# GLM 5 Landing Copy

> GLM 5 is a fifth-generation frontier large language model by Zhipu AI. 745B parameters, Mixture-of-Experts architecture with 44B activated, 128K context window, and state-of-the-art reasoning, coding, and agentic AI. One platform for chat, image, and video.

## Navigation
- GLM 5 AI Image Generator (`/generate`, icon: Sparkles)
- GLM 5 AI Features (`/#features`, icon: Zap)
- Pricing (`/pricing`, icon: DollarSign)
- API Reference (`/api/docs`, icon: KeyRound)

## Hero
- Title: GLM 5 — One Platform for Chat, Image & Video
- Description: GLM 5 is a fifth-generation frontier large language model — 745B parameters, MoE architecture, 128K context, and state-of-the-art reasoning, coding & agentic AI.
- CTA: Try GLM 5 Free (`/generate`, target self, icon: Zap)

## Overview
- 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 128K context window — delivering open-source SOTA performance across reasoning, coding, and agentic tasks.
- Highlights:
  - MoE Architecture with 745B Parameters — 78 layers, 256 experts per layer with 8 activated, ~44B active parameters at 5.9% sparsity.
  - Advanced Reasoning & Coding — SOTA on MMLU, BBH, HumanEval, AgentBench. Competes with top proprietary models.
  - 128K Context & Multi-Language — Long-document understanding, multi-turn dialogue. Native English, Chinese, and 15+ languages.
  - Multi-Token Prediction (MTP) — Predicting multiple tokens per forward pass for 2x throughput.

## Usage: Three Steps
1) Access GLM 5 on glm5.app — Visit glm5.app for free credits and instant access.
2) Integrate GLM 5 via API — OpenAI SDK-compatible format with full docs at `/api/docs`. Also available on OpenRouter.
3) Build with GLM 5 Agents — Deploy autonomous agents for coding, data analysis, research, and workflow automation.

## Pages
- [Grok 4.6 API: Pricing, Context Window, and How to Use It](https://glm5.app/blog/grok-4-6): Plan Grok 4.6 API usage with its 500K-token context window, 200K long-context price threshold, and configurable reasoning effort.
- [GLM 5.3 Model](https://glm5.app/glm-5-3): Z.ai new flagship LLM — 1M context, 128K output, top open-source coding benchmarks; GLM 5.3 chat on glm5.app, official API coming soon.
- [GLM 5.3 Flash](https://glm5.app/glm-5-3-flash): Z.ai's 320B-A18B natively multimodal MoE, previewed as Ox Alpha — 1M context, MIT weights on Hugging Face, $0.15/$0.50 list pricing, benchmarks, and how to run or call it.
- [GLM 5.2 Chat & API](https://glm5.app/glm-5-2): A text-first GLM 5.2 coding page with a real Chat handoff, documented OpenAI-compatible API, 64K public context, 8,192 maximum output, and function tools.
- [GLM 5.5 Coming Soon](https://glm5.app/glm-5-5): A release tracker for the anticipated GLM 5.5 model — not yet announced by Z.ai; verified news, release-date, size, pricing, and open-source expectations, plus free GLM 5.2 access.
- [GLM 5 Chat](https://glm5.app/chat): Free online chat with GLM 5 — ask questions, write and debug code, brainstorm ideas, and analyze images. No install required.
- [GLM 5 API Reference](https://glm5.app/api/docs): OpenAI-compatible chat completions, models, API keys, billing, streaming, and function calling.

## Features
- Advanced Reasoning — Multi-step logic, math, and analytical tasks with chain-of-thought.
- Agentic AI Workflows — Tool use, function calling, multi-turn planning, and self-correction.
- Code Generation — SOTA on HumanEval and BigCodeBench. 50+ programming languages.
- Creative Writing — Long-form content, marketing copy, technical docs, and fiction.
- 128K Context Window — Process entire codebases, papers, and documents in one prompt.
- Image Generation — Seedream 5.0 for photorealistic 2K images from text prompts.

## FAQ
- What is GLM 5? Fifth-generation frontier LLM with 745B parameters, MoE architecture, 128K context.
- Is GLM 5 free? Yes — free credits for new users on glm5.app. Paid plans available.
- API access? OpenAI SDK-compatible API via glm5.app and OpenRouter.
- Agent capabilities? Tool use, function calling, multi-turn planning, self-correction.

## Blog (/blog)
Technical articles and model comparisons on GLM 5.2 and frontier AI (English only):
- GLM 5.2 vs Claude Opus 4.8: Coding, Compared (`/blog/glm-5-2-vs-claude-opus-4-8`) — Benchmarks, 1M context window, open weights, and real-world coding compared head-to-head; how to try GLM 5.2 free in the browser.
- What Is GLM 5.2? Features, Specs & Open Weights (`/blog/what-is-glm-5-2`) — Z.AI's open-weight LLM explained: 1M-token context, thinking modes, tool calling, what it's built for, and how to try it free.
- GLM 5.2 Benchmarks: How It Really Performs (`/blog/glm-5-2-benchmarks`) — What the GLM 5.2 scores actually say: SWE-bench Pro 62.1, FrontierSWE 74.4, Terminal-Bench 81.0; beats GPT-5.5, trails Opus 4.8 only on the hardest tasks, at a fraction of the cost.
- GLM 5.2 Pricing: API Cost, Plans & Free Tiers (`/blog/glm-5-2-pricing`) — Z.AI API ~$1.40/$4.40 per 1M tokens, cheaper via OpenRouter, Coding Plan subscriptions, and free self-host/browser routes; ~5-6x cheaper output than Claude/GPT-5.5.
- GLM 5.2 Alternatives: 6 Open-Weight Coding Models (`/blog/glm-5-2-alternatives`) — 6 open-weight alternatives (DeepSeek V4, Qwen, Kimi, MiniMax, GLM-5.1) compared; each wins on one axis, but GLM 5.2 leads the open-weight leaderboards overall.
- Run GLM 5.2 Locally: Ollama, VRAM & Hardware Guide (`/blog/how-to-run-glm-5-2-locally`) — Honest local setup guide covering Ollama cloud vs true local inference, Unsloth GGUF memory tiers, llama.cpp steps for Mac/Linux, and when browser access is the better path.
- GLM 5.2 vs Fable 5: Open Source vs Closed (`/blog/glm-5-2-vs-fable-5`) — GLM 5.2 (MIT open-weight, $1.40/1M) vs Claude Fable 5 (closed, $10/1M); GLM 5.2 is #1 on Design Arena (Elo 1360), Fable 5 leads frontend coding; full benchmark, price, and self-hosting breakdown.
- How to Use GLM 5.2 for Free: 4 Methods That Work in 2026 (`/blog/how-to-use-glm-5-2-for-free`) — Four free access routes: browser chat at glm5.app (zero setup), NVIDIA NIM free API credits, Cloudflare Workers AI (10k neurons/day, officially listed), and MIT self-hosting (needs 240GB+ RAM); honest limits and decision table for each.
- Is GLM 5.2 Multimodal? Vision Capabilities Explained (`/blog/is-glm-5-2-multimodal`) — GLM 5.2 is text-only (no image input); Z.ai's actual vision model is GLM-5V-Turbo (closed API, Design2Code 94.8); explains the three sources of multimodal confusion, GLM 5.2 vs GLM-5V-Turbo comparison table, and how to pair GLM 5.2 with a vision model in a pipeline.
- GLM 5.2 vs GPT-4o: Benchmarks, Pricing, and the Real Cost Difference (`/blog/glm-5-2-vs-gpt-4o`) — GLM 5.2 costs 56% less per output token than GPT-4o and runs 3x faster, but GPT-4o adds audio and multimodal input that GLM 5.2 does not support.
- GLM 5.2 API: Endpoints, Authentication, and Python Integration Guide (`/blog/glm-5-2-api`) — GLM 5.2 uses the OpenAI SDK format with a different base_url and model name. Here is the complete guide to endpoints, authentication, streaming, and Python integration.
- GLM 5.2 Context Window: What 1 Million Tokens Actually Means (`/blog/glm-5-2-context-window`) — GLM 5.2 supports 1,048,576 tokens — 8x GPT-4o's 128K. Here is what that capacity enables for codebases, documents, and long agent sessions, and when it matters.
- GLM 5.2 Architecture: 753B Parameters, MoE Design, and How It Works (`/blog/glm-5-2-architecture`) — GLM 5.2 uses Mixture-of-Experts with 753B total parameters but only 40B active per token. Here is how its architecture works and what it means for cost, speed, and capability.
- GLM 5.2 License: MIT Open Weights and What It Means for Commercial Use (`/blog/glm-5-2-license`) — GLM 5.2 weights are MIT-licensed with no commercial restrictions. Here is what you can do — fine-tune, redistribute, build products — and how MIT compares to Llama and Apache licenses.
- Kimi K3 Pricing: API Costs, Context Window, and Value Compared (`/blog/kimi-k3-pricing`) — Kimi K3 costs $3.00/M input and $15.00/M output tokens. That is 3.4x more expensive than GLM 5.2 per output token for a 6-point AA Intelligence advantage. Here is the full breakdown.
- How to Use GLM 5.2 API with Python: Complete Integration Guide (`/blog/how-to-use-glm-5-2-api-python`) — GLM 5.2 is OpenAI SDK-compatible. Change base_url and model name and your existing Python code works. Here is the step-by-step guide with streaming, function calling, and async examples.
- GLM 5.2 for Coding: Benchmarks, Best Prompts, and IDE Integration (`/blog/glm-5-2-for-coding`) — GLM 5.2 scores 62.1% on SWE-bench Pro and 78% on Terminal-Bench. Here is how to use it as a coding assistant, what prompt patterns work best, and how to integrate it with VS Code and Cursor.
- How to Fine-Tune GLM 5.2: Hardware Requirements and What Is Actually Practical (`/blog/how-to-fine-tune-glm-5-2`) — GLM 5.2 MIT license allows fine-tuning. Full 753B fine-tuning requires 16+ H100s. LoRA on quantized versions is feasible on 2-4 A100s. Here is what works and what does not.
- GLM 5.2 Function Calling: Tool Use, Parallel Calls, and Agentic Workflows (`/blog/glm-5-2-function-calling`) — GLM 5.2 supports OpenAI-compatible function calling with parallel tool calls. Here is how to define tools, handle responses, build multi-step agents, and run real agentic loops.
- GLM 5.2 System Prompt Guide: Templates for Coding, Analysis, and Writing (`/blog/glm-5-2-system-prompt`) — The system prompt sets the frame for everything GLM 5.2 outputs. Here are proven templates for code generation, document analysis, and structured extraction, with token-efficiency tips.
- GLM 5.2 vs DeepSeek V3: Benchmarks, Pricing, and Use Cases (`/blog/glm-5-2-vs-deepseek-v3`) — DeepSeek V3 costs 75% less per output token than GLM 5.2 but scores 4 points lower on the AA Intelligence Index and has 8x less context. Here is when each model is the right call.
- GLM 5.2 vs Gemini 2.5 Pro: Performance, Price, and When to Choose Each (`/blog/glm-5-2-vs-gemini-2-5-pro`) — Gemini 2.5 Pro scores ~75 on the AA Intelligence Index versus GLM 5.2's 51, but output tokens cost 2.3x more and it is closed source. GLM 5.2 is 3x faster. Here is the full breakdown.
- GLM 5.2 vs Llama 3.3 70B: Open-Weight Models Compared on Speed, Benchmarks, and Cost (`/blog/glm-5-2-vs-llama-3-3`) — Llama 3.3 70B costs 80% less via Groq than GLM 5.2 direct but scores 9 points lower on the AA Index and has 8x less context. Here is which open-weight model fits your workload.
- Kimi K3 vs GPT-4o: Benchmarks, Speed, and When to Choose Each (`/blog/kimi-k3-vs-gpt-4o`) — Kimi K3 and GPT-4o both score around 57 on the AA Intelligence Index but take opposite positions on cost and context. K3 output tokens cost 50% more; GPT-4o has audio input but only 128K context.
- GLM 5.2 vs Qwen 3: Chinese Open-Weight AI Models Compared (`/blog/glm-5-2-vs-qwen-3`) — Qwen 3 235B-A22B costs 73% less per output token than GLM 5.2, but GLM 5.2 has 8x larger context and stronger agentic benchmark results. Here is how these open-weight leaders compare.
- GLM 5.2 vs Mistral Large 2: Benchmarks, Cost, and Enterprise Fit (`/blog/glm-5-2-vs-mistral-large`) — Mistral Large 2 costs $6.00/M output tokens versus GLM 5.2's $4.40 and scores lower on most benchmarks. GLM 5.2 also has 8x larger context and MIT-open weights. Here is the full comparison.
- GLM 5.2 vs Grok 3: Speed, Pricing, and Benchmark Comparison (`/blog/glm-5-2-vs-grok-3`) — Grok 3 scores ~68 on the AA Intelligence Index versus GLM 5.2's 51, but output tokens cost 3.4x more and responses arrive 3x slower. Here is when each model is worth it.
- GLM 5.2 for Data Analysis: Structured Output, SQL Generation, and Python Workflows (`/blog/glm-5-2-for-data-analysis`) — GLM 5.2's 1M context window and JSON mode make it practical for large-scale data analysis. Here is how to use it for SQL generation, CSV analysis, structured extraction, and Python data workflows.
- GLM 5.2 on OpenRouter: Access, Pricing, and Integration Guide (`/blog/glm-5-2-openrouter`) — GLM 5.2 is available on OpenRouter as z-ai/glm-5.2. Here is how to access it, how OpenRouter pricing compares to Z.ai direct, and how to switch from any other model with one line of code.
- GLM 5.2 vs GLM-4: What Changed and Whether the Upgrade Is Worth It (`/blog/glm-5-2-vs-glm-4`) — GLM 5.2 has 753B parameters versus GLM-4's 9B-130B range, scores significantly higher on reasoning benchmarks, and offers 1M token context. Here is when the upgrade is worth it.
- GLM 5.2 vs GPT-5.6 Sol: Benchmarks, Pricing, and the 6.8× Cost Gap (`/blog/glm-5-2-vs-gpt-5-6-sol`) — GPT-5.6 Sol costs $30/M output tokens versus GLM 5.2's $4.40 — 6.8× more expensive. Sol leads on GPQA Diamond (94.6% vs 89%) and Terminal-Bench, but both score within 2.5 points on SWE-bench Pro. When each model is worth the premium.
- GLM 5.2 vs GPT-4.1: Benchmarks, Pricing, and When to Choose (/blog/glm-5-2-vs-gpt-4-1) — 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.
- GLM 5.2 vs Claude Sonnet 5: Cost, Benchmarks, and Open vs Closed (/blog/glm-5-2-vs-claude-sonnet-5) — 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.
- GLM 5.2 vs Phi-4: Frontier Scale vs Efficient Small Model (/blog/glm-5-2-vs-phi-4) — 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.
- GLM 5.2 vs Claude Haiku 4.5: Speed, Cost, and When Size Matters (/blog/glm-5-2-vs-haiku-4-5) — 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.
- GLM 5.2 vs Gemini 2.5 Flash: Cost, Speed, and Benchmark Comparison (/blog/glm-5-2-vs-gemini-2-5-flash) — 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.
- GLM 5.2 vs MiniMax M1: Chinese AI Models Head-to-Head (/blog/glm-5-2-vs-minimax-m1) — 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.
- Kimi K3 vs DeepSeek V3: Cost, Speed, and Coding Benchmarks (/blog/kimi-k3-vs-deepseek-v3) — 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.
- Kimi K3 vs Claude Opus 4.8: Frontier Models Head-to-Head (/blog/kimi-k3-vs-claude-opus-4-8) — 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.
- Kimi K3 vs Gemini 2.5 Pro: Performance, Price, and Context Window (/blog/kimi-k3-vs-gemini-2-5-pro) — 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.
- GLM 5.2 Thinking Mode: How Extended Reasoning Works (/blog/glm-5-2-thinking-mode) — 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.
- GLM 5.2 JSON Mode: Structured Output and Schema Enforcement (/blog/glm-5-2-json-mode) — 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.
- GLM 5.2 GGUF: Quantized Weights and Local Inference Guide (/blog/glm-5-2-gguf) — 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.
- GLM 5.2 Temperature and Sampling: How to Control Output Quality (/blog/glm-5-2-temperature) — 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.
- GLM 5.2 API Rate Limits: Tiers, Quotas, and Error Handling (/blog/glm-5-2-rate-limits) — 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.
- How to Use GLM 5.2 with LangChain: Complete Integration Guide (/blog/glm-5-2-langchain) — 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.
- GLM 5.2 RAG: Building Retrieval-Augmented Generation Applications (/blog/glm-5-2-rag) — 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.
- GLM 5.2 Streaming API: Real-Time Token Output Guide (/blog/glm-5-2-streaming) — 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.
- GLM 5.2 Batch Processing: High-Volume API Workflows (/blog/glm-5-2-batch-processing) — 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.
- GLM 5.2 for Writing: Prompts, Quality, and Content Workflows (/blog/glm-5-2-for-writing) — 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.
- Deploying GLM 5.2 with Docker: Self-Hosting and vLLM API Setup (/blog/glm-5-2-docker) — 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.

- 智谱 GLM 5.3 是什么？全新编程旗舰全面解析 (`/blog/zhipu-glm-5-3`) — 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。
- Z.AI GLM 5.3: How to Access the New Flagship (Coding Plan, ZCode, API) (`/blog/z-ai-glm-5-3`) — 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.
- When Is GLM 5.3 Coming Out? Release Date & Availability (`/blog/when-is-glm-5-3-coming-out`) — 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.
- Kimi K3 vs GLM 5.3：基准对比、独立评测与选购建议 (`/blog/kimi-k3-vs-glm-5-3`) — 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。
- ZCode + GLM 5.3: The Complete Guide to Z.AI's Coding Agent (`/blog/glm-5-3-zcode`) — 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.
- GLM 5.3 vs Kimi K3: Benchmarks, Intelligence Index & Which Is Better (`/blog/glm-5-3-vs-kimi-k3`) — 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…
- GLM 5.3 vs Claude Fable 5: Coding, Agents & the Open-Weight Gap (`/blog/glm-5-3-vs-fable-5`) — 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…
- GLM 5.3 什么时候发布？发布时间线与获取方式 (`/blog/glm-5-3-release-date-zh`) — 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。
- GLM 5.3 on Reddit: What the Community Is Saying (`/blog/glm-5-3-reddit`) — 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.
- GLM 5.3 Post-Training Explained: How Scaling RL Made One Model Jump 6x (`/blog/glm-5-3-post-training`) — 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…
- GLM 5.3 OpenRouter：上架状态、模型 ID 与接入教程 (`/blog/glm-5-3-openrouter-zh`) — 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。
- How to Use GLM 5.3 in OpenCode: Setup Guide (`/blog/glm-5-3-opencode`) — 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.
- GLM 5.3 Ollama：本地运行指南与硬件要求（权重发布后可用） (`/blog/glm-5-3-ollama`) — 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。
- How to Migrate from GLM 5.2 to GLM 5.3: Thinking Parameters & Breaking Changes (`/blog/glm-5-3-migration-guide`) — 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…
- GLM 5.3 Local Deployment: Hardware Requirements & What to Prepare (`/blog/glm-5-3-local-deployment`) — 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.
- GLM 5.3 DeepSWE v1.1: 66.9 Explained — What the Score Means (`/blog/glm-5-3-deepswe`) — 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…
- GLM 5.3 Cybersecurity: CyberGym SOTA, 2,436 Real Vulnerabilities & the Disclosure Ledger (`/blog/glm-5-3-cybersecurity`) — 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…
- GLM 5.3 Found a Vulnerability in Cursor: What We Know (`/blog/glm-5-3-cursor-vulnerability`) — 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…
- GLM 5.3 Artificial Analysis Score: 60 on the Intelligence Index — Explained (`/blog/glm-5-3-artificial-analysis`) — 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).
- GLM 5.3 Architecture: Same Base as 5.2, ~743B MoE — Post-Training Explained (`/blog/glm-5-3-architecture`) — 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.
- GLM 5.3 API: Endpoints, Parameters & Python Examples (`/blog/glm-5-3-api`) — 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.

- [What Is Ox Alpha? The Free 1M-Context Stealth Model](https://glm5.app/blog/what-is-ox-alpha) — 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.
- [Ox Alpha Benchmarks: Community Scores Decoded](https://glm5.app/blog/ox-alpha-benchmarks) — 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.
- [How to Use Ox Alpha for Free: 3 Ways](https://glm5.app/blog/how-to-use-ox-alpha-free) — 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.
- [Ox Alpha on OpenRouter: Model ID, Pricing & API](https://glm5.app/blog/ox-alpha-openrouter) — Ox Alpha on OpenRouter — model ID stealth/ox-alpha, free $0/$0 preview pricing, 1M context, and curl + Python API setup for agentic coding.
- [How to Use Ox Alpha in OpenCode: Free Setup](https://glm5.app/blog/ox-alpha-opencode) — 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).
- [Ox Alpha on Reddit: What the Community Is Saying](https://glm5.app/blog/ox-alpha-reddit) — Ox Alpha Reddit discussion is thin but real: identity speculation, unverified benchmark scores, and free-window hype — and how to separate fact from rumor.
- [What Is GPT-6 Astra? OpenAI's Computer-Use Flagship](https://glm5.app/blog/what-is-gpt-6-astra) — GPT-6 Astra explained: released September 3 2026, 1.05M context, $10/$50 per 1M tokens, state-of-the-art computer use, and the three benchmarks OpenAI's own appendix concedes to Anthropic.
- [What Is Claude Fable 5.1? Anthropic's Cheaper Frontier Model](https://glm5.app/blog/what-is-claude-fable-5-1) — Claude Fable 5.1 explained: released September 1 2026, 1M context, unchanged $10/$50 rates, cache reads cut 75% to $0.25, three breaking changes, and how Mythos 5.1 differs.
- [GPT-6 Astra vs Claude Fable 5.1: Same Price, Different Bill](https://glm5.app/blog/gpt-6-astra-vs-claude-fable-5-1) — Identical $10/$50 list price but 4x apart on cache reads, plus Astra's 272K surcharge; both vendors' benchmark tables side by side with independent Artificial Analysis numbers.
- [How to Use GPT-6 Astra: ChatGPT, API & Codex](https://glm5.app/blog/how-to-use-gpt-6-astra) — Setup guide for GPT-6 Astra across ChatGPT, the OpenAI API, Codex, Azure and Bedrock — effort levels, the 272K pricing cliff, rate limits by tier, and what it refuses.
- [Claude Fable 5.1 Pricing: What 25% Cheaper Means](https://glm5.app/blog/claude-fable-5-1-pricing) — Full Fable 5.1 rate card and why the 25% saving is a cache-read discount, not a rate cut; three worked examples landing on 0%, 26% and 45%.
- [What Is GLM 5.3 Flash? Z.ai's 320B-A18B Multimodal Model](https://glm5.app/blog/what-is-glm-5-3-flash) — 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.
- [GLM 5.3 Flash Pricing: Real Cost per Token, per Provider](https://glm5.app/blog/glm-5-3-flash-pricing) — $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.
- [GLM 5.3 Flash Benchmarks: Vendor Claims vs Independent Numbers](https://glm5.app/blog/glm-5-3-flash-benchmarks) — 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.
- [GLM 5.3 Flash vs DeepSeek V4 Flash: Which Cheap Open Model Wins](https://glm5.app/blog/glm-5-3-flash-vs-deepseek-v4-flash) — Two cheap open-weight models compared on price, context, multimodality and benchmark scores, with the workloads each one actually suits.
- [GLM 5.3 Flash Parameters and Size: 320B-A18B, 328 GB, MIT Weights](https://glm5.app/blog/glm-5-3-flash-parameters) — 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.
- [GLM 5.3 Flash on OpenRouter: Model ID, Provider Prices & API Setup](https://glm5.app/blog/glm-5-3-flash-openrouter) — 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.
- [GLM 5.3 Flash on DGX Spark: Does It Fit, and How Fast Is It](https://glm5.app/blog/glm-5-3-flash-dgx-spark) — Whether 328 GB of FP8 weights fit NVIDIA DGX Spark hardware, what quantization it takes, and the community-reported throughput.
- [GLM 5.3 Flash on Reddit: What the Community Actually Found](https://glm5.app/blog/glm-5-3-flash-reddit) — Community reports on GLM 5.3 Flash separated from vendor claims — what is corroborated, what is unverified, and what is rumour.
- [Ox Alpha vs GLM 5.3 Flash: They Are the Same Model](https://glm5.app/blog/ox-alpha-vs-glm-5-3-flash) — Ox Alpha was GLM 5.3 Flash under a codename. What changed between the free stealth preview and the priced public launch.

## Call to Action
- Start Using GLM 5 Today — Try it free.
- Button: Try GLM 5 Free (`/generate`, target self, icon: Zap)

## Contact
- Website: https://glm5.app
- Email: support@glm5.app
- [GLM 5.2 Embeddings: Generate Text Vectors with the Zhipu API](https://glm5.app/blog/glm-5-2-embeddings): 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.
- [GLM 5.2 Tokenizer: Token Counting, Vocabulary Size, and Cost Estimation](https://glm5.app/blog/glm-5-2-tokenizer): Understand how GLM 5.2's tokenizer handles Chinese, English, and code — and how to estimate token counts accurately to control API costs.
- [GLM 5.2 with LlamaIndex: Build RAG Pipelines in Python](https://glm5.app/blog/glm-5-2-llamaindex): 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.
- [GLM 5.2 for Customer Service: Accuracy, Cost, and Multilingual Support](https://glm5.app/blog/glm-5-2-for-customer-service): 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.
- [GLM 5.2 Agentic Workflows: Function Calling, Tool Use, and Multi-Step Tasks](https://glm5.app/blog/glm-5-2-agentic-workflows): Build production AI agents with GLM 5.2 using function calling, parallel tool execution, and multi-step reasoning — complete Python examples included.
- [GLM 5.2 Cost Optimization: 6 Strategies to Reduce API Spend](https://glm5.app/blog/glm-5-2-cost-optimization): 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.
- [GLM 5.2 Multilingual: Languages Supported, Benchmarks, and Real-World Use](https://glm5.app/blog/glm-5-2-multilingual): 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.
- [GLM 5.2 vs LLaMA 4 Scout: Open-Source Giants Compared](https://glm5.app/blog/glm-5-2-vs-llama-4-scout): 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.
- [Kimi K3 vs Qwen 3: Chinese AI Frontier Models Compared](https://glm5.app/blog/kimi-k3-vs-qwen-3): Kimi K3 vs Qwen 3 — cost per million tokens, thinking mode, context length, coding benchmarks, and which Chinese AI model fits your workflow.
- [Kimi K3 vs Grok 3: Open Weights vs Closed Source](https://glm5.app/blog/kimi-k3-vs-grok-3): Kimi K3 vs Grok 3 — compare cost, context length, benchmark performance, self-hosting options, and which model to choose for your AI application.
- [GLM 5.2 Vision API: Image Understanding, Analysis, and Multimodal Use Cases](https://glm5.app/blog/glm-5-2-vision): 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.
- [GLM 5.2 Structured Output: JSON Mode, Schema Validation, and Reliable Extraction](https://glm5.app/blog/glm-5-2-structured-output): Master GLM 5.2 structured output: enable JSON mode, define output schemas, validate responses, and build reliable data extraction pipelines with Python.
- [GLM 5.2 for Legal: Contract Analysis, Document Review, and Compliance Use Cases](https://glm5.app/blog/glm-5-2-for-legal): Evaluate GLM 5.2 for legal work: contract clause extraction, document summarization, compliance checking, and how its 1M token context handles large legal files.
- [GLM 5.2 for Education: Tutoring, Assessment, and Learning App Development](https://glm5.app/blog/glm-5-2-for-education): How to use GLM 5.2 to build education tools: AI tutoring, automated essay feedback, quiz generation, and multilingual learning apps — with cost estimates.
- [Kimi K3 API: Authentication, Endpoints, and Python Integration Guide](https://glm5.app/blog/kimi-k3-api): Get started with the Kimi K3 API: set up authentication, call chat completions, use streaming and function calling — complete Python examples included.
- [GLM 5.2 vs Gemma 3: Open-Source AI from China and Google Compared](https://glm5.app/blog/glm-5-2-vs-gemma-3): GLM 5.2 vs Gemma 3 — benchmark scores, pricing, context length, multimodal support, deployment options, and which open-weights model fits your use case.
- [GLM 5.2 vs DeepSeek R1: General LLM vs Reasoning Specialist Compared](https://glm5.app/blog/glm-5-2-vs-deepseek-r1): 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.
- [GLM 5.2 vs LLaMA 4 Maverick: Large Open-Source MoE Models Compared](https://glm5.app/blog/glm-5-2-vs-llama-4-maverick): 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.
- [Kimi K3 vs LLaMA 4 Scout: Long-Context Open Models Compared](https://glm5.app/blog/kimi-k3-vs-llama-4-scout): 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.
- [Kimi K3 vs Mistral Large 2: Cost, Multilingual, and Performance Compared](https://glm5.app/blog/kimi-k3-vs-mistral-large): Kimi K3 vs Mistral Large 2 — compare pricing, multilingual benchmarks, context length, self-hosting options, and which model to choose for your workload.
- [Kimi K3 vs Claude Sonnet 5: Open Weights vs Closed API](https://glm5.app/blog/kimi-k3-vs-claude-sonnet-5): 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.
- [Kimi K3 for Coding: Benchmarks, Setup, and Real-World Use Cases](https://glm5.app/blog/kimi-k3-for-coding): 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.
- [Kimi K3 Context Window: 1M Tokens Explained](https://glm5.app/blog/kimi-k3-context-window): 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.
- [GLM 5.2 with AutoGen: Build Multi-Agent AI Workflows in Python](https://glm5.app/blog/glm-5-2-autogen): 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.
- [GLM 5.2 with CrewAI: Orchestrate AI Agent Teams on a Budget](https://glm5.app/blog/glm-5-2-crewai): 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.
- [GLM 5.2 for Research: Literature Review, Summarization, and Analysis](https://glm5.app/blog/glm-5-2-for-research): 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.
- [GLM 5.2 vs Command R+: Enterprise AI for RAG and Tool Use Compared](https://glm5.app/blog/glm-5-2-vs-command-r-plus): 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.
- [Kimi K3 vs Gemma 3: Chinese Open-Source vs Google Open-Source](https://glm5.app/blog/kimi-k3-vs-gemma-3): Kimi K3 vs Gemma 3 — compare cost, context length, benchmark scores, self-hosting requirements, and which open-source model is right for your project.
- [Kimi K3 vs Phi-4: Large Context vs Compact Efficiency](https://glm5.app/blog/kimi-k3-vs-phi-4): Kimi K3 vs Microsoft Phi-4 — compare benchmark performance, context length, pricing, self-hosting requirements, and which model to choose for your use case.
- [GLM 5.2 vs Llama 3.1 Nemotron 70B: NVIDIA Fine-Tuned vs Zhipu Frontier](https://glm5.app/blog/glm-5-2-vs-nemotron): 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.
- [GLM 5.2 on Hugging Face: Load and Run with Transformers](https://glm5.app/blog/glm-5-2-hugging-face): 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.
- [GLM 5.2 with Dify: Build No-Code AI Apps and Workflows](https://glm5.app/blog/glm-5-2-dify): 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.
- [GLM 5.2 Web Search: Real-Time Browsing with Tool Use](https://glm5.app/blog/glm-5-2-web-search): 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.
- [Kimi K3 Thinking Mode: How Extended Reasoning Works](https://glm5.app/blog/kimi-k3-thinking-mode): 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.
- [Kimi K3 for Writing: Content, Copywriting, and Creative Use Cases](https://glm5.app/blog/kimi-k3-for-writing): How to use Kimi K3 for professional writing — blog posts, copywriting, creative fiction, translation, and content marketing — with prompt templates and real examples.
- [Kimi K3 vs GPT-4.1: Long Context vs API Ecosystem](https://glm5.app/blog/kimi-k3-vs-gpt-4-1): 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.
- [Kimi K3 vs Fable 5: Open Weights vs Anthropic's Creative Model](https://glm5.app/blog/kimi-k3-vs-fable-5): 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.
- [Kimi K3 vs DeepSeek R1: Two Open-Source Reasoning Models Compared](https://glm5.app/blog/kimi-k3-vs-deepseek-r1): Kimi K3 vs DeepSeek R1 — compare reasoning performance, pricing, context length, self-hosting requirements, and which open-source model to choose for complex tasks.
- [Kimi K3 vs MiniMax M1: Chinese Open-Source Models Head-to-Head](https://glm5.app/blog/kimi-k3-vs-minimax-m1): Kimi K3 vs MiniMax M1 — compare pricing, context window, benchmark performance, open-source licensing, and which Chinese AI model to choose for your project.
- [GLM 5.2 for Healthcare: Clinical Documentation, Research, and Patient Communication](https://glm5.app/blog/glm-5-2-for-healthcare): 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.
- [GLM 5.2 vs DeepSeek V4 Pro: Chinese Open-Source AI Head-to-Head](https://glm5.app/blog/glm-5-2-vs-deepseek-v4-pro): GLM 5.2 vs DeepSeek V4 Pro — compare benchmark scores, pricing, context window, open-source licensing, and which Chinese frontier model fits your workload.
- [Kimi K3 vs DeepSeek V4 Pro: Battle of Chinese Open-Source Reasoning Models](https://glm5.app/blog/kimi-k3-vs-deepseek-v4-pro): Kimi K3 vs DeepSeek V4 Pro — compare long-context performance, reasoning benchmarks, pricing, and open-source licensing to find the right Chinese AI model for your project.
- [How to Download GLM 5.2: Open Weights, API Access, and Local Deployment](https://glm5.app/blog/glm-5-2-download): Step-by-step guide to downloading GLM 5.2 open weights from HuggingFace, running locally with llama.cpp or vLLM, or accessing via API without download.
- [GLM 5.2 Free API: How to Get Free Tokens and Try GLM-4-Plus](https://glm5.app/blog/glm-5-2-free-api): Learn how to access GLM 5.2 (GLM-4-Plus) for free: sign up on BigModel platform, claim free trial tokens, and start calling the API in minutes.
- [GLM 5.2 Subscription Plans: API Pricing, Token Bundles, and Enterprise Options](https://glm5.app/blog/glm-5-2-subscription): Compare GLM 5.2 subscription and pricing plans: pay-as-you-go API rates, token bundles, enterprise contracts, and how to choose the right plan for your usage.
- [GLM 5.2 Coding Plan: Pricing, Features, and Is It Worth It for Developers?](https://glm5.app/blog/glm-5-2-coding-plan): Explore GLM 5.2 coding capabilities and pricing options for developers: SWE-bench scores, code generation benchmarks, API setup, and the value of coding-focused usage plans.
- [GLM 5.2 with Ollama: Run GLM Models Locally on Your Machine](https://glm5.app/blog/glm-5-2-ollama): Learn how to run GLM models locally using Ollama: download GLM-4-9B or compatible GLM models, configure Ollama, and compare local performance to the cloud GLM 5.2 API.
- [GLM 5.2 vs MiniMax M1: Comparing Chinese AI Frontier Models](https://glm5.app/blog/glm-5-2-vs-minimax-m1): GLM 5.2 vs MiniMax M1 — in-depth comparison of two leading Chinese AI models: benchmark scores, context window, pricing, API access, and which fits your use case.
- [Colibri AI with GLM 5.2: Enhancing Real-Time AI Conversations](https://glm5.app/blog/colibri-glm-5-2): Discover how Colibri AI integrates with GLM 5.2 for real-time conversation intelligence, live coaching, and multilingual AI support — including setup and use cases.
- [DeepSeek V4 Online: Try DeepSeek V4 in Your Browser on glm5.app](https://glm5.app/blog/deepseek-v4-online): 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.
- [What Is DeepSeek V4 Flash? Architecture, Specs & Price](https://glm5.app/blog/what-is-deepseek-v4-flash): 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.
- [DeepSeek V4 Flash vs GLM 5.2: Cheap Speed or Depth?](https://glm5.app/blog/deepseek-v4-flash-vs-glm-5-2): 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.
- [DeepSeek V4 Flash vs V4 Pro: Which Tier to Pick](https://glm5.app/blog/deepseek-v4-flash-vs-deepseek-v4-pro): 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.
- [DeepSeek V4 Flash Pricing: Full Cost Breakdown (2026)](https://glm5.app/blog/deepseek-v4-flash-pricing): 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.
- [DeepSeek V4 Flash API: Key, Endpoint, and Python Quickstart](https://glm5.app/blog/deepseek-v4-flash-api): 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.
- [DeepSeek V4 Flash vs GPT-5 Mini: Cheap, Fast Model Compared](https://glm5.app/blog/deepseek-v4-flash-vs-gpt-5-mini): 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.
- [DeepSeek V4 Flash vs Gemini 2.5 Flash: Open vs Hosted](https://glm5.app/blog/deepseek-v4-flash-vs-gemini-2-5-flash): 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.
- [DeepSeek V4 Flash Benchmarks: Speed and Scores Decoded](https://glm5.app/blog/deepseek-v4-flash-benchmarks): 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.
- [DeepSeek V4 Flash Context Window: What 1M Tokens Really Means](https://glm5.app/blog/deepseek-v4-flash-context-window): 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.
- [DeepSeek V4 Flash Alternatives: 8 Options Ranked (2026)](https://glm5.app/blog/deepseek-v4-flash-alternatives): 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.
- [What Is OpenAI Astra? The $2,000 Math Breakthrough Explained](https://glm5.app/blog/what-is-openai-astra): 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.
- [GLM 5.5: Release Status, What We Know, and Specs to Expect (2026)](https://glm5.app/blog/glm-5-5): GLM 5.5 has not been released — as of August 2026, Z.AI has made zero official announcements. Verified status across official channels, the GLM release rhythm, and labeled speculation on what 5.5 could bring.
- [What Is DeepSeek V4 Pro? The 1.6T Reasoning Flagship, Explained](https://glm5.app/blog/what-is-deepseek-v4-pro): DeepSeek V4 Pro explained: the 1.6T/49B MoE reasoning flagship, the 0813 release, 1M-token context, official pricing, and a decision framework vs V4 Flash.
- [DeepSeek V4 Pro Pricing: Full Cost Breakdown (2026)](https://glm5.app/blog/deepseek-v4-pro-pricing): DeepSeek V4 Pro pricing broken down: $0.435/$0.87 per 1M tokens, cache-hit input at $0.003625, real workload cost scenarios, and the official price-increase notice.
- [DeepSeek V4 Pro API: Key, Endpoint, and Python Quickstart](https://glm5.app/blog/deepseek-v4-pro-api): Call the DeepSeek V4 Pro API in minutes: get a key at platform.deepseek.com, hit the OpenAI-compatible endpoint, run Python and curl examples, stream, and toggle thinking mode.
- [DeepSeek V4 Pro Benchmarks: 0813 Scores and Speed Decoded](https://glm5.app/blog/deepseek-v4-pro-benchmarks): DeepSeek V4 Pro benchmarks decoded from Artificial Analysis: Intelligence Index 53 (#2/104), 83.2 tokens/s, 1.63s TTFT, verbosity, and how to read the scores.
- [DeepSeek V4 Pro Context Window: What 1M Tokens Really Means](https://glm5.app/blog/deepseek-v4-pro-context-window): DeepSeek V4 Pro's 1M-token context window explained: what 1,048,576 tokens hold in pages, input vs 384K max output, real use cases, and KV-cache cost engineering.
- [DeepSeek V4 Pro vs GPT-5: Open-Weights Flagship or OpenAI Best?](https://glm5.app/blog/deepseek-v4-pro-vs-gpt-5): DeepSeek V4 Pro vs GPT-5 compared on architecture, openness, price, context, and reasoning, with a scenario-based decision framework and honest trade-offs.
- [DeepSeek V4 Pro vs Claude Opus 4.8: Open Weights vs Agentic Powerhouse](https://glm5.app/blog/deepseek-v4-pro-vs-claude-opus-4-8): DeepSeek V4 Pro vs Claude Opus 4.8 compared: architecture, pricing, coding and agentic depth, self-hosting reality, and which flagship fits which team.
- [DeepSeek V4 Pro vs Gemini 2.5 Pro: Open Weights vs Google's Multi-Modal Flagship](https://glm5.app/blog/deepseek-v4-pro-vs-gemini-2-5-pro): DeepSeek V4 Pro vs Gemini 2.5 Pro compared on price, context, modalities, reasoning and ecosystem. One question decides the comparison: do you need image input?
- [DeepSeek V4 Pro for Coding: A Practical Guide for Developers](https://glm5.app/blog/deepseek-v4-pro-for-coding): How to use DeepSeek V4 Pro for coding: thinking-mode configuration, tool calling and JSON mode, 1M-token repository analysis, cost control, and honest limits.
- [DeepSeek V4 Pro Alternatives: 7 Flagship Options Ranked (2026)](https://glm5.app/blog/deepseek-v4-pro-alternatives): 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, Qwen 3.

- [What Is GLM 5.3? The Coding-First Release That Skips a New Base Model](https://glm5.app/blog/what-is-glm-5-3) — 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.

- [How to Use GLM 5.3: API Setup, Thinking Parameters & Coding Plan](https://glm5.app/blog/how-to-use-glm-5-3) — 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.

- [GLM 5.3 vs GLM 5.2: What's Actually New (Benchmarks Included)](https://glm5.app/blog/glm-5-3-vs-glm-5-2) — 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.

- [GLM 5.3 Pricing: API Cost, Token Rates & Coding Plan (2026)](https://glm5.app/blog/glm-5-3-pricing) — 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.

- [GLM 5.3 Open Weights: Release Date & What Self-Hosting Gets You](https://glm5.app/blog/glm-5-3-open-weights) — 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.

- [GLM 5.3 Benchmarks: Full Scores vs GLM 5.2, Kimi K3, GPT-5.6 Sol & More](https://glm5.app/blog/glm-5-3-benchmarks) — 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.

- [GLM 5.3 Coding Plan: Prices, Credits, Limits & Best Tier](https://glm5.app/blog/glm-5-3-coding-plan) — 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.

- [GLM 5.3 Weights: Release Date, Specs & What's Inside](https://glm5.app/blog/glm-5-3-weights) — 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.

- [GLM 5.3 vs GPT-5.6 Sol: Benchmark Battle & Which to Choose](https://glm5.app/blog/glm-5-3-vs-sol) — 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.

- [GLM 5.3 vs Claude Opus 5: Which Coding Model Wins?](https://glm5.app/blog/glm-5-3-vs-opus-5) — 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.

- [GLM 5.3 on OpenRouter: Availability, Model ID & Setup](https://glm5.app/blog/glm-5-3-openrouter) — 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.

- [Glm5.3 API](https://glm5.app/blog/glm5-3-api) — 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.

- [GLM 5.3 Release Date](https://glm5.app/blog/glm-5-3-release-date) — 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.

- [GLM 5.3 Free](https://glm5.app/blog/glm-5-3-free) — 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.

- [How To Use Glm AI: Step-by-Step Guide](https://glm5.app/blog/how-to-use-glm-ai) — 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.

- [How To Use Glm 5.2 App: Step-by-Step Guide](https://glm5.app/blog/how-to-use-glm-5-2-app) — 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.

- [GLM 5.3 on AI Leaderboards: Where It Ranks (AA, Terminal-Bench, CyberGym)](https://glm5.app/blog/glm-5-3-ai-leaderboard) — 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.

- [How to Use Ox Alpha: A Practical First-Task Workflow](https://glm5.app/blog/how-to-use-ox-alpha) — How to use Ox Alpha: a practical first-task workflow covering reasoning effort, 1M-token context, tools and structured output, plus repeatable mini-evaluation.

- [Ox Alpha Vs Fable 5 — Ox Alpha vs Fable 5 decision](https://glm5.app/blog/ox-alpha-vs-fable-5) — 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.