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GLM-4.7

From Zhipu AI (Z.ai)

Zhipu AI’s Flagship Open-Weight Model – Elite Agentic Coding, Complex Reasoning, and Tool Use Excellence
Tool Release Date

22 Dec 2025

Tool Users
N/A
0.0

Pricing Model

Starting Price

$3/Month

Visit GLM-4.7
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About This AI

GLM-4.7 is the latest flagship large language model from Zhipu AI (Z.ai), released on December 22, 2025, representing a major upgrade over GLM-4.6 with focus on advanced programming, stable multi-step reasoning, and agentic execution.

Built as an approximately 358B-400B MoE (Mixture-of-Experts) architecture, it features interleaved thinking (reasoning before responses/tool calls), preserved thinking (retaining reasoning across multi-turns), and turn-level thinking control for balancing latency and accuracy.

It excels in multilingual agentic coding, terminal tasks, UI/web development aesthetics, complex math/reasoning, and tool invocation, achieving top open-source results on key benchmarks like SWE-bench Verified (73.8%), SWE-bench Multilingual (66.7%), Terminal Bench 2.0 (41%), ฯ„ยฒ-Bench (87.4%), HLE with tools (42.8%), and strong math scores (95.7% AIME 2025).

Supports 200K token context window (up to 131K-128K output), high inference speed (55+ tokens/s), and deep integration with coding agents/tools like Claude Code, Kilo Code, Cline, Roo Code.

Available via Z.ai chat interface (free access with GLM-4.7 selection), API (usage-based pricing starting around $0.60/M input tokens), OpenRouter, Hugging Face/ModelScope weights for local deployment (vLLM/SGLang support), and coding-specific subscriptions starting at $3/month for enhanced quotas in dev tools.

Positioned as a top open-weight alternative for developers, researchers, and enterprises needing reliable coding assistance, agentic workflows, and high-quality reasoning without proprietary lock-in.

Key Features

  1. Interleaved Thinking: Reasons before every response and tool call for better instruction following and quality
  2. Preserved Thinking: Retains full reasoning chains across multi-turn conversations to reduce loss in long-horizon tasks
  3. Turn-level Thinking Control: Enable/disable reasoning per turn to optimize latency and cost
  4. Elite Agentic Coding: Strong multilingual, terminal-based, and multi-file software engineering performance
  5. Superior UI/Frontend Generation: Produces clean, modern webpages and slides with accurate layouts
  6. Advanced Tool Use: Top open-source scores on interactive tool invocation and web browsing benchmarks
  7. Complex Reasoning Boost: Major gains in math, science, and graduate-level questions
  8. 200K Token Context: Supports long documents, codebases, and extended conversations
  9. High Inference Efficiency: 55+ tokens per second with MoE design for fast responses
  10. Integration with Coding Agents: Native support in tools like Claude Code, Kilo Code, Cline, Roo Code

Price Plans

  1. Free Chat ($0): Basic access to GLM-4.7 via Z.ai chat interface with usage limits
  2. GLM Coding Plan ($3/Month starting): Enhanced quotas and integration in coding agents/tools like Claude Code, Cline
  3. API Usage-based ($0.60/M input, $2.20/M output tokens approx.): Pay-per-use via Z.ai API or OpenRouter for developers
  4. Pro/Enterprise (Custom): Higher limits, priority, dedicated support for heavy or business use

Pros

  1. Top-tier open-weight coding: Leads open models on SWE-bench, Terminal Bench, and agent benchmarks
  2. Stable long-horizon execution: Thinking mechanisms enable reliable multi-step agentic workflows
  3. Competitive reasoning: Strong math/science scores rivaling or beating many closed models
  4. Accessible deployment: Open weights on Hugging Face/ModelScope for local use, plus free chat/API options
  5. Cost-effective coding plans: Low-cost subscriptions unlock high quotas in dev tools
  6. Multilingual strength: Excellent agentic coding across languages
  7. Rapid inference: Balanced speed and quality for production use

Cons

  1. High parameter count: 358B-400B MoE requires substantial hardware for full local inference
  2. API pricing: Usage-based costs can add up for heavy users
  3. Coding plan separate: Full agent/tool quotas in third-party tools require additional subscription
  4. Limited free tier depth: Chat free access may have rate limits; advanced features paid
  5. Knowledge cutoff: Not explicitly stated but typical for 2025 releases (likely mid-2025)
  6. Setup for local: Requires vLLM/SGLang expertise and powerful GPUs
  7. No native multimodal: Focus on text/coding; vision/tool use strong but not emphasized

Use Cases

  1. Agentic software engineering: Multi-file code generation, debugging, and terminal automation
  2. Frontend/UI development: Creating modern webpages, slides, and visual prototypes
  3. Complex math/science reasoning: Solving advanced problems with step-by-step thinking
  4. Tool-using agents: Web browsing, interactive task execution, and workflow orchestration
  5. Code review and refactoring: Analyzing large codebases with long context
  6. Developer productivity: Integrating into IDEs or agents for real-time assistance
  7. Research prototyping: Testing multi-step reasoning and agent behaviors

Target Audience

  1. Software developers and engineers: Needing strong coding and agentic support
  2. AI researchers: Experimenting with frontier open-weight models
  3. Dev teams: Using in production for code generation and automation
  4. Students and educators: Learning advanced programming and reasoning
  5. Startups and enterprises: Cost-effective alternative to closed APIs for dev workflows
  6. Coding tool users: Subscribers to Claude Code, Cline, etc. wanting GLM-4.7 power

How To Use

  1. Chat interface: Visit chat.z.ai, select GLM-4.7 from model picker, start prompting
  2. API access: Sign up at z.ai, get API key, integrate via docs.z.ai/guides/llm/glm-4.7
  3. Local deployment: Download weights from Hugging Face (zai-org/GLM-4.7), run with vLLM/SGLang
  4. Coding agents: Subscribe to GLM Coding Plan, use in supported tools like Kilo Code or Cline
  5. Enable thinking: Prompt with 'think step-by-step' or use turn-level controls in API
  6. Long context: Upload large code/files or extend conversations up to 200K tokens
  7. Best results: Use detailed prompts, enable preserved thinking for multi-turn tasks

How we rated GLM-4.7

  • Performance: 4.8/5
  • Accuracy: 4.7/5
  • Features: 4.8/5
  • Cost-Efficiency: 4.6/5
  • Ease of Use: 4.5/5
  • Customization: 4.7/5
  • Data Privacy: 4.6/5
  • Support: 4.4/5
  • Integration: 4.7/5
  • Overall Score: 4.7/5

GLM-4.7 integration with other tools

  1. Z.ai Chat Interface: Direct selection of GLM-4.7 in chat.z.ai for instant use
  2. Z.ai API: Full programmatic access with thinking mode support for custom apps
  3. OpenRouter: Available on OpenRouter for easy integration with multiple models
  4. Coding Agents/Tools: Native support in Claude Code, Kilo Code, Cline, Roo Code, OpenCode
  5. Local Frameworks: vLLM and SGLang for high-performance self-hosted deployment

Best prompts optimised for GLM-4.7

  1. Act as a senior full-stack developer. Build a complete modern dark-mode responsive portfolio website in HTML/CSS/JS with animated sections and contact form. Think step-by-step, preserve reasoning across turns.
  2. Solve this graduate-level math problem from AIME 2025: [insert problem]. Use interleaved thinking, explain each step clearly, and verify answer.
  3. You are an expert agent. Use tools to research and summarize the latest advancements in quantum computing as of today, then generate a slide deck outline.
  4. Fix this buggy Python codebase for a web scraper: [paste code/files]. Identify issues, propose fixes, and output corrected version with explanations.
  5. Generate a professional business presentation slide deck on AI ethics in 2026, including key points, visuals suggestions, and speaker notes.
GLM-4.7 stands out as a top open-weight model with elite agentic coding, stable multi-step reasoning, and strong tool use that rivals closed leaders. Its thinking modes ensure reliable complex task execution, while open weights and affordable access make it ideal for developers and enterprises. Excellent choice for coding, math, and agent workflows.

FAQs

  • What is GLM-4.7?

    GLM-4.7 is Zhipu AI’s latest flagship open-weight LLM, released December 22, 2025, with major upgrades in agentic coding, reasoning, tool use, and UI generation via interleaved/preserved thinking modes.

  • When was GLM-4.7 released?

    It was officially released on December 22, 2025, with weights on Hugging Face and API/chat access shortly after.

  • Is GLM-4.7 open-source?

    Yes, open-weight with full model available on Hugging Face (zai-org/GLM-4.7) and ModelScope for local deployment under permissive license.

  • What are GLM-4.7’s key benchmarks?

    It achieves 73.8% on SWE-bench Verified, 66.7% SWE-bench Multilingual, 41% Terminal Bench 2.0, 87.4% ฯ„ยฒ-Bench, 42.8% HLE with tools, and strong math scores like 95.7% AIME 2025.

  • How to access GLM-4.7?

    Use free chat at chat.z.ai (select model), API via z.ai or OpenRouter (usage-based), or download weights for local run with vLLM/SGLang.

  • What is the context window for GLM-4.7?

    It supports 200,000 tokens context with up to 128K-131K output tokens, ideal for long codebases and multi-turn tasks.

  • Does GLM-4.7 require a subscription?

    Free chat access available; coding agents/tools need GLM Coding Plan from $3/month; API is pay-per-use starting around $0.60/M input tokens.

  • How does GLM-4.7 compare to competitors?

    It leads open models in many coding/agent benchmarks and competes closely with Claude Sonnet 4.5 and Gemini 3.0 Pro in reasoning/tool use, often at lower cost.

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