarah Mitchell (Data Scientist): "GLM 5 transformed our data analysis workflow. Complex statistical queries that took hours of manual coding now get solved in minutes." - Alex Romero (DevOps Lead): "We use GLM 5 agents to automate our CI/CD pipeline debugging — saving our team 10+ hours per week." - Priya Sharma (AI Product Manager): "Best open-source model we've tested. GLM 5 handles multi-turn conversations with context retention that actually works." - Tom Henderson (Technical Writer): "GLM 5 drafts technical documentation that actually reads well. The 128K context means I can feed it an entire codebase." - Yuki Tanaka (Backend Developer): "The API is OpenAI-compatible, so migration was painless. GLM 5 gives us better reasoning at a fraction of the cost." - Emma Collins (UX Researcher): "We run user interview analysis through GLM 5 — it synthesizes hundreds of transcripts into actionable insights." - Marcus Lee (Security Engineer): "GLM 5 is our go-to for code audits. It catches subtle vulnerabilities across our entire repository." - Nina Petrova (Startup Founder): "As a solo founder, GLM 5 is like having a full engineering team." - Ryan O'Brien (Cloud Architect): "GLM 5's understanding of infrastructure-as-code is remarkable. Terraform and Kubernetes configs that are production-ready." - Mei Zhang (NLP Researcher): "The multilingual capability is genuinely impressive. GLM 5 handles Chinese-English cross-lingual tasks better than any model I've benchmarked." - Jake Morrison (Game Developer): "We use GLM 5 to generate game dialogue, quest logic, and NPC behavior scripts." - Sophie Laurent (Marketing Director): "GLM 5 powers our entire content pipeline — blog posts, ad copy, email campaigns." - Linda Wu (Research Scientist): "The MoE architecture delivers incredible reasoning depth at lower compute cost." ## FAQ - What is GLM 5? GLM 5 is a fifth-generation frontier large language model. It features approximately 745 billion total parameters with a Mixture-of-Experts (MoE) architecture that activates ~44B parameters per inference, delivering state-of-the-art performance in reasoning, coding, creative writing, and agentic AI tasks. - What context length does GLM 5 support? GLM 5 supports a 200K token context window, enabling long-document understanding, multi-turn dialogue, and complex agent workflows. - Is GLM 5 free to use? Yes. GLM 5 offers free credits for new users on glm5.app. Paid plans include one-time credit packs and monthly or yearly subscriptions with up to 50% savings. - How can I access GLM 5 via API? GLM 5 is available through the glm5.app API with OpenAI SDK-compatible format. Also accessible via OpenRouter. - Can GLM 5 be used as an AI agent? Yes. GLM 5 supports tool use, function calling, multi-turn planning, and self-correction for autonomous multi-step tasks. - Does GLM 5 support image generation? Yes. The GLM 5 ecosystem includes Seedream 5.0 for photorealistic 2K image generation from text prompts. - Can I use GLM 5 for commercial projects? Yes. GLM 5 generated content can be used for commercial purposes. ## Gallery - AI-generated images powered by the GLM 5 ecosystem's Seedream 5.0 model — from editorial portraits to creative artwork. ## Blog (/blog) Technical articles, comparisons, and how-to guides on GLM 5.2 and frontier AI models. English only — the blog defaults to English across all locales. ### GLM 5.2 vs Claude Opus 4.8: Coding, Compared - URL: /blog/glm-5-2-vs-claude-opus-4-8 - Summary: A hands-on head-to-head between GLM 5.2 (open-weight) and Claude Opus 4.8 (Anthropic) on real coding pull requests — covering benchmarks (SWE-bench Pro, FrontierSWE, MCP-Atlas), a ~1M-token context window, open weights vs closed, and agentic coding reliability. - Verdict: Claude Opus 4.8 still edges the hardest reasoning; GLM 5.2 matches it on the everyday ~90% of coding work and adds open weights, self-hosting (Ollama / Hugging Face / vLLM), and a huge context window. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.2 vs opus 4.8, glm 5.2 benchmark, glm 5.2 coding, glm 5.2 api, is glm 5.2 free, glm 5.2 open source, glm 5.2 context window. ### What Is GLM 5.2? Features, Specs & Open Weights - URL: /blog/what-is-glm-5-2 - Summary: A plain-English explainer of GLM 5.2—Z.AI's (formerly Zhipu AI) latest open-weight text LLM. Covers what it is, an at-a-glance specs table (~1M-token context, ~128K output, multiple thinking modes, function calling + MCP, structured JSON, text-only), what "open weights" means, what it's built for (coding, agents, long-horizon tasks; FrontierSWE / SWE-bench Pro / Terminal-Bench 2.1), how it fits the GLM family, and when to use it or not. - Note: GLM 5.2 is text-in/text-out (not multimodal); Z.AI hasn't published an exact parameter count. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat—no install, no API key. - Topics: what is glm 5.2, glm 5.2 open weights, glm 5.2 context window, is glm 5.2 free, glm 5.2 coding, who made glm 5.2, glm 5.2 specs. ### GLM 5.2 Benchmarks: How It Really Performs - URL: /blog/glm-5-2-benchmarks - Summary: A decoded read of GLM 5.2's benchmark scores across coding (SWE-bench Pro 62.1, Terminal-Bench 2.1 81.0), long-horizon (FrontierSWE 74.4, PostTrainBench 34.3, SWE-Marathon 13.0), tool use (MCP-Atlas 76.8, Tool-Decathlon 48.2), and reasoning (AIME 2026 99.2, GPQA-Diamond 91.2) — compared against Claude Opus 4.8 and GPT-5.5, with what each test means in practice. - Verdict: GLM 5.2 is the leading open-weight model — it beats GPT-5.5 on most coding benchmarks and lands within a point or two of Opus 4.8 on long-horizon work, only clearly trailing on the hardest multi-hour tasks (SWE-Marathon, Tool-Decathlon), all at roughly a fraction of the API cost. - Note: Benchmark figures come from Z.AI's official release and third-party leaderboards (as of June 2026) and move fast — verify current numbers on each vendor's page. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.2 benchmark, glm 5.2 benchmarks, glm 5.2 swe-bench, glm 5.2 vs gpt-5.5, glm 5.2 vs opus 4.8, glm 5.2 frontierswe, glm 5.2 performance. ### GLM 5.2 Pricing: API Cost, Plans & Free Tiers - URL: /blog/glm-5-2-pricing - Summary: A full breakdown of GLM 5.2 pricing — Z.AI official API rates (~$1.40/1M input, ~$4.40/1M output, ~$0.26 cached), cheaper third-party access via OpenRouter (~$0.95/$3.00), GLM Coding Plan subscription tiers (Lite to Team), self-hosting the MIT-licensed open weights, and free browser access. - Verdict: On output tokens GLM 5.2 runs roughly 5-6x cheaper than Claude Opus 4.8 or GPT-5.5 while staying competitive on coding benchmarks; OpenRouter is the cheapest metered route, a Coding Plan suits all-day IDE/CLI use, and self-host or glm5.app is free. - Note: Launch-period rates (mid-June 2026); promos reset and GLM 5.2 consumes plan quota faster than older models — verify current numbers on each vendor's page. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.2 pricing, glm 5.2 api cost, glm 5.2 api price, glm coding plan, is glm 5.2 free, glm 5.2 openrouter, glm 5.2 vs claude pricing. ### GLM 5.2 Alternatives: 6 Open-Weight Coding Models - URL: /blog/glm-5-2-alternatives - Summary: Six open-weight alternatives to GLM 5.2 compared — DeepSeek V4-Pro (hard reasoning), Kimi K2.7 Code (agentic), MiniMax M3 (multimodal + cheap), Qwen 3.7/Qwen3-Coder (Apache self-hosting), DeepSeek V4-Flash (lowest cost), and GLM-5.1 (free family option). Each wins on a single axis. - Verdict: GLM 5.2 remains the leading open-weight model overall (Artificial Analysis Intelligence Index) with a ~1M context and MIT weights; pick an alternative only for a specific need (cheapest, multimodal, or sharpest reasoner). - Note: Rankings and prices come from independent leaderboards and vendor pages (mid-2026) and shift often — verify on each source. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.2 alternatives, best open-weight coding models, glm 5.2 vs deepseek v4, glm 5.2 vs qwen, open source coding llm, glm 5.2 competitors. ### Run GLM 5.2 Locally: Ollama, VRAM & Hardware Guide - URL: /blog/how-to-run-glm-5-2-locally - Summary: A practical local-inference guide explaining why Ollama's `glm-5.2:cloud` tag is not on-device inference, what Unsloth GGUF quantization tiers require, and how to run GLM 5.2 with llama.cpp on 256 GB Mac Studio or high-RAM Linux workstations. - Verdict: True local GLM 5.2 is possible, but the memory bar is high: the accessible path is the 2-bit GGUF on 256 GB unified memory or a 24 GB GPU plus 256 GB system RAM; most developers should evaluate first in the browser. - Note: Hardware requirements and quantization sizes come from Unsloth and Z.AI sources as of mid-2026 and should be rechecked before purchasing hardware. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key, no 239 GB download. - Topics: run glm 5.2 locally, glm 5.2 ollama, glm 5.2 gguf, glm 5.2 vram, llama.cpp glm 5.2, glm 5.2 local hardware, glm 5.2 mac studio. ### GLM 5.2 vs Fable 5: Open Source vs Closed - URL: /blog/glm-5-2-vs-fable-5 - Summary: Head-to-head comparison of GLM 5.2 (744B open-weight, MIT, $1.40/$4.40 per 1M tokens) and Claude Fable 5 (closed, $10/$50 per 1M tokens) across Design Arena, Frontend Coding Arena, agentic benchmarks, pricing, and self-hosting options. - Verdict: GLM 5.2 is #1 on Design Arena (Elo 1360), beats GPT-5.5 on FrontierSWE (74.4 vs 72.6), and costs 7× less on input; Fable 5 still leads Frontend Coding Arena and is the only option for multimodal tasks. - Note: Benchmark scores from Design Arena and latent.space AINews as of late June 2026; leaderboards update frequently—verify before deciding. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs fable 5, glm 5.2 open source, is glm 5.2 open source, glm 5.2 pricing, glm 5.2 vs fable, is glm 5.2 better than fable. ### How to Use GLM 5.2 for Free: 4 Methods That Work in 2026 - URL: /blog/how-to-use-glm-5-2-for-free - Summary: Four working routes to GLM 5.2 at zero cost: browser chat at glm5.app (no setup, no API key), NVIDIA NIM free API credits, Cloudflare Workers AI (10k neurons/day, officially listed), and MIT self-hosting (needs 240GB+ RAM/VRAM); honest per-method limits and a decision table for choosing the right one. - Verdict: The best zero-friction starting point is glm5.app/chat — no install, no key. For API access, NVIDIA NIM and Cloudflare Workers AI are both real free tiers, not trials. Self-hosting is free but only feasible on high-end workstations. - Note: Free credit amounts and daily limits from NVIDIA NIM and Cloudflare Workers AI are current as of mid-2026 and may change — verify on each provider before building production workflows around them. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: how to use glm 5.2 for free, glm 5.2 free api, glm 5.2 free trial, glm 5.2 cloudflare, glm 5.2 nvidia nim, glm 5.2 free access, is glm 5.2 free. ### GLM 5.2 vs Kimi K2.5: Benchmarks, Pricing, and When to Choose Each - URL: /blog/glm-5-2-vs-kimi-k2-5 - Summary: Side-by-side comparison of GLM 5.2 and Kimi K2.5 across the Artificial Analysis Intelligence Index (51 vs 35), speed (158 vs 51 t/s), context window (1M vs 256K tokens), pricing ($1.40/$4.40 vs $0.60/$3.00 per 1M tokens), and multimodal support (GLM 5.2 text-only; K2.5 supports text, image, video). Decision framework for which model fits each use case. - Verdict: GLM 5.2 leads on intelligence, speed, and context window by substantial margins; Kimi K2.5 is 57% cheaper on input and supports image/video input that GLM 5.2 does not. Choose GLM 5.2 for complex text tasks at scale; choose K2.5 if visual input or lower cost is the priority. - Note: Benchmark data from Artificial Analysis as of July 2026; pricing from median across providers for GLM 5.2 and official Kimi API for K2.5 — verify before production use. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs kimi k2.5, kimi k2.5 vs glm 5.2, glm 5.2 kimi comparison, kimi k2.5 benchmarks, glm 5.2 benchmarks, kimi k2.5 pricing, glm 5.2 pricing comparison. ### What Is Kimi K3? Moonshot AI's 2.8T Open-Weight Model Explained - URL: /blog/what-is-kimi-k3 - Summary: Comprehensive guide to Kimi K3 — Moonshot AI's 2.8-trillion-parameter MoE reasoning model released July 16, 2026. Covers architecture (896 experts, 16 active per token), benchmarks (AA Intelligence Index 57, DeepSWE 67.5%, FrontierSWE 81.2%), pricing ($3.00/$15.00 per 1M tokens), API quickstart (model ID kimi-k3, OpenAI-compatible), image input, 1M context window, and open weights timeline (July 27, 2026). - Verdict: Highest-scoring open-weight model as of July 2026, matching Claude Opus 4.8 on benchmarks. Best for image-aware pipelines and frontier-tier coding; expensive at $15/M output vs GLM 5.2's $4.40/M. - Note: Model released July 16, 2026; open weights planned July 27, 2026 — verify current status before production use. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: what is kimi k3, kimi k3 explained, kimi k3 moonshot ai, kimi k3 parameters, kimi k3 architecture, kimi k3 api, kimi k3 open source, kimi k3 release date. ### GLM 5.2 vs Kimi K3: Benchmarks, Pricing, and Which One to Use - URL: /blog/glm-5-2-vs-kimi-k3 - Summary: Side-by-side comparison of GLM 5.2 and Kimi K3 across the Artificial Analysis Intelligence Index (51 vs 57), speed (158 vs 62 t/s), output pricing ($4.40 vs $15.00 per 1M tokens), context window (1M tokens each), and multimodal support (GLM 5.2 text-only; K3 supports text and image). Decision framework for which model fits each use case. - Verdict: Kimi K3 scores higher on most benchmarks and supports image input; GLM 5.2 is 71% cheaper on output tokens, 2.5x faster, and already open-source under MIT. Choose K3 for image workflows or highest benchmark scores; choose GLM 5.2 for cost-sensitive, speed-sensitive, or text-only production use. - Note: Benchmark data from Artificial Analysis and Moonshot AI as of July 2026; Kimi K3 launched July 16, 2026 — verify before production use. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs kimi k3, kimi k3 vs glm 5.2, kimi k3 benchmarks, kimi k3 pricing, kimi k3 context window, kimi k3 api, glm 5.2 kimi k3 comparison. ### Is GLM 5.2 Multimodal? Vision Capabilities Explained - URL: /blog/is-glm-5-2-multimodal - Summary: GLM 5.2 is text-only — no image input, no screenshot analysis, no visual reasoning. Z.ai's actual vision model is GLM-5V-Turbo (closed API, Design2Code score 94.8, 202K context). Explains three sources of confusion (Design Arena #1 ≠ vision; Seedream image generation is separate; GLM family includes a vision model but it isn't GLM 5.2), provides a side-by-side comparison table, and covers the two-step workaround pipeline for adding vision to a GLM 5.2 workflow. - Verdict: For vision tasks, use GLM-5V-Turbo. For text at scale, open weights, or self-hosting, GLM 5.2 is the right tool. They serve different use cases and are architecturally separate products. - Note: GLM-5V-Turbo specs from the April 2026 arXiv paper and Z.ai developer documentation; as of July 2026, no public commitment from Z.ai to add vision to the GLM 5.2 model line. - Try it: Chat with GLM 5.2 (text) free at glm5.app/chat — no API key required. - Topics: is glm 5.2 multimodal, does glm 5.2 have vision, glm 5.2 vision, glm 5.2 image input, glm 5.2 multimodal, glm-5v-turbo vs glm 5.2, can glm 5.2 see images. ### GLM 5.2 vs GPT-4o: Benchmarks, Pricing, and the Real Cost Difference - URL: /blog/glm-5-2-vs-gpt-4o - Summary: Side-by-side comparison of GLM 5.2 and GPT-4o across output pricing ($4.40 vs $10.00 per 1M tokens), speed (GLM 5.2 runs 3x faster), context window (1M vs 128K tokens), and benchmark scores. GPT-4o adds audio and multimodal input; GLM 5.2 is MIT open-weight and text-only. - Verdict: GLM 5.2 costs 56% less per output token and runs 3x faster with 8x larger context; GPT-4o is the only option if you need audio or image input. For pure text workloads, GLM 5.2 is the cost-efficient choice. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs gpt-4o, gpt-4o vs glm 5.2, glm 5.2 pricing vs gpt-4o, glm 5.2 benchmarks vs gpt-4o, is glm 5.2 better than gpt-4o. ### GLM 5.2 API: Endpoints, Authentication, and Python Integration Guide - URL: /blog/glm-5-2-api - Summary: Complete developer guide to the GLM 5.2 API — base URL (https://open.bigmodel.cn/api/paas/v4/), authentication via Bearer token, OpenAI SDK compatibility (change base_url and model name), streaming, function calling, and async Python examples. - Verdict: GLM 5.2 is a drop-in replacement for OpenAI API calls in most Python codebases. The main change is base_url and model="glm-5.2"; everything else — streaming, tools, system prompts — works identically. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 api, glm 5.2 api endpoint, glm 5.2 python, glm 5.2 openai sdk, glm 5.2 api authentication, glm 5.2 streaming api. ### GLM 5.2 Context Window: What 1 Million Tokens Actually Means - URL: /blog/glm-5-2-context-window - Summary: GLM 5.2 supports 1,048,576 tokens — 8x GPT-4o's 128K and 4x Kimi K3's 256K. Covers what that capacity enables (entire codebases, long document chains, extended agent sessions), practical limits (cost, latency at >500K), and when the large context actually matters versus when 128K is sufficient. - Verdict: The 1M context window is a real differentiator for large-codebase agents and multi-document pipelines. For most single-document or short-chat tasks, 128K is sufficient — but having 1M available means you never hit the wall. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 context window, glm 5.2 1 million tokens, glm 5.2 context length, how long is glm 5.2 context, glm 5.2 vs gpt-4o context. ### GLM 5.2 Architecture: 753B Parameters, MoE Design, and How It Works - URL: /blog/glm-5-2-architecture - Summary: Technical breakdown of GLM 5.2's architecture — 753B total parameters with Mixture-of-Experts design activating only ~40B per token, transformer decoder layers, expert routing, and what MoE means for inference cost, latency, and scaling compared to dense models. - Verdict: MoE architecture is why GLM 5.2 can pack 753B parameters while staying cost-competitive — only 40B activate per token, so compute scales with active parameters, not total. The tradeoff is memory: serving requires loading all experts even though most are idle. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 architecture, glm 5.2 parameters, glm 5.2 moe, glm 5.2 mixture of experts, how does glm 5.2 work, glm 5.2 model size. ### GLM 5.2 License: MIT Open Weights and What It Means for Commercial Use - URL: /blog/glm-5-2-license - Summary: GLM 5.2 weights are MIT-licensed — no commercial use restrictions, no attribution requirement, fine-tuning allowed, redistribution allowed. Compares MIT to Llama 3's custom license (usage limits above 700M monthly active users) and Apache 2.0 (patent protection included). - Verdict: MIT is the most permissive major open-source license. For commercial products, GLM 5.2's MIT license is cleaner than Llama's custom terms and broadly equivalent to Apache 2.0 without the patent grant. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 license, glm 5.2 commercial use, glm 5.2 open source license, glm 5.2 mit license, can i use glm 5.2 commercially, glm 5.2 open weights. ### Kimi K3 Pricing: API Costs, Context Window, and Value Compared - URL: /blog/kimi-k3-pricing - Summary: Kimi K3 API pricing breakdown — $3.00/M input, $15.00/M output tokens at official Moonshot AI rates. Compares to GLM 5.2 ($1.40/$4.40), GPT-4o ($2.50/$10.00), and Claude Opus 4.8 ($15/$75). Covers the 1M context window, AA Intelligence Index score of 57, and value-per-benchmark-point analysis. - Verdict: Kimi K3 costs 3.4x more per output token than GLM 5.2 for a 6-point intelligence advantage on AA Index. Justified if you need image input or the absolute highest open-weight benchmark score; otherwise GLM 5.2 delivers better cost efficiency. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: kimi k3 pricing, kimi k3 api cost, kimi k3 price per token, moonshot ai kimi k3 price, kimi k3 vs glm 5.2 price, kimi k3 cost. ### How to Use GLM 5.2 API with Python: Complete Integration Guide - URL: /blog/how-to-use-glm-5-2-api-python - Summary: Step-by-step Python integration guide for GLM 5.2 — installing the OpenAI SDK, setting base_url and model name, basic chat completions, streaming responses, function calling with tool definitions, and async usage with asyncio. Includes working code examples for each pattern. - Verdict: Migrating existing OpenAI Python code to GLM 5.2 is a two-line change in most cases. The SDK handles the rest identically. The main gotcha is the model name ("glm-5.2") and the Z.AI base URL. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 python, glm 5.2 api python, how to use glm 5.2 api, glm 5.2 openai python, glm 5.2 sdk, glm 5.2 integration python. ### GLM 5.2 for Coding: Benchmarks, Best Prompts, and IDE Integration - URL: /blog/glm-5-2-for-coding - Summary: Practical coding guide for GLM 5.2 — SWE-bench Pro score (62.1%), Terminal-Bench score (78%), how it compares to GPT-4o and Claude on coding tasks, prompt patterns that improve output quality, and how to integrate GLM 5.2 as a backend for VS Code (Continue extension) and Cursor. - Verdict: GLM 5.2 is competitive with top coding models at a fraction of the API cost. The 1M context window lets you feed entire repos. VS Code and Cursor integrations work via the OpenAI-compatible endpoint with no plugin changes needed. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 coding, glm 5.2 for code generation, glm 5.2 vs gpt-4o coding, glm 5.2 cursor, glm 5.2 vs code, glm 5.2 swe bench, glm 5.2 best coding model. ### How to Fine-Tune GLM 5.2: Hardware Requirements and What Is Actually Practical - URL: /blog/how-to-fine-tune-glm-5-2 - Summary: Realistic guide to fine-tuning GLM 5.2 under its MIT license — full 753B fine-tuning hardware requirements (16+ H100 80GB GPUs, ~1.2TB VRAM), LoRA on quantized variants (2-4 A100s feasible), PEFT library setup, dataset formatting, and when fine-tuning is worth it versus prompt engineering. - Verdict: Full fine-tuning of GLM 5.2 is not feasible for most teams. LoRA on a quantized checkpoint is the practical path and works on 2-4 A100s. For most use cases, prompt engineering and system prompts achieve comparable results without the infrastructure cost. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 fine tuning, how to fine tune glm 5.2, glm 5.2 lora, glm 5.2 training, fine tune glm 5.2 python, glm 5.2 peft. ### GLM 5.2 Function Calling: Tool Use, Parallel Calls, and Agentic Workflows - URL: /blog/glm-5-2-function-calling - Summary: Complete guide to GLM 5.2 function calling — OpenAI-compatible tool definitions, single and parallel tool calls, handling tool_calls responses, building multi-step agent loops, and real agentic workflow examples (web search agent, code execution agent). - Verdict: GLM 5.2 function calling is a drop-in replacement for OpenAI tool use. Parallel tool calls work correctly. The main practical consideration is latency on multi-step agent loops — GLM 5.2's 158 t/s throughput keeps loops fast. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 function calling, glm 5.2 tool use, glm 5.2 tools, glm 5.2 agent, glm 5.2 parallel tool calls, glm 5.2 agentic. ### GLM 5.2 System Prompt Guide: Templates for Coding, Analysis, and Writing - URL: /blog/glm-5-2-system-prompt - Summary: Practical guide to writing effective system prompts for GLM 5.2 — how the system prompt affects output quality, proven templates for code generation (role + output format + constraints), document analysis (extraction schema + format), structured data extraction (JSON schema), and token-efficiency tips. - Verdict: System prompt quality has a larger effect on GLM 5.2 output than most users expect. A structured role + format + constraint template consistently outperforms a vague instruction. The templates in this guide work across coding, analysis, and writing tasks. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 system prompt, glm 5.2 prompt engineering, glm 5.2 prompt template, glm 5.2 best prompts, how to prompt glm 5.2, glm 5.2 instructions. ### GLM 5.2 vs DeepSeek V3: Benchmarks, Pricing, and Use Cases - URL: /blog/glm-5-2-vs-deepseek-v3 - Summary: Side-by-side comparison of GLM 5.2 and DeepSeek V3 across AA Intelligence Index (51 vs 47), speed, output pricing ($4.40 vs ~$1.10 per 1M tokens), context window (1M vs 128K tokens), and open-weight status. Decision framework for text generation, coding, and cost-sensitive workloads. - Verdict: DeepSeek V3 costs 75% less per output token but scores 4 points lower on AA Index and has 8x less context. Choose DeepSeek V3 for high-volume, cost-sensitive text tasks; choose GLM 5.2 for large-context pipelines or benchmark-sensitive applications. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs deepseek v3, deepseek v3 vs glm 5.2, glm 5.2 deepseek comparison, deepseek v3 benchmarks, glm 5.2 vs deepseek pricing. ### GLM 5.2 vs Gemini 2.5 Pro: Performance, Price, and When to Choose Each - URL: /blog/glm-5-2-vs-gemini-2-5-pro - Summary: Comparison of GLM 5.2 and Gemini 2.5 Pro across AA Intelligence Index (~51 vs ~75), output pricing ($4.40 vs ~$10.00 per 1M tokens), speed (GLM 5.2 3x faster), context window (1M vs 1M tokens), open-weight status (GLM 5.2 MIT; Gemini closed), and multimodal support (Gemini supports image/audio/video). - Verdict: Gemini 2.5 Pro leads on benchmark scores by a significant margin; GLM 5.2 is open-weight, 3x faster, and 2.3x cheaper on output tokens. Choose Gemini 2.5 Pro for tasks where benchmark headroom matters; choose GLM 5.2 for cost-efficient, self-hostable, text-only production workloads. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs gemini 2.5 pro, gemini 2.5 pro vs glm 5.2, glm 5.2 gemini comparison, gemini 2.5 pro pricing, glm 5.2 vs google gemini. ### GLM 5.2 vs Llama 3.3 70B: Open-Weight Models Compared on Speed, Benchmarks, and Cost - URL: /blog/glm-5-2-vs-llama-3-3 - Summary: Comparison of GLM 5.2 and Llama 3.3 70B across AA Intelligence Index (51 vs 42), context window (1M vs 128K tokens), output pricing (GLM 5.2 $4.40 direct vs Llama 3.3 ~$0.88 via Groq), speed, and open-weight licensing (both permissive, but GLM 5.2 MIT vs Llama 3 custom). - Verdict: Llama 3.3 70B costs 80% less via Groq but scores 9 points lower on AA Index and has 8x less context. Choose Llama 3.3 for high-volume, cost-sensitive workloads where the benchmark gap is acceptable; choose GLM 5.2 for complex tasks needing large context or higher benchmark performance. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs llama 3.3, llama 3.3 70b vs glm 5.2, open weight model comparison, glm 5.2 llama comparison, llama 3.3 benchmarks, glm 5.2 vs llama. ### Kimi K3 vs GPT-4o: Benchmarks, Speed, and When to Choose Each - URL: /blog/kimi-k3-vs-gpt-4o - Summary: Comparison of Kimi K3 and GPT-4o across AA Intelligence Index (57 vs ~57), output pricing ($15.00 vs $10.00 per 1M tokens), context window (1M vs 128K tokens), speed, multimodal support (both support image; GPT-4o adds audio), and open-weight status (Kimi K3 open July 27; GPT-4o closed). - Verdict: Kimi K3 and GPT-4o are close on benchmark scores. GPT-4o is 33% cheaper on output and adds audio input; Kimi K3 has 8x larger context and will be open-weight. For image workflows without audio, Kimi K3 matches GPT-4o capability; GPT-4o wins if audio or cost is the priority. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: kimi k3 vs gpt-4o, gpt-4o vs kimi k3, kimi k3 benchmarks vs gpt-4o, kimi k3 pricing vs gpt-4o, moonshot ai vs openai. ### GLM 5.2 vs Qwen 3: Chinese Open-Weight AI Models Compared - URL: /blog/glm-5-2-vs-qwen-3 - Summary: Comparison of GLM 5.2 and Qwen 3 235B-A22B across AA Intelligence Index (51 vs ~48), output pricing ($4.40 vs ~$1.20 per 1M tokens), context window (1M vs 128K tokens), and open-weight licensing (both permissive). Both are Chinese-developed open-weight models with strong multilingual performance. - Verdict: Qwen 3 235B-A22B costs 73% less per output token than GLM 5.2 but has 8x less context and slightly lower benchmark scores. GLM 5.2 wins on large-context tasks; Qwen 3 wins on cost efficiency for shorter-context workloads. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs qwen 3, qwen 3 vs glm 5.2, chinese open weight models, glm 5.2 qwen comparison, qwen 3 235b vs glm 5.2, glm 5.2 vs alibaba qwen. ### GLM 5.2 vs Mistral Large 2: Benchmarks, Cost, and Enterprise Fit - URL: /blog/glm-5-2-vs-mistral-large - Summary: Comparison of GLM 5.2 and Mistral Large 2 across AA Intelligence Index, output pricing ($4.40 vs $6.00 per 1M tokens), context window (1M vs 128K tokens), open-weight status (both permissive), and enterprise deployment options. Mistral Large 2 is 45.7B dense; GLM 5.2 is 753B MoE. - Verdict: GLM 5.2 is cheaper, has 8x larger context, and scores higher on most benchmarks than Mistral Large 2. Mistral Large 2 has an edge for European data-residency requirements and a more established enterprise support ecosystem. For most technical workloads, GLM 5.2 is the stronger choice. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs mistral large, mistral large 2 vs glm 5.2, glm 5.2 enterprise, mistral large benchmarks, glm 5.2 vs mistral comparison. ### GLM 5.2 vs Grok 3: Speed, Pricing, and Benchmark Comparison - URL: /blog/glm-5-2-vs-grok-3 - Summary: Comparison of GLM 5.2 and Grok 3 across AA Intelligence Index (~51 vs ~68), output pricing ($4.40 vs ~$15.00 per 1M tokens), speed (GLM 5.2 ~3x faster), context window (1M vs 131K tokens), and open-weight status (GLM 5.2 MIT; Grok 3 closed). - Verdict: Grok 3 scores ~17 points higher on AA Index but costs 3.4x more per output token and responds 3x slower. For high-performance tasks where benchmark headroom justifies cost, Grok 3 is the choice; for cost-efficient production at scale, GLM 5.2 is the better fit. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs grok 3, grok 3 vs glm 5.2, grok 3 benchmarks, grok 3 pricing, glm 5.2 vs xai grok, grok 3 comparison. ### GLM 5.2 for Data Analysis: Structured Output, SQL Generation, and Python Workflows - URL: /blog/glm-5-2-for-data-analysis - Summary: Practical guide to using GLM 5.2 for data analysis tasks — JSON mode for structured extraction, SQL generation from natural language descriptions, CSV analysis with the 1M context window, Python data workflow automation, and pandas/polars code generation patterns. - Verdict: GLM 5.2's 1M context window is particularly useful for data analysis — you can load entire CSVs, database schemas, and multi-table contexts in one prompt. JSON mode reliably produces structured output for downstream processing. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 data analysis, glm 5.2 sql generation, glm 5.2 python data, glm 5.2 json mode, glm 5.2 structured output, glm 5.2 pandas, glm 5.2 for analysts. ### GLM 5.2 on OpenRouter: Access, Pricing, and Integration Guide - URL: /blog/glm-5-2-openrouter - Summary: Guide to accessing GLM 5.2 via OpenRouter (model ID: z-ai/glm-5.2) — how OpenRouter pricing compares to Z.AI direct ($0.95/$3.00 vs $1.40/$4.40 per 1M tokens), how to switch from any other OpenRouter model with a single model name change, and when OpenRouter routing is preferable to direct API access. - Verdict: OpenRouter is the cheapest metered route for GLM 5.2 — ~32% cheaper on input and ~32% cheaper on output than Z.AI direct. The unified API also makes it easy to fall back to other models if needed. The tradeoff is an additional network hop and dependency on OpenRouter uptime. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 openrouter, openrouter glm 5.2, z-ai glm-5.2 openrouter, glm 5.2 cheapest api, glm 5.2 openrouter pricing, glm 5.2 via openrouter. ### GLM 5.2 vs GLM-4: What Changed and Whether the Upgrade Is Worth It - URL: /blog/glm-5-2-vs-glm-4 - Summary: Comparison of GLM 5.2 (753B MoE, MIT, 1M context) and GLM-4 (9B-130B dense, various licenses, 128K context) across parameter count, architecture, benchmark scores (AA Intelligence Index 51 vs ~30-38), context window, pricing, and use-case fit. - Verdict: GLM 5.2 is a substantially different model — larger, smarter, and with 8x more context. The upgrade is worth it for any workload that needs large-context processing, agentic tasks, or competitive benchmark performance. GLM-4 remains viable only for lightweight local inference on consumer hardware. - Try it: Chat with GLM 5.2 free in the browser at glm5.app/chat — no install, no API key required. - Topics: glm 5.2 vs glm 4, glm 5.2 upgrade, glm 4 vs glm 5.2, is glm 5.2 better than glm 4, glm 5.2 vs glm4 benchmarks, zhipu ai glm comparison. - GLM 5.2 vs GPT-5.6 Sol: Benchmarks, Pricing, and the 6.8× Cost Gap - URL: /blog/glm-5-2-vs-gpt-5-6-sol - Summary: Comprehensive comparison of GLM 5.2 (AA Index 51, $1.40/$4.40/M, 158 t/s, 1M context, MIT) and GPT-5.6 Sol (AA Index 59, $5/$30/M, ~1.05M context, closed, vision input). Sol leads on GPQA Diamond (94.6% vs 89%), Terminal-Bench (88.8% vs 78%), and overall AA Index (+8 points). GLM 5.2 is 85% cheaper per output token (6.8× less expensive) and faster on standard providers. SWE-bench Pro gap is only 2.5 points (64.6% vs 62.1%). Sol adds vision input and 128K output window; GLM 5.2 is text-only with MIT open weights. - Verdict: GPT-5.6 Sol is a stronger model by every benchmark at 6.8× the output cost. For text-only coding and analysis workloads where SWE-bench-level performance is the bar, GLM 5.2 delivers comparable results at a fraction of the price. Sol's premium is justified for vision workflows, deep scientific reasoning, or maximum agentic performance. - Try it: Chat with GLM 5.2 free at glm5.app/chat — no API key needed. - Topics: glm 5.2 vs gpt 5.6 sol, gpt 5.6 sol comparison, glm 5.2 vs gpt 5.6, gpt 5.6 sol pricing, gpt 5.6 sol benchmarks, openai gpt 5.6 vs glm 5.2. ### 智谱 GLM 5.3 是什么？全新编程旗舰全面解析 - URL: /blog/zhipu-glm-5-3 - Summary: 智谱 Z.ai 于 2026 年 8 月 14 日发布 GLM 5.3：与 GLM 5.2 同基座（约 743B MoE），全部提升来自后训练——编程能力提升 50%、Terminal-Bench 3.0 从 4.6 跃至 28.3、CyberGym 84.5 登顶。 - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: 智谱glm 5.3. ### Z.AI GLM 5.3: How to Access the New Flagship (Coding Plan, ZCode, API) - URL: /blog/z-ai-glm-5-3 - Summary: Z.AI GLM 5.3 access guide — GLM Coding Plan ($18/$80/$168), ZCode agent, and the upcoming API (glm-5.3, $1.40/$4.40 per 1M). What's included and how to start. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: z ai glm 5.3. ### When Is GLM 5.3 Coming Out? Release Date & Availability - URL: /blog/when-is-glm-5-3-coming-out - Summary: When is GLM 5.3 coming out? It launched August 14, 2026 — live on the GLM Coding Plan and ZCode now, API rolling out, open weights expected in late August. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: when is glm 5.3 coming out. ### Kimi K3 vs GLM 5.3：基准对比、独立评测与选购建议 - URL: /blog/kimi-k3-vs-glm-5-3 - Summary: Kimi K3 vs GLM 5.3 对比：官方基准表（GLM 5.3 赢 CyberGym 84.5 vs 80.0、AutomationBench 48.2 vs 46.7；Kimi K3 赢 SWE-Marathon 48.1 vs 42.5）、AA 指数均为 60 分、价格差近一倍。 - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: kimi k3 vs glm 5.3. ### ZCode + GLM 5.3: The Complete Guide to Z.AI's Coding Agent - URL: /blog/glm-5-3-zcode - Summary: ZCode with GLM 5.3 — Goal mode, 98%+ cache hit rate, 1.5x quota boost (through Aug 31), Remote Control from WeChat/Feishu. How to set up and get the most from Z.AI's agent. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: zcode. ### GLM 5.3 vs Kimi K3: Benchmarks, Intelligence Index & Which Is Better - URL: /blog/glm-5-3-vs-kimi-k3 - Summary: GLM 5.3 vs Kimi K3 — Z.AI's official table (GLM wins CyberGym 84.5 vs 80.0, AutomationBench 48.2 vs 46.7; Kimi wins DeepSWE 67.5 vs 66.9, SWE-Marathon 48.1 vs 42.5) plus… - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 vs kimi k3. ### GLM 5.3 vs Claude Fable 5: Coding, Agents & the Open-Weight Gap - URL: /blog/glm-5-3-vs-fable-5 - Summary: GLM 5.3 vs Claude Fable 5 — Z.AI says GLM 5.3 approaches Fable 5 on coding and agents. Full official benchmark comparison (TB 3.0 28.3 vs 33.7, DeepSWE 66.9 vs 69.7) and when GLM… - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 vs fable 5. ### GLM 5.3 什么时候发布？发布时间线与获取方式 - URL: /blog/glm-5-3-release-date-zh - Summary: GLM 5.3 发布时间：2026 年 8 月 14 日已正式发布（Coding Plan/ZCode 即刻可用）；API 逐步开放；开源权重预计 8 月底（发布约两周后）。附完整时间线与怎么用。 - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3什么时候发布. ### GLM 5.3 on Reddit: What the Community Is Saying - URL: /blog/glm-5-3-reddit - Summary: GLM 5.3 Reddit discussion roundup — post-training scaling debate, open-weights vs closed models, cyber capability concerns, and hands-on reports from r/LocalLLaMA and r/artificial. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 reddit. ### GLM 5.3 Post-Training Explained: How Scaling RL Made One Model Jump 6x - URL: /blog/glm-5-3-post-training - Summary: GLM 5.3's post-training-only upgrade — same 743B base as 5.2, but RL scaling on long-horizon environments delivered Terminal-Bench 3.0 4.6→28.3 (6x). The IndexShare/SAO/slime… - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 post-training. ### GLM 5.3 OpenRouter：上架状态、模型 ID 与接入教程 - URL: /blog/glm-5-3-openrouter-zh - Summary: GLM 5.3 上 OpenRouter 了吗？目前还没有——最新是 z-ai/glm-5.2。本文说明上架进度、模型 ID 约定（z-ai/glm-5.3）、定价参考与接入参数。 - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: openrouter glm 5.3. ### How to Use GLM 5.3 in OpenCode: Setup Guide - URL: /blog/glm-5-3-opencode - Summary: Use GLM 5.3 in OpenCode — configure the Z.AI devpack, set model glm-5.3, enable thinking (reasoning_effort max), and start agentic coding with the open-weights SOTA model. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: opencode glm 5.3. ### GLM 5.3 Ollama：本地运行指南与硬件要求（权重发布后可用） - URL: /blog/glm-5-3-ollama - Summary: GLM 5.3 能在 Ollama 上跑吗？目前 Ollama 最新只有 GLM 5.2——GLM 5.3 权重 8 月底才开源。本文说明等待时间、硬件要求（约 743B MoE）与发布后如何在 Ollama 部署。 - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 ollama. ### How to Migrate from GLM 5.2 to GLM 5.3: Thinking Parameters & Breaking Changes - URL: /blog/glm-5-3-migration-guide - Summary: GLM 5.3 migration guide — the required thinking.enabled change, reasoning_effort (low/high/max), what breaks from GLM 5.2, and a step-by-step cutover checklist for API and Coding… - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 migration. ### GLM 5.3 Local Deployment: Hardware Requirements & What to Prepare - URL: /blog/glm-5-3-local-deployment - Summary: GLM 5.3 local deployment guide — ~753B MoE model, VRAM estimates, SGLang/vLLM setup, quantization options, and a preparation checklist before weights drop on HuggingFace. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 local deployment. ### GLM 5.3 DeepSWE v1.1: 66.9 Explained — What the Score Means - URL: /blog/glm-5-3-deepswe - Summary: GLM 5.3 DeepSWE v1.1 score 66.9 (from 46.2) explained — what DeepSWE tests, the full leaderboard vs Kimi K3, DeepSeek-V4, Opus 4.8, Fable 5, GPT-5.6 Sol, and why it matters for… - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: deepswe. ### GLM 5.3 Cybersecurity: CyberGym SOTA, 2,436 Real Vulnerabilities & the Disclosure Ledger - URL: /blog/glm-5-3-cybersecurity - Summary: GLM 5.3's emergent cyber capability — CyberGym 84.5 (best public result), 2,436 real-world vulnerabilities found across 269 projects, and why Z.AI holds the weights for safety… - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 cybersecurity. ### GLM 5.3 Found a Vulnerability in Cursor: What We Know - URL: /blog/glm-5-3-cursor-vulnerability - Summary: GLM 5.3 reportedly found a 'potentially serious vulnerability' in Cursor (SpaceX-acquired) — the first real-world exploit of its cyber capability. What Z.ai said, the… - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 cursor vulnerability. ### GLM 5.3 Artificial Analysis Score: 60 on the Intelligence Index — Explained - URL: /blog/glm-5-3-artificial-analysis - Summary: GLM 5.3 scores 60 on Artificial Analysis Intelligence Index (max reasoning) — matching Kimi K3, 3 behind Claude Opus 5, 8th of 181 models, at the lowest cost per task ($0.68). - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 artificial analysis. ### GLM 5.3 Architecture: Same Base as 5.2, ~743B MoE — Post-Training Explained - URL: /blog/glm-5-3-architecture - Summary: GLM 5.3 architecture — same ~743B-753B MoE base model as GLM 5.2, all gains from post-training (IndexShare, SAO, slime), 1M context, 128K output. What's inside and what it means. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 architecture. ### GLM 5.3 API: Endpoints, Parameters & Python Examples - URL: /blog/glm-5-3-api - Summary: GLM 5.3 API guide — model ID glm-5.3, endpoints (OpenAI/Anthropic-compatible), thinking parameters (reasoning_effort low/high/max), pricing $1.40/$4.40, and Python code examples. - Try it: Chat with GLM 5.3 free in the browser at glm5.app/chat — no install, no API key. - Topics: glm 5.3 api. ### What Is GPT-6 Astra? OpenAI's Computer-Use Flagship, Explained - URL: /blog/what-is-gpt-6-astra - Summary: A specifications-and-evidence explainer of GPT-6 Astra, OpenAI's frontier model released September 3, 2026. Covers the API model ID gpt-6-astra, a 1,050,000-token context window, 128K max output, an April 30 2026 knowledge cutoff, $10/$50 per 1M tokens with $1.00 cached input, the five reasoning-effort levels, and the computer-use benchmarks the launch was built on (Agents' Last Exam 59.3%, ScreenSpot-Pro 92.7%, OSWorld 2.0 72.6%). - Verdict: A genuine step change in computer use and cybersecurity capability, a modest step in raw reasoning, and — on independent measurement — not the highest-scoring model available. - Note: Two pricing rules the headline rate hides — requests above 272,000 input tokens are billed at 2x input and 1.5x output for the entire request, and cache writes cost 1.25x fresh input. OpenAI has not disclosed a parameter count. - Try it: Compare GPT-6 Astra against other frontier models at glm5.app/chat?model=gpt-6-astra — one account, one credit balance. - Topics: gpt 6 astra, gpt 6 astra release date, gpt 6 astra price, gpt 6 astra benchmarks, gpt 6 astra free, gpt 6 astra agi, gpt 6 astra pro, gpt 6 astra context window, gpt 6 astra parameter count. ### What Is Claude Fable 5.1? Anthropic's Cheaper Frontier Model, Explained - URL: /blog/what-is-claude-fable-5-1 - Summary: A plain explainer of Claude Fable 5.1, released September 1, 2026 — model ID claude-fable-5-1, 1M context, 128K max output, a June 2026 knowledge cutoff, adaptive thinking that is always on, and pricing identical to Fable 5 at $10/$50 per 1M tokens. Explains why the widely reported "25% cheaper" is a 75% cut to cache reads ($1.00 to $0.25) rather than a rate cut, lists the three breaking changes from Fable 5, and covers the loosened cybersecurity and biology safeguards. - Verdict: The current top of Artificial Analysis's independent Intelligence Index, but Anthropic's own documentation tells you to start with Claude Opus 5 at half the price and escalate only when Opus at higher effort falls short. - Note: A single-turn call with no prompt caching costs exactly what it cost on Fable 5 — zero saving. Claude Mythos 5.1 is the same model with more permissive safeguards, invitation-only through Project Glasswing. - Try it: Run Claude Fable 5.1 next to GPT-6 Astra at glm5.app/chat?model=claude-fable-5.1 — same account, same credit balance. - Topics: claude fable 5.1, claude fable 5.1 pricing, claude fable 5.1 free, claude fable 5.1 release date, claude mythos 5.1, is fable 5.1 cheaper than 5. ### GPT-6 Astra vs Claude Fable 5.1: Same Price, Very Different Bill - URL: /blog/gpt-6-astra-vs-claude-fable-5-1 - Summary: A head-to-head that reads both vendors' own tables instead of one. Both models list at $10/$50 per 1M tokens, but Astra charges $1.00 per 1M cached input against Fable 5.1's $0.25, and Astra alone reprices the entire request above 272,000 input tokens. Includes the benchmarks each model leads, marked by which vendor published them, and the three rows where OpenAI and Anthropic independently report the same score for Fable 5.1. - Verdict: No single winner. Astra leads terminal work, agentic coding, computer use, math and science; Fable 5.1 leads Humanity's Last Exam with tools and the Artificial Analysis Intelligence Index — a lead published in OpenAI's own appendix. On cost per task, independent measurement favours Astra ($2.57 vs $6.12). - Note: Vendor benchmark numbers are labelled by source throughout; OSWorld 2.0 scores are not compared head-to-head because the two vendors ran different task releases and scoring modes. - Try it: Put both models on the same prompt at glm5.app/chat?model=gpt-6-astra and switch to claude-fable-5.1 in the same picker. - Topics: gpt 6 astra vs fable 5.1, gpt 6 astra vs claude fable 5.1, claude fable 5.1 vs gpt 6, gpt 6 astra vs opus 5, gpt 6 astra benchmarks. ### How to Use GPT-6 Astra: ChatGPT, the API, Codex, and Cost Control - URL: /blog/how-to-use-gpt-6-astra - Summary: A setup guide covering all four routes to GPT-6 Astra — ChatGPT (Plus, Pro, Business, Enterprise, with Astra Pro on the upper tiers), the OpenAI API as gpt-6-astra with reasoning.effort from low to max, Codex with its new cross-context-window note-keeping, and Microsoft Azure plus AWS Bedrock. Includes the rate-limit table by usage tier and the four settings that control cost. - Note: On Enterprise plans access is off by default at launch until an administrator enables it. Fine-tuning is not supported. Astra refuses proof-of-concept exploit creation, and when misalignment monitoring intervenes the API stops the task outright rather than pausing it. - Try it: Skip the API-key setup and run Astra directly at glm5.app/chat?model=gpt-6-astra. - Topics: how to use gpt 6 astra, gpt 6 astra how to use, how to use gpt 6 astra for free, gpt 6 astra api, gpt 6 astra api cost, gpt 6 astra codex, gpt 6 astra pro. ### Claude Fable 5.1 Pricing: What the 25% Cheaper Claim Actually Means - URL: /blog/claude-fable-5-1-pricing - Summary: The complete Claude Fable 5.1 rate card — $10 input, $50 output, $0.25 cache read, $12.50 five-minute cache write, $20 one-hour cache write, 50% Batch discount — and the mechanism behind the headline saving. Three worked examples show the same model landing on 0%, 26% and 45% savings depending purely on how much of the bill was cache reads, plus a comparison against Opus 5, Sonnet 5, Haiku 4.5 and GPT-6 Astra. - Verdict: Worth it for long context-heavy agentic sessions; a pure capability upgrade at unchanged cost for short stateless calls, where the saving is exactly zero. - Note: Worked examples are arithmetic on Anthropic's published rates with stated assumptions, not measurements. Anthropic publishes no long-context surcharge, unlike GPT-6 Astra's 272K rule. - Try it: Test the effort ladder yourself at glm5.app/chat?model=claude-fable-5.1. - Topics: claude fable 5.1 pricing, claude fable 5.1 cost, is fable 5.1 cheaper than 5, claude fable 5.1 free, claude fable 5.1 vs gpt 6. ### What Is GLM 5.3 Flash? Z.ai's 320B-A18B Multimodal Model - URL: /blog/what-is-glm-5-3-flash - Summary: GLM 5.3 Flash is Z.ai's 320B-A18B natively multimodal MoE with a 1M context, MIT weights, and $0.15/$0.50 list pricing — and the model that previewed anonymously as Ox Alpha. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: what is glm 5 3 flash. ### GLM 5.3 Flash Pricing: Real Cost per Token, per Provider - URL: /blog/glm-5-3-flash-pricing - Summary: $0.15/$0.50 list, a temporary 50% launch discount that most coverage quotes as if it were list, and a 2x price spread across ten OpenRouter providers. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: glm 5 3 flash pricing. ### GLM 5.3 Flash Benchmarks: Vendor Claims vs Independent Numbers - URL: /blog/glm-5-3-flash-benchmarks - Summary: Which GLM 5.3 Flash scores come from Z.ai's own launch table and which from independent measurement, and how far apart they sit. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: glm 5 3 flash benchmarks. ### GLM 5.3 Flash vs DeepSeek V4 Flash: Which Cheap Open Model Wins - URL: /blog/glm-5-3-flash-vs-deepseek-v4-flash - Summary: Two cheap open-weight models compared on price, context, multimodality and benchmark scores, with the workloads each one actually suits. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: glm 5 3 flash vs deepseek v4 flash. ### GLM 5.3 Flash Parameters and Size: 320B-A18B, 328 GB, MIT Weights - URL: /blog/glm-5-3-flash-parameters - Summary: Architecture and footprint of GLM 5.3 Flash — 320B total / 18B active parameters, ~328 GB in FP8, MIT-licensed weights, and where to get them. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: glm 5 3 flash parameters. ### GLM 5.3 Flash on OpenRouter: Model ID, Provider Prices & API Setup - URL: /blog/glm-5-3-flash-openrouter - Summary: The z-ai/glm-5.3-flash model ID, the full per-provider price and context table from OpenRouter's endpoints API, and curl + Python setup. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: glm 5 3 flash openrouter. ### GLM 5.3 Flash on DGX Spark: Does It Fit, and How Fast Is It - URL: /blog/glm-5-3-flash-dgx-spark - Summary: Whether 328 GB of FP8 weights fit NVIDIA DGX Spark hardware, what quantization it takes, and the community-reported throughput. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: glm 5 3 flash dgx spark. ### GLM 5.3 Flash on Reddit: What the Community Actually Found - URL: /blog/glm-5-3-flash-reddit - Summary: Community reports on GLM 5.3 Flash separated from vendor claims — what is corroborated, what is unverified, and what is rumour. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: glm 5 3 flash reddit. ### Ox Alpha vs GLM 5.3 Flash: They Are the Same Model - URL: /blog/ox-alpha-vs-glm-5-3-flash - Summary: Ox Alpha was GLM 5.3 Flash under a codename. What changed between the free stealth preview and the priced public launch. - Try it: Chat with GLM 5.3 Flash free in the browser at glm5.app/chat?model=glm-5.3-flash — no install, no API key. - Topics: ox alpha vs glm 5 3 flash. ## GLM 5.2 vs GPT-4.1: Benchmarks, Pricing, and When to Choose URL: /blog/glm-5-2-vs-gpt-4-1 Summary: GPT-4.1 costs $8/M output tokens versus GLM 5.2's $4.40 — 82% more expensive. GPT-4.1 improves on instruction following and long-context coding, but GLM 5.2 delivers MIT open weights at significantly lower cost for comparable software engineering tasks. Topics: 5, 2, gpt, 4, 1 ## GLM 5.2 vs Claude Sonnet 5: Cost, Benchmarks, and Open vs Closed URL: /blog/glm-5-2-vs-claude-sonnet-5 Summary: Claude Sonnet 5 costs $15/M output tokens versus GLM 5.2's $4.40 — 3.4× more expensive. Sonnet 5 leads on reasoning and coding benchmarks, but GLM 5.2 offers MIT open weights and 1M-token context at significantly lower cost. Topics: 5, 2, claude, sonnet, 5 ## GLM 5.2 vs Phi-4: Frontier Scale vs Efficient Small Model URL: /blog/glm-5-2-vs-phi-4 Summary: Phi-4 is a 14B-parameter model that runs on a single GPU. GLM 5.2 is a 753B MoE model with 1M-token context and frontier coding benchmarks. Here is when each is the right tool. Topics: 5, 2, phi, 4 ## GLM 5.2 vs Claude Haiku 4.5: Speed, Cost, and When Size Matters URL: /blog/glm-5-2-vs-haiku-4-5 Summary: Claude Haiku 4.5 costs $4/M output tokens — nearly the same as GLM 5.2's $4.40. But Haiku 4.5 is optimized for speed on simple tasks, while GLM 5.2 excels at complex coding with 1M-token context. Topics: 5, 2, haiku, 4, 5 ## GLM 5.2 vs Gemini 2.5 Flash: Cost, Speed, and Benchmark Comparison URL: /blog/glm-5-2-vs-gemini-2-5-flash Summary: Gemini 2.5 Flash costs $0.30/M output tokens versus GLM 5.2's $4.40 — 15× cheaper. Flash is optimized for high-volume tasks. GLM 5.2 scores higher on complex coding and offers open weights. Here is when each model is the right call. Topics: 5, 2, gemini, 2, 5, flash ## GLM 5.2 vs MiniMax M1: Chinese AI Models Head-to-Head URL: /blog/glm-5-2-vs-minimax-m1 Summary: MiniMax M1 and GLM 5.2 are both Chinese-origin AI models with MoE architectures optimized for long-context tasks. Here is how they compare on benchmarks, pricing, and production use cases. Topics: 5, 2, minimax, m1 ## Kimi K3 vs DeepSeek V3: Cost, Speed, and Coding Benchmarks URL: /blog/kimi-k3-vs-deepseek-v3 Summary: DeepSeek V3 costs $1.10/M output tokens versus Kimi K3's $15/M — 13× cheaper. Kimi K3 scores ~10 points higher on the AA Intelligence Index. Here is when each open-weight-adjacent model is the right call. Topics: kimi, k3, deepseek, v3 ## Kimi K3 vs Claude Opus 4.8: Frontier Models Head-to-Head URL: /blog/kimi-k3-vs-claude-opus-4-8 Summary: Claude Opus 4.8 costs $75/M output tokens versus Kimi K3's $15/M — 5× more expensive. Opus 4.8 leads every benchmark and sets the bar for top-tier AI. Here is when the premium is justified. Topics: kimi, k3, claude, opus, 4, 8 ## Kimi K3 vs Gemini 2.5 Pro: Performance, Price, and Context Window URL: /blog/kimi-k3-vs-gemini-2-5-pro Summary: Gemini 2.5 Pro scores significantly higher on the AA Intelligence Index than Kimi K3 and costs $10/M output tokens versus K3's $15/M — 33% cheaper for a stronger model. Here is how these 1M-context models compare. Topics: kimi, k3, gemini, 2, 5, pro ## GLM 5.2 Thinking Mode: How Extended Reasoning Works URL: /blog/glm-5-2-thinking-mode Summary: GLM 5.2 supports an extended thinking mode that exposes the model's reasoning chain before the final answer. Here is how thinking mode works, when to enable it, and how it affects cost and response quality. Topics: 5, 2, thinking, mode ## GLM 5.2 JSON Mode: Structured Output and Schema Enforcement URL: /blog/glm-5-2-json-mode Summary: GLM 5.2 supports JSON mode via response_format — the model outputs valid, parseable JSON every time. Here is how to use it for structured extraction, data pipelines, and schema-enforced AI responses. Topics: 5, 2, json, mode ## GLM 5.2 GGUF: Quantized Weights and Local Inference Guide URL: /blog/glm-5-2-gguf Summary: GLM 5.2's MIT-licensed weights can be quantized to GGUF format for local inference. Here is the hardware requirements at each quantization level, and how to set up inference with llama.cpp and LM Studio. Topics: 5, 2, gguf ## GLM 5.2 Temperature and Sampling: How to Control Output Quality URL: /blog/glm-5-2-temperature Summary: GLM 5.2's temperature, top-p, and top-k parameters control randomness and creativity. Here are the recommended settings for coding, analysis, creative writing, and structured extraction — with concrete examples. Topics: 5, 2, temperature ## GLM 5.2 API Rate Limits: Tiers, Quotas, and Error Handling URL: /blog/glm-5-2-rate-limits Summary: GLM 5.2 API rate limits vary by Z.ai subscription tier. Here is the tier structure, how to handle 429 errors with exponential backoff, and strategies for scaling high-volume production workloads. Topics: 5, 2, rate, limits ## How to Use GLM 5.2 with LangChain: Complete Integration Guide URL: /blog/glm-5-2-langchain Summary: GLM 5.2 works with LangChain via ChatOpenAI by pointing base_url to Z.ai's API. Here is how to build chains, agents, and tool-calling workflows with GLM 5.2 as the backend model. Topics: 5, 2, langchain ## GLM 5.2 RAG: Building Retrieval-Augmented Generation Applications URL: /blog/glm-5-2-rag Summary: GLM 5.2's 1M-token context window changes the RAG calculus — for many use cases you can skip the retrieval step entirely. Here is how to build both full-context and traditional retrieval-based RAG pipelines with GLM 5.2. Topics: 5, 2, rag ## GLM 5.2 Streaming API: Real-Time Token Output Guide URL: /blog/glm-5-2-streaming Summary: GLM 5.2 supports streaming via the standard OpenAI SSE format. Here is how to implement streaming in Python and JavaScript, handle delta tokens, and build real-time chat interfaces. Topics: 5, 2, streaming ## GLM 5.2 Batch Processing: High-Volume API Workflows URL: /blog/glm-5-2-batch-processing Summary: Processing thousands of requests with GLM 5.2 requires async concurrency, rate limit handling, and cost optimization. Here is how to build efficient batch pipelines that maximize throughput without hitting API limits. Topics: 5, 2, batch, processing ## GLM 5.2 for Writing: Prompts, Quality, and Content Workflows URL: /blog/glm-5-2-for-writing Summary: GLM 5.2 handles long-form blog posts, technical documentation, and marketing copy at production scale. Here are the best prompt templates, writing quality expectations, and workflow patterns for content teams. Topics: 5, 2, writing ## Call to Action - Start Using GLM 5 Today — Try GLM 5 free — reasoning, coding, agents, and image generation in one platform. - Button: Try GLM 5 Free (`/generate`, target self, icon: Zap) ## Contact - Website: https://glm5.app - Email: support@glm5.app ## Deploying GLM 5.2 with Docker: Self-Hosting and vLLM API Setup URL: /blog/glm-5-2-docker Summary: GLM 5.2's MIT license allows full self-hosting on your own GPU infrastructure. Here is how to containerize a GLM 5.2 API server using Docker and vLLM, with memory requirements and production configuration. Topics: docker, vllm, self-hosting, gpu, deployment, production ## Page: GLM 5.2 Embeddings: Generate Text Vectors with the Zhipu API URL: https://glm5.app/blog/glm-5-2-embeddings Date: 2026-07-23 Description: Learn how to use Zhipu AI's embedding models alongside GLM 5.2 for RAG, semantic search, and vector database workflows — with Python code examples. ## Page: GLM 5.2 Tokenizer: Token Counting, Vocabulary Size, and Cost Estimation URL: https://glm5.app/blog/glm-5-2-tokenizer Date: 2026-07-23 Description: Understand how GLM 5.2's tokenizer handles Chinese, English, and code — and how to estimate token counts accurately to control API costs. ## Page: GLM 5.2 with LlamaIndex: Build RAG Pipelines in Python URL: https://glm5.app/blog/glm-5-2-llamaindex Date: 2026-07-23 Description: Step-by-step guide to integrating GLM 5.2 into LlamaIndex for document Q&A, RAG pipelines, and agentic workflows using the OpenAI-compatible adapter. ## Page: GLM 5.2 for Customer Service: Accuracy, Cost, and Multilingual Support URL: https://glm5.app/blog/glm-5-2-for-customer-service Date: 2026-07-23 Description: Evaluate GLM 5.2 as a customer service AI: bilingual Chinese-English accuracy, function calling for CRM integration, streaming responses, and API cost at scale. ## Page: GLM 5.2 Agentic Workflows: Function Calling, Tool Use, and Multi-Step Tasks URL: https://glm5.app/blog/glm-5-2-agentic-workflows Date: 2026-07-23 Description: Build production AI agents with GLM 5.2 using function calling, parallel tool execution, and multi-step reasoning — complete Python examples included. ## Page: GLM 5.2 Cost Optimization: 6 Strategies to Reduce API Spend URL: https://glm5.app/blog/glm-5-2-cost-optimization Date: 2026-07-23 Description: Cut your GLM 5.2 API costs with prompt compression, context management, batch API, model routing, caching, and output control — practical tactics with Python code. ## Page: GLM 5.2 Multilingual: Languages Supported, Benchmarks, and Real-World Use URL: https://glm5.app/blog/glm-5-2-multilingual Date: 2026-07-23 Description: An honest look at GLM 5.2's multilingual capabilities: which languages it excels at, where it falls short, and how it compares to GPT-4o for non-English tasks. ## Page: GLM 5.2 vs LLaMA 4 Scout: Open-Source Giants Compared URL: https://glm5.app/blog/glm-5-2-vs-llama-4-scout Date: 2026-07-23 Description: GLM 5.2 vs LLaMA 4 Scout — benchmark scores, pricing, 10M vs 1M context, multimodal support, deployment options, and which open-weights model to choose. ## Page: Kimi K3 vs Qwen 3: Chinese AI Frontier Models Compared URL: https://glm5.app/blog/kimi-k3-vs-qwen-3 Date: 2026-07-23 Description: Kimi K3 vs Qwen 3 — cost per million tokens, thinking mode, context length, coding benchmarks, and which Chinese AI model fits your workflow. ## Page: Kimi K3 vs Grok 3: Open Weights vs Closed Source URL: https://glm5.app/blog/kimi-k3-vs-grok-3 Date: 2026-07-23 Description: Kimi K3 vs Grok 3 — compare cost, context length, benchmark performance, self-hosting options, and which model to choose for your AI application. ## Page: GLM 5.2 Vision API: Image Understanding, Analysis, and Multimodal Use Cases URL: https://glm5.app/blog/glm-5-2-vision Date: 2026-07-27 Description: A complete guide to GLM 5.2's vision capabilities: how to send images via the API, what tasks it handles well, and real Python code examples for multimodal apps. ## Page: GLM 5.2 Structured Output: JSON Mode, Schema Validation, and Reliable Extraction URL: https://glm5.app/blog/glm-5-2-structured-output Date: 2026-07-27 Description: Master GLM 5.2 structured output: enable JSON mode, define output schemas, validate responses, and build reliable data extraction pipelines with Python. ## Page: GLM 5.2 for Legal: Contract Analysis, Document Review, and Compliance Use Cases URL: https://glm5.app/blog/glm-5-2-for-legal Date: 2026-07-27 Description: Evaluate GLM 5.2 for legal work: contract clause extraction, document summarization, compliance checking, and how its 1M token context handles large legal files. ## Page: GLM 5.2 for Education: Tutoring, Assessment, and Learning App Development URL: https://glm5.app/blog/glm-5-2-for-education Date: 2026-07-27 Description: How to use GLM 5.2 to build education tools: AI tutoring, automated essay feedback, quiz generation, and multilingual learning apps — with cost estimates. ## Page: Kimi K3 API: Authentication, Endpoints, and Python Integration Guide URL: https://glm5.app/blog/kimi-k3-api Date: 2026-07-27 Description: Get started with the Kimi K3 API: set up authentication, call chat completions, use streaming and function calling — complete Python examples included. ## Page: GLM 5.2 vs Gemma 3: Open-Source AI from China and Google Compared URL: https://glm5.app/blog/glm-5-2-vs-gemma-3 Date: 2026-07-27 Description: GLM 5.2 vs Gemma 3 — benchmark scores, pricing, context length, multimodal support, deployment options, and which open-weights model fits your use case. ## Page: GLM 5.2 vs DeepSeek R1: General LLM vs Reasoning Specialist Compared URL: https://glm5.app/blog/glm-5-2-vs-deepseek-r1 Date: 2026-07-27 Description: GLM 5.2 vs DeepSeek R1 — benchmark scores, pricing, context length, thinking mode, and which model to choose for coding, math, reasoning, or general tasks. ## Page: GLM 5.2 vs LLaMA 4 Maverick: Large Open-Source MoE Models Compared URL: https://glm5.app/blog/glm-5-2-vs-llama-4-maverick Date: 2026-07-27 Description: GLM 5.2 vs LLaMA 4 Maverick — benchmark performance, pricing, 1M context vs 1M context, multimodal, and which large open-weights MoE model to use. ## Page: Kimi K3 vs LLaMA 4 Scout: Long-Context Open Models Compared URL: https://glm5.app/blog/kimi-k3-vs-llama-4-scout Date: 2026-07-27 Description: Kimi K3 vs LLaMA 4 Scout — compare 1M vs 10M context, pricing, multimodal, benchmark scores, and which open-source long-context model fits your project. ## Page: Kimi K3 vs Mistral Large 2: Cost, Multilingual, and Performance Compared URL: https://glm5.app/blog/kimi-k3-vs-mistral-large Date: 2026-07-27 Description: Kimi K3 vs Mistral Large 2 — compare pricing, multilingual benchmarks, context length, self-hosting options, and which model to choose for your workload. ## Page: Kimi K3 vs Claude Sonnet 5: Open Weights vs Closed API URL: https://glm5.app/blog/kimi-k3-vs-claude-sonnet-5 Date: 2026-07-28 Description: Kimi K3 vs Claude Sonnet 5 — compare cost, benchmark performance, context length, open weights vs closed source, and which model to choose for your use case. ## Page: Kimi K3 for Coding: Benchmarks, Setup, and Real-World Use Cases URL: https://glm5.app/blog/kimi-k3-for-coding Date: 2026-07-28 Description: How well does Kimi K3 handle coding tasks? Benchmark scores, Python API setup, code generation examples, and how it compares to GPT-4o and Claude Sonnet 5. ## Page: Kimi K3 Context Window: 1M Tokens Explained URL: https://glm5.app/blog/kimi-k3-context-window Date: 2026-07-28 Description: Everything about Kimi K3's 1M token context window: what it means in practice, what fits inside it, performance at long context, and how to use it effectively. ## Page: GLM 5.2 with AutoGen: Build Multi-Agent AI Workflows in Python URL: https://glm5.app/blog/glm-5-2-autogen Date: 2026-07-28 Description: Step-by-step guide to using GLM 5.2 as the LLM backend in AutoGen — configure AssistantAgent, UserProxyAgent, and build multi-agent pipelines with Python code examples. ## Page: GLM 5.2 with CrewAI: Orchestrate AI Agent Teams on a Budget URL: https://glm5.app/blog/glm-5-2-crewai Date: 2026-07-28 Description: Use GLM 5.2 as the LLM backend for CrewAI — set up agents, tasks, and crews in Python, and cut multi-agent costs by 3x vs GPT-4o without sacrificing quality. ## Page: GLM 5.2 for Research: Literature Review, Summarization, and Analysis URL: https://glm5.app/blog/glm-5-2-for-research Date: 2026-07-28 Description: How researchers and academics can use GLM 5.2 for literature reviews, paper summarization, data extraction, and research Q&A — with 1M context for entire paper collections. ## Page: GLM 5.2 vs Command R+: Enterprise AI for RAG and Tool Use Compared URL: https://glm5.app/blog/glm-5-2-vs-command-r-plus Date: 2026-07-28 Description: GLM 5.2 vs Cohere Command R+ — compare pricing, context length, RAG performance, tool use, multilingual support, and which model fits your enterprise AI workload. ## Page: Kimi K3 vs Gemma 3: Chinese Open-Source vs Google Open-Source URL: https://glm5.app/blog/kimi-k3-vs-gemma-3 Date: 2026-07-28 Description: Kimi K3 vs Gemma 3 — compare cost, context length, benchmark scores, self-hosting requirements, and which open-source model is right for your project. ## Page: Kimi K3 vs Phi-4: Large Context vs Compact Efficiency URL: https://glm5.app/blog/kimi-k3-vs-phi-4 Date: 2026-07-28 Description: Kimi K3 vs Microsoft Phi-4 — compare benchmark performance, context length, pricing, self-hosting requirements, and which model to choose for your use case. ## Page: GLM 5.2 vs Llama 3.1 Nemotron 70B: NVIDIA Fine-Tuned vs Zhipu Frontier URL: https://glm5.app/blog/glm-5-2-vs-nemotron Date: 2026-07-28 Description: GLM 5.2 vs Llama 3.1 Nemotron 70B — compare benchmark scores, pricing, deployment options, and which model delivers better results for enterprise AI workloads. ## Page: GLM 5.2 on Hugging Face: Load and Run with Transformers URL: https://glm5.app/blog/glm-5-2-hugging-face Date: 2026-07-31 Description: How to load and run GLM 5.2 (GLM-4) on Hugging Face using the Transformers library — model IDs, hardware requirements, quantization, and inference code examples. ## Page: GLM 5.2 with Dify: Build No-Code AI Apps and Workflows URL: https://glm5.app/blog/glm-5-2-dify Date: 2026-07-31 Description: Step-by-step guide to connecting GLM 5.2 to Dify — configure the OpenAI-compatible provider, build chatbots, agents, and RAG workflows without writing code. ## Page: GLM 5.2 Web Search: Real-Time Browsing with Tool Use URL: https://glm5.app/blog/glm-5-2-web-search Date: 2026-07-31 Description: How to enable real-time web search in GLM 5.2 using function calling — build a web-aware AI assistant with Python code examples and Zhipu's native search tool. ## Page: Kimi K3 Thinking Mode: How Extended Reasoning Works URL: https://glm5.app/blog/kimi-k3-thinking-mode Date: 2026-07-31 Description: Everything about Kimi K3's thinking mode — how extended chain-of-thought reasoning works, when to enable it, API parameters, performance on hard problems, and cost trade-offs. ## Page: Kimi K3 for Writing: Content, Copywriting, and Creative Use Cases URL: https://glm5.app/blog/kimi-k3-for-writing Date: 2026-07-31 Description: How to use Kimi K3 for professional writing — blog posts, copywriting, creative fiction, translation, and content marketing — with prompt templates and real examples. ## Page: Kimi K3 vs GPT-4.1: Long Context vs API Ecosystem URL: https://glm5.app/blog/kimi-k3-vs-gpt-4-1 Date: 2026-07-31 Description: Kimi K3 vs OpenAI GPT-4.1 — compare pricing, context window, benchmark performance, API ecosystem, open weights vs closed source, and which to choose for your use case. ## Page: Kimi K3 vs Fable 5: Open Weights vs Anthropic's Creative Model URL: https://glm5.app/blog/kimi-k3-vs-fable-5 Date: 2026-07-31 Description: Kimi K3 vs Claude Fable 5 — compare pricing, benchmark scores, creative writing quality, context length, open weights vs closed source, and which model fits your use case. ## Page: Kimi K3 vs DeepSeek R1: Two Open-Source Reasoning Models Compared URL: https://glm5.app/blog/kimi-k3-vs-deepseek-r1 Date: 2026-07-31 Description: Kimi K3 vs DeepSeek R1 — compare reasoning performance, pricing, context length, self-hosting requirements, and which open-source model to choose for complex tasks. ## Page: Kimi K3 vs MiniMax M1: Chinese Open-Source Models Head-to-Head URL: https://glm5.app/blog/kimi-k3-vs-minimax-m1 Date: 2026-07-31 Description: Kimi K3 vs MiniMax M1 — compare pricing, context window, benchmark performance, open-source licensing, and which Chinese AI model to choose for your project. ## Page: GLM 5.2 for Healthcare: Clinical Documentation, Research, and Patient Communication URL: https://glm5.app/blog/glm-5-2-for-healthcare Date: 2026-07-31 Description: How healthcare organizations can use GLM 5.2 for clinical documentation, medical literature review, patient Q&A chatbots, and healthcare data extraction — with deployment considerations. ## Page: GLM 5.2 vs DeepSeek V4 Pro: Chinese Open-Source AI Head-to-Head URL: https://glm5.app/blog/glm-5-2-vs-deepseek-v4-pro Date: 2026-08-01 ## Page: Kimi K3 vs DeepSeek V4 Pro: Battle of Chinese Open-Source Reasoning Models URL: https://glm5.app/blog/kimi-k3-vs-deepseek-v4-pro Date: 2026-08-01 ## Page: How to Download GLM 5.2: Open Weights, API Access, and Local Deployment URL: https://glm5.app/blog/glm-5-2-download Date: 2026-08-01 ## Page: GLM 5.2 Free API: How to Get Free Tokens and Try GLM-4-Plus URL: https://glm5.app/blog/glm-5-2-free-api Date: 2026-08-01 ## Page: GLM 5.2 Subscription Plans: API Pricing, Token Bundles, and Enterprise Options URL: https://glm5.app/blog/glm-5-2-subscription Date: 2026-08-01 ## Page: GLM 5.2 Coding Plan: Pricing, Features, and Is It Worth It for Developers? URL: https://glm5.app/blog/glm-5-2-coding-plan Date: 2026-08-01 ## Page: GLM 5.2 with Ollama: Run GLM Models Locally on Your Machine URL: https://glm5.app/blog/glm-5-2-ollama Date: 2026-08-01 ## Page: GLM 5.2 vs MiniMax M1: Comparing Chinese AI Frontier Models URL: https://glm5.app/blog/glm-5-2-vs-minimax-m1 Date: 2026-08-01 ## Page: Colibri AI with GLM 5.2: Enhancing Real-Time AI Conversations URL: https://glm5.app/blog/colibri-glm-5-2 Date: 2026-08-01 ## Page: DeepSeek V4 Online: Try DeepSeek V4 in Your Browser on glm5.app URL: https://glm5.app/blog/deepseek-v4-online Date: 2026-08-01 Description: DeepSeek V4 is now available on glm5.app. Learn how to open the DeepSeek V4 chat page, when to use V4 Pro vs V4 Flash, and what the official DeepSeek API supports. ## Page: What Is DeepSeek V4 Flash? Architecture, Specs & Price URL: https://glm5.app/blog/what-is-deepseek-v4-flash Date: 2026-08-03 Description: DeepSeek V4 Flash explained: the 284B/13B MoE architecture, 1M-token context, first-party pricing, how it differs from V4 Pro, and who should use it. ## Page: DeepSeek V4 Flash vs GLM 5.2: Cheap Speed or Depth? URL: https://glm5.app/blog/deepseek-v4-flash-vs-glm-5-2 Date: 2026-08-03 Description: DeepSeek V4 Flash vs GLM 5.2 compared — pricing, context window, coding and agentic strength, plus a decision framework for choosing the right open-weight model. ## Page: DeepSeek V4 Flash vs V4 Pro: Which Tier to Pick URL: https://glm5.app/blog/deepseek-v4-flash-vs-deepseek-v4-pro Date: 2026-08-03 Description: DeepSeek V4 Flash vs V4 Pro compared: 284B/13B cheap fast tier vs the ~1.6T reasoning flagship. Specs, pricing, and a workload-by-workload decision guide. ## Page: DeepSeek V4 Flash Pricing: Full Cost Breakdown (2026) URL: https://glm5.app/blog/deepseek-v4-flash-pricing Date: 2026-08-03 Description: DeepSeek V4 Flash pricing explained: $0.14/M input, $0.28/M output, provider variation, real cost examples, and how it compares to GLM 5.2 on price per token. ## Page: DeepSeek V4 Flash API: Key, Endpoint, and Python Quickstart URL: https://glm5.app/blog/deepseek-v4-flash-api Date: 2026-08-03 Description: Call the DeepSeek V4 Flash API in minutes: get a key at platform.deepseek.com, hit the OpenAI-compatible endpoint, run Python and curl examples, stream, and read pricing. ## Page: DeepSeek V4 Flash vs GPT-5 Mini: Cheap, Fast Model Compared URL: https://glm5.app/blog/deepseek-v4-flash-vs-gpt-5-mini Date: 2026-08-03 Description: DeepSeek V4 Flash vs GPT-5 Mini compared on price, context, openness, and latency. A practical decision guide for picking the right cheap, fast tier model in 2026. ## Page: DeepSeek V4 Flash vs Gemini 2.5 Flash: Open vs Hosted URL: https://glm5.app/blog/deepseek-v4-flash-vs-gemini-2-5-flash Date: 2026-08-03 Description: DeepSeek V4 Flash vs Gemini 2.5 Flash compared on price, context, openness, multimodality and speed - plus a clear decision guide for picking a fast, cheap model. ## Page: DeepSeek V4 Flash Benchmarks: Speed and Scores Decoded URL: https://glm5.app/blog/deepseek-v4-flash-benchmarks Date: 2026-08-03 Description: DeepSeek V4 Flash benchmarks explained: what is verified on throughput, latency, and coding/MMLU scores, what is not, and how the specs stack up against GLM 5.2. ## Page: DeepSeek V4 Flash Context Window: What 1M Tokens Really Means URL: https://glm5.app/blog/deepseek-v4-flash-context-window Date: 2026-08-03 Description: The DeepSeek V4 Flash context window is 1M tokens with up to 384K output. Here is what that means in pages, use cases, cost, and real recall limits. ## Page: DeepSeek V4 Flash Alternatives: 8 Options Ranked (2026) URL: https://glm5.app/blog/deepseek-v4-flash-alternatives Date: 2026-08-03 Description: The best DeepSeek V4 Flash alternatives, ranked — GLM 5.2, V4 Pro, Gemini 2.5 Flash, GPT-5 Mini, Qwen, Kimi and Llama, with rough pricing, context, and best-for guidance. ## Page: What Is OpenAI Astra? The $2,000 Math Breakthrough Explained URL: https://glm5.app/blog/what-is-openai-astra Date: 2026-08-05 Description: OpenAI Astra explained: the unreleased next model behind 10 new math results and a ~$2,000 token run, with Lean certificates and what it means. ## Page: GLM 5.5: Release Status, What We Know, and Specs to Expect (2026) URL: https://glm5.app/blog/glm-5-5 Date: 2026-08-12 Description: GLM 5.5 has not been released — as of August 2026, Z.AI has made zero official announcements, verified across every official channel (z.ai homepage/blog, Hugging Face zai-org, GitHub, arXiv, news). Covers the GLM release rhythm (GLM-5 Feb 2026, GLM-5.1 May 13, GLM-5.2 June 16), GLM 5.2's official baseline (753B, solid 1M context, MIT, IndexShare ~2.9x FLOP reduction, AIME 2026 99.2 / Terminal-Bench 2.1 81.0 / FrontierSWE 74.4), clearly-labeled speculation on what 5.5 could bring, official release-tracking channels, and a decide-now-or-wait decision rule. All expectations are marked as prediction; article will be updated on the day official information appears. ## Page: What Is DeepSeek V4 Pro? The 1.6T Reasoning Flagship, Explained URL: https://glm5.app/blog/what-is-deepseek-v4-pro Date: 2026-08-13 Description: DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released August 13, 2026 as version 0813. ## Page: DeepSeek V4 Pro Pricing: Full Cost Breakdown (2026) URL: https://glm5.app/blog/deepseek-v4-pro-pricing Date: 2026-08-13 Description: DeepSeek V4 Pro pricing fully broken down: $0.435/M input, $0.87/M output, $0.003625 cache hit, three real workload cost scenarios, and what the official price-increase notice means for budgets. ## Page: DeepSeek V4 Pro API: Key, Endpoint, and Python Quickstart URL: https://glm5.app/blog/deepseek-v4-pro-api Date: 2026-08-13 Description: Connect to the DeepSeek V4 Pro API in 3 steps: get a key at platform.deepseek.com, point at the OpenAI-compatible endpoint, run the Python quickstart, curl, streaming, and thinking-mode controls. ## Page: DeepSeek V4 Pro Benchmarks: 0813 Scores and Speed Decoded URL: https://glm5.app/blog/deepseek-v4-pro-benchmarks Date: 2026-08-13 Description: DeepSeek V4 Pro benchmarks explained: Intelligence Index 53 (#2/104), 83.2 tok/s output, 1.63s TTFT, verbosity warning, and a framework for reading the numbers. ## Page: DeepSeek V4 Pro Context Window: What 1M Tokens Really Means URL: https://glm5.app/blog/deepseek-v4-pro-context-window Date: 2026-08-13 Description: DeepSeek V4 Pro has a 1M-token context window. Here is what 1,048,576 tokens hold in pages and books, the 384K output limit, when you need it, and KV-cache cost math. ## Page: DeepSeek V4 Pro vs GPT-5: Open-Weights Flagship or OpenAI Best? URL: https://glm5.app/blog/deepseek-v4-pro-vs-gpt-5 Date: 2026-08-13 Description: DeepSeek V4 Pro vs GPT-5 compared on price, architecture, openness, 1M context and reasoning — with a scenario decision framework and the honest trade-offs. ## Page: DeepSeek V4 Pro vs Claude Opus 4.8: Open Weights vs Agentic Powerhouse URL: https://glm5.app/blog/deepseek-v4-pro-vs-claude-opus-4-8 Date: 2026-08-13 Description: DeepSeek V4 Pro vs Claude Opus 4.8 — architecture, pricing, reasoning and coding depth, agentic ecosystem, self-hosting reality, and a team-by-team decision framework. ## Page: DeepSeek V4 Pro vs Gemini 2.5 Pro: Open Weights vs Google's Multi-Modal Flagship URL: https://glm5.app/blog/deepseek-v4-pro-vs-gemini-2-5-pro Date: 2026-08-13 Description: DeepSeek V4 Pro vs Gemini 2.5 Pro compared on price, context, modalities, reasoning depth and ecosystem. One question decides the comparison: do you need image, audio or video input?. ## Page: DeepSeek V4 Pro for Coding: A Practical Guide for Developers URL: https://glm5.app/blog/deepseek-v4-pro-for-coding Date: 2026-08-13 Description: How to use DeepSeek V4 Pro for coding: thinking-mode configuration, tool calling, 1M-token repository analysis workflows, cost control with KV cache, and honest limits. ## Page: DeepSeek V4 Pro Alternatives: 7 Flagship Options Ranked (2026) URL: https://glm5.app/blog/deepseek-v4-pro-alternatives Date: 2026-08-13 Description: The best DeepSeek V4 Pro alternatives, ranked by a 6-dimension framework — GLM 5.2, Kimi K3, GPT-5.6, Claude Opus 4.8, Gemini 2.5 Pro, Llama 4 Maverick and Qwen 3, with best-for guidance. ## Page: What Is GLM 5.3? The Coding-First Release That Skips a New Base Model URL: https://glm5.app/blog/what-is-glm-5-3 Date: 2026-08-14 Description: What is GLM 5.3? Z.AI's August 14, 2026 release keeps the GLM 5.2 base model and gains everything from post-training — 50% better coding, emergent cyber capabilities, and open weights in two weeks. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/what-is-glm-5-3.webp - URL path: /blog/what-is-glm-5-3 ## Page: How to Use GLM 5.3: API Setup, Thinking Parameters & Coding Plan URL: https://glm5.app/blog/how-to-use-glm-5-3 Date: 2026-08-14 Description: How to use GLM 5.3 — API setup with the new thinking parameters, GLM Coding Plan points, ZCode tips, and what changed from GLM 5.2. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/how-to-use-glm-5-3.webp - URL path: /blog/how-to-use-glm-5-3 ## Page: GLM 5.3 vs GLM 5.2: What's Actually New (Benchmarks Included) URL: https://glm5.app/blog/glm-5-3-vs-glm-5-2 Date: 2026-08-14 Description: GLM 5.3 vs GLM 5.2 — same base model, all post-training. See the real benchmark deltas, the API change you must make, and whether upgrading is worth it. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-vs-glm-5-2.webp - URL path: /blog/glm-5-3-vs-glm-5-2 ## Page: GLM 5.3 Pricing: API Cost, Token Rates & Coding Plan (2026) URL: https://glm5.app/blog/glm-5-3-pricing Date: 2026-08-14 Updated: 2026-08-26 Description: GLM 5.3 costs $1.40 per 1M input tokens, $0.26 per 1M cached input tokens, and $4.40 per 1M output tokens. See worked API costs and compare the Coding Plan. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-pricing.webp - URL path: /blog/glm-5-3-pricing ## Page: GLM 5.3 Open Weights: Release Date & What Self-Hosting Gets You URL: https://glm5.app/blog/glm-5-3-open-weights Date: 2026-08-14 Description: When do GLM 5.3 weights release? Z.AI says two weeks after the August 14 launch. Here's what the open-weight release includes and what to prepare for self-hosting. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-open-weights.webp - URL path: /blog/glm-5-3-open-weights ## Page: GLM 5.3 Benchmarks: Full Scores vs GLM 5.2, Kimi K3, GPT-5.6 Sol & More URL: https://glm5.app/blog/glm-5-3-benchmarks Date: 2026-08-18 Description: GLM 5.3 benchmark results — Terminal-Bench 3.0 28.3, DeepSWE 66.9, CyberGym 84.5 (SOTA), AutomationBench 48.2. Full official table vs GLM 5.2, Kimi K3, Opus 4.8, GPT-5.6 Sol. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-benchmarks.webp - URL path: /blog/glm-5-3-benchmarks ## Page: GLM 5.3 Coding Plan: Prices, Credits, Limits & Best Tier URL: https://glm5.app/blog/glm-5-3-coding-plan Date: 2026-08-18 Updated: 2026-08-26 Description: Compare GLM 5.3 Coding Plan Lite, Pro and Max prices, 5-hour and weekly credits, off-peak rules, credit multipliers, and which tier fits your workload. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-coding-plan.webp - URL path: /blog/glm-5-3-coding-plan ## Page: GLM 5.3 Weights: Release Date, Specs & What's Inside URL: https://glm5.app/blog/glm-5-3-weights Date: 2026-08-18 Description: GLM 5.3 weights — release date (2 weeks after Aug 14, 2026), same base as GLM 5.2, 1M context, and what the open-weight release means for self-hosting. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-weights.webp - URL path: /blog/glm-5-3-weights ## Page: GLM 5.3 vs GPT-5.6 Sol: Benchmark Battle & Which to Choose URL: https://glm5.app/blog/glm-5-3-vs-sol Date: 2026-08-18 Description: GLM 5.3 vs GPT-5.6 Sol — head-to-head from Z.AI's official benchmark table: CyberGym 84.5 vs 83.6 (GLM wins), Terminal-Bench 3.0 28.3 vs 34.6 (Sol wins), and price: open-weights vs closed API. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-vs-sol.webp - URL path: /blog/glm-5-3-vs-sol ## Page: GLM 5.3 vs Claude Opus 5: Which Coding Model Wins? URL: https://glm5.app/blog/glm-5-3-vs-opus-5 Date: 2026-08-18 Description: GLM 5.3 vs Claude Opus 5 — Z.AI's official table compares GLM 5.3 to Opus 4.8 (GLM wins 6 of 10). Here's how Opus 5 stacks up on price, context, and capability. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-vs-opus-5.webp - URL path: /blog/glm-5-3-vs-opus-5 ## Page: GLM 5.3 on OpenRouter: Availability, Model ID & Setup URL: https://glm5.app/blog/glm-5-3-openrouter Date: 2026-08-18 Description: Is GLM 5.3 on OpenRouter? Not yet — as of August 14, 2026 OpenRouter lists z-ai/glm-5.2 but not glm-5.3. Here's how to use GLM 5.3 today and what to expect when it lands. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-openrouter.webp - URL path: /blog/glm-5-3-openrouter ## Page: Glm5.3 API URL: https://glm5.app/blog/glm5-3-api Date: 2026-08-18 Description: The GLM 5.3 API explained: model ID, specs, thinking modes, and verified benchmarks, plus how to test glm-5.3 with an OpenAI-compatible endpoint today. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm5-3-api.webp - URL path: /blog/glm5-3-api ## Page: GLM 5.3 Release Date URL: https://glm5.app/blog/glm-5-3-release-date Date: 2026-08-18 Description: GLM 5.3 is officially out. Z.ai launched the GLM-5.3 model on August 14, 2026, built on the GLM-5.2 base with post-training-only gains. Here is the confirmed release date, what changed, and how to use it today. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-release-date.webp - URL path: /blog/glm-5-3-release-date ## Page: GLM 5.3 Free URL: https://glm5.app/blog/glm-5-3-free Date: 2026-08-18 Description: GLM 5.3 is Z.ai's newest flagship model — 1M-token context, 128K max output, and always-on reasoning. Try GLM 5.3 free in your browser today, with confirmed facts separated from unconfirmed details. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-free.webp - URL path: /blog/glm-5-3-free ## Page: How To Use Glm AI: Step-by-Step Guide URL: https://glm5.app/blog/how-to-use-glm-ai Date: 2026-08-19 Description: Learn how to use GLM AI step by step — official chat, the Z.ai API, and hosted access — with practical examples, tips, and the most common mistakes to avoid. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/how-to-use-glm-ai.webp - URL path: /blog/how-to-use-glm-ai ## Page: How To Use Glm 5.2 App: Step-by-Step Guide URL: https://glm5.app/blog/how-to-use-glm-5-2-app Date: 2026-08-19 Description: Learn how to use the GLM 5.2 app: open glm5.app in any browser, start a coding chat, and move to the OpenAI-compatible API — with examples and honest platform limits. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/how-to-use-glm-5-2-app.webp - URL path: /blog/how-to-use-glm-5-2-app ## Page: GLM 5.3 on AI Leaderboards: Where It Ranks (AA, Terminal-Bench, CyberGym) URL: https://glm5.app/blog/glm-5-3-ai-leaderboard Date: 2026-08-19 Description: GLM 5.3 leaderboard positions — AA Intelligence Index 60 (8th of 181, tied Kimi K3), Terminal-Bench 3.0 open SOTA 28.3, CyberGym 84.5 best public. Full ranking context. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/glm-5-3-ai-leaderboard.webp - URL path: /blog/glm-5-3-ai-leaderboard ## Page: What Is Ox Alpha? The Free 1M-Context Stealth Model URL: https://glm5.app/blog/what-is-ox-alpha Date: 2026-08-22 Description: What Is Ox Alpha? The free 1M-context stealth reasoning model, explained — official specs, data policy, community rumors, usage signals, and how to try it. Media: https://cdn.glm-image.me/blog/glm5/what-is-ox-alpha.webp - URL path: /blog/what-is-ox-alpha ## Page: Ox Alpha Benchmarks: Community Scores Decoded URL: https://glm5.app/blog/ox-alpha-benchmarks Date: 2026-08-22 Description: Ox Alpha benchmarks: no official scores exist, community DeepSWE and Kingbench results are unverified — here's how to read them and test the model yourself. Media: https://cdn.glm-image.me/blog/glm5/ox-alpha-benchmarks.webp Translations: /zh-CN/blog/ox-alpha-benchmarks, /id/blog/ox-alpha-benchmarks, /de/blog/ox-alpha-benchmarks, /es/blog/ox-alpha-benchmarks, /ru/blog/ox-alpha-benchmarks, /tr/blog/ox-alpha-benchmarks, /ko/blog/ox-alpha-benchmarks, /th/blog/ox-alpha-benchmarks, /ar/blog/ox-alpha-benchmarks - URL path: /blog/ox-alpha-benchmarks ## Page: How to Use Ox Alpha for Free: 3 Ways URL: https://glm5.app/blog/how-to-use-ox-alpha-free Date: 2026-08-22 Description: Use Ox Alpha free three ways: no-key glm5.app browser chat, OpenRouter API at $0/$0 during preview, and OpenCode Go's one-week free window. Setup steps inside. Media: https://cdn.glm-image.me/blog/glm5/how-to-use-ox-alpha-free.webp Translations: /zh-CN/blog/how-to-use-ox-alpha-free, /id/blog/how-to-use-ox-alpha-free, /de/blog/how-to-use-ox-alpha-free, /es/blog/how-to-use-ox-alpha-free, /ru/blog/how-to-use-ox-alpha-free, /tr/blog/how-to-use-ox-alpha-free, /ko/blog/how-to-use-ox-alpha-free, /th/blog/how-to-use-ox-alpha-free, /ar/blog/how-to-use-ox-alpha-free - URL path: /blog/how-to-use-ox-alpha-free ## Page: Ox Alpha on OpenRouter: Model ID, Pricing & API URL: https://glm5.app/blog/ox-alpha-openrouter Date: 2026-08-22 Description: Ox Alpha on OpenRouter — model ID stealth/ox-alpha, free $0/$0 preview pricing, 1M context, and curl + Python API setup for agentic coding. Media: https://cdn.glm-image.me/blog/glm5/ox-alpha-openrouter.webp - URL path: /blog/ox-alpha-openrouter ## Page: How to Use Ox Alpha in OpenCode: Free Setup URL: https://glm5.app/blog/ox-alpha-opencode Date: 2026-08-22 Description: Use Ox Alpha in OpenCode — enable free Ox Alpha Free on OpenCode Go for a week of near-unlimited agentic coding, or connect via OpenRouter (stealth/ox-alpha). Media: https://cdn.glm-image.me/blog/glm5/ox-alpha-opencode.webp - URL path: /blog/ox-alpha-opencode ## Page: Ox Alpha on Reddit: What the Community Is Saying URL: https://glm5.app/blog/ox-alpha-reddit Date: 2026-08-22 Description: Ox Alpha Reddit discussion is thin but real: identity speculation, unverified benchmark scores, and free-window hype — and how to separate fact from rumor. Media: https://cdn.glm-image.me/blog/glm5/ox-alpha-reddit.webp - URL path: /blog/ox-alpha-reddit ## Page: How to Use Ox Alpha: A Practical First-Task Workflow URL: https://glm5.app/blog/how-to-use-ox-alpha Date: 2026-08-23 Description: How to use Ox Alpha: a practical first-task workflow covering reasoning effort, 1M-token context, tools and structured output, plus repeatable mini-evaluation. Media: https://cdn.glm-image.me/blog/glm5/how-to-use-ox-alpha.webp - URL path: /blog/how-to-use-ox-alpha ## Page: Ox Alpha Vs Fable 5 — Ox Alpha vs Fable 5 decision URL: https://glm5.app/blog/ox-alpha-vs-fable-5 Date: 2026-08-24 Description: Ox Alpha vs Fable 5 compared: pricing, 1M context, coding, agentic work, and the stealth-model risk — plus a decision framework and a free GLM 5 alternative. Media: https://adminjen.ducttapeai.com/covers/blog/glm5/ox-alpha-vs-fable-5.webp - URL path: /blog/ox-alpha-vs-fable-5 ## Page: Which Harness Is Best for GLM 5.3 Flash? A Practical Choice Guide URL: https://glm5.app/blog/glm-5-3-flash-harnesses Date: 2026-10-07 Description: Choose a coding harness for GLM 5.3 Flash by checking official support, tool use, context handling, and the quality of your own coding loop. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-flash-harnesses.webp - URL path: /blog/glm-5-3-flash-harnesses ## Page: Why Does GLM 5.2 Repeat Answers Twice? Causes and Fixes URL: https://glm5.app/blog/glm-5-2-repeated-responses Date: 2026-10-07 Description: Diagnose repeated GLM 5.2 answers by separating model repetition from duplicated prompts, streaming UI bugs, and client retries. Media: https://cdn.glm-image.me/blog/glm5/glm-5-2-repeated-responses.webp - URL path: /blog/glm-5-2-repeated-responses ## Page: How Good Is GLM 5.3 Flash for Building Android Apps? URL: https://glm5.app/blog/glm-5-3-flash-android-app-development Date: 2026-10-07 Description: A practical framework for using GLM 5.3 Flash on Android app work, from Kotlin and Compose changes to emulator checks and review. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-flash-android-app-development.webp - URL path: /blog/glm-5-3-flash-android-app-development ## Page: What Is GLM-5.3-FlashX? Z.ai's High-Speed Flash Variant, Explained URL: https://glm5.app/blog/glm-5-3-flashx Date: 2026-10-09 Description: GLM-5.3-FlashX is the high-speed variant of GLM-5.3-Flash: up to 200 tokens/s claimed, $0.37/$1.25 per 1M tokens, same 320B-A18B architecture with a 1M context window. Claimed vs. measured speed, the price premium math, and when FlashX is worth it over Flash. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-flashx.webp - URL path: /blog/glm-5-3-flashx ## Page: GLM 5.3 vs GLM 5.3 Flash: Which Should You Use? URL: https://glm5.app/blog/glm-5-3-vs-glm-5-3-flash Date: 2026-10-09 Description: GLM 5.3 and GLM 5.3 Flash are different tiers with roughly a 10x price gap: $1.40/$4.40 vs $0.15/$0.50 per 1M tokens. Full comparison — pricing math, modality, reasoning, benchmarks, and a decision framework for coding agents, chat products, and self-hosting. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-vs-glm-5-3-flash.webp - URL path: /blog/glm-5-3-vs-glm-5-3-flash ## Page: How to Get GLM 5.3 FlashX: Subscription Plans, Zcode, OpenCode, and API Access URL: https://glm5.app/blog/glm-5-3-flashx-access Date: 2026-10-17 Description: GLM-5.3-FlashX is a documented Z.ai API model code at $0.37/$1.25 per 1M tokens. The same official page says it is not on the Coding Plan yet. How API, Zcode, and OpenCode actually differ. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-flashx-access.webp - URL path: /blog/glm-5-3-flashx-access ## Page: GLM 5.3 FlashX vs DeepSeek V4.1 Flash: Which Fast-Tier Model Wins? URL: https://glm5.app/blog/glm-5-3-flashx-vs-deepseek-v4-1-flash Date: 2026-10-17 Description: GLM-5.3-FlashX is Z.ai's fast serving tier at $0.37/$1.25 per 1M tokens. DeepSeek-V4.1-Flash is a 552B model with peak $0.30/$1.20 and half-price off-peak. A decision, not a fake shared leaderboard. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-flashx-vs-deepseek-v4-1-flash.webp - URL path: /blog/glm-5-3-flashx-vs-deepseek-v4-1-flash ## Page: Can You Run GLM 5.3 FlashX Locally? GGUF, Ollama, and the Weights Reality URL: https://glm5.app/blog/glm-5-3-flashx-local Date: 2026-10-17 Description: There is no official GLM-5.3-FlashX checkpoint. Local installs use GLM-5.3-Flash weights (a ~226 GB safetensors snapshot), not a GGUF or Ollama tag for the hosted fast tier. Media: https://cdn.glm-image.me/blog/glm5/glm-5-3-flashx-local.webp - URL path: /blog/glm-5-3-flashx-local