GLM 5 Blog
Blog
Read about our latest product features, solutions, and updates.
Claude Fable 5.1 Pricing: What the 25% Cheaper Claim Actually Means
Claude Fable 5.1 pricing: $10/$50 per 1M tokens, unchanged from Fable 5. Cache reads cut 75% to $0.25. Full rate card and three worked examples.
Colibri AI with GLM 5.2: Enhancing Real-Time AI Conversations
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 Flash Alternatives: 8 Options Ranked (2026)
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.
DeepSeek V4 Flash API: Key, Endpoint, and Python Quickstart
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 Benchmarks: Speed and Scores Decoded
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
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 Pricing: Full Cost Breakdown (2026)
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 vs V4 Pro: Which Tier to Pick
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 vs Gemini 2.5 Flash: Open vs Hosted
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 vs GLM 5.2: Cheap Speed or Depth?
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 GPT-5 Mini: Cheap, Fast Model Compared
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 Online: Try DeepSeek V4 in Your Browser on glm5.app
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.
DeepSeek V4 Pro Alternatives: 7 Flagship Options Ranked (2026)
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. With pricing, best-for picks, and honest trade-offs.
DeepSeek V4 Pro API: Key, Endpoint, and Python Quickstart
Connect to the DeepSeek V4 Pro API in 3 steps: get a key at platform.deepseek.com, point at the OpenAI-compatible endpoint, and run copy-paste Python and curl examples — including thinking mode.
DeepSeek V4 Pro Benchmarks: 0813 Scores and Speed Decoded
DeepSeek V4 Pro benchmarks explained: Intelligence Index 53 (#2/104), 83.2 tok/s, 1.63s TTFT — what the 0813 numbers mean, where they come from, and how to read them without being misled.
DeepSeek V4 Pro Context Window: What 1M Tokens Really Means
DeepSeek V4 Pro has a 1M-token context window. Here is what 1,048,576 tokens hold in pages and books, when long context pays off, how the 384K output cap fits, and what cached long-context calls really cost.
DeepSeek V4 Pro for Coding: A Practical Guide for Developers
How to use DeepSeek V4 Pro for coding: thinking-mode configuration, tool calling, 1M-token repository analysis, agent workflows, KV-cache cost control, and the honest limits.
DeepSeek V4 Pro Pricing: Full Cost Breakdown (2026)
DeepSeek V4 Pro pricing, fully broken down: $0.435/$0.87 per 1M tokens, cache-hit input at $0.003625 (-99%), real workload cost math, verbosity tax, and what the official price-increase notice means for your budget.
DeepSeek V4 Pro vs Claude Opus 4.8: Open Weights vs Agentic Powerhouse
DeepSeek V4 Pro vs Claude Opus 4.8 — compare architecture, pricing ($0.87 vs $75 per M output tokens), 1M context, reasoning, coding, and agentic capabilities. Plus a scenario-based decision framework and GLM 5.2 as a third option.
DeepSeek V4 Pro vs Gemini 2.5 Pro: Open Weights vs Google's Multi-Modal Flagship
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?
DeepSeek V4 Pro vs GPT-5: Open-Weights Flagship or OpenAI Best?
DeepSeek V4 Pro vs GPT-5 compared on price ($0.87 vs $10 per 1M output tokens), architecture, 1M context, and reasoning depth — with a scenario-based decision framework for 2026.
GLM 5.2 Agentic Workflows: Function Calling, Tool Use, and Multi-Step Tasks
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 Alternatives: 6 Open-Weight Coding Models
GLM 5.2 alternatives, ranked: 6 open-weight coding models from DeepSeek V4 to Kimi & MiniMax—how each compares, and when GLM 5.2 is still the best pick.
GLM 5.2 API: Endpoints, Authentication, and Python Integration Guide
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 Architecture: 753B Parameters, MoE Design, and How It Works
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 with AutoGen: Build Multi-Agent AI Workflows in Python
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 Batch Processing: High-Volume API Workflows
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 Benchmarks: How It Really Performs
GLM 5.2 benchmarks decoded: 62.1 on SWE-bench Pro, 74.4 on FrontierSWE—beating GPT-5.5 and chasing Opus 4.8 at a fraction of the cost. See what the scores mean.
GLM 5.2 Coding Plan: Pricing, Features, and Is It Worth It for Developers?
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 Context Window: What 1 Million Tokens Actually Means
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.