LLMPrivate

GLM 4.7

Z.AI's open-weight coding and reasoning model with multi-mode thinking, MIT-licensed weights, and strong agentic performance.

Get API key

What is GLM 4.7?

GLM 4.7 is Z.AI's open-weight language model released in December 2025, built on the GLM architecture with 358 billion parameters. It specializes in agentic coding, web search, and complex reasoning via multiple thinking modes, delivering strong scores on SWE-bench and terminal automation benchmarks.

Use GLM 4.7 privately on Venice

On Venice, GLM 4.7 runs inside a TEE with end-to-end encryption and zero retention — your prompts are not stored or profiled. You get open-weight sovereignty with private inference, plus built-in reasoning, web search, and code-optimized output without Big-Tech surveillance.

Private (zero retention)
No prompt training
TEE · hardware enclave
End-to-end encrypted

What can GLM 4.7 do?

Strengths
  • Open weights under MIT license — full sovereignty to self-host, audit, or fine-tune.
  • Strong coding performance with improvements on SWE-bench and terminal tasks, plus code-optimized output on Venice.
  • Multi-mode reasoning for stable complex task execution.
  • Built-in web search support for grounded, autonomous workflows on Venice.
  • Better UI and front-end generation than its predecessor, with cleaner layouts and modern design.
Limitations
  • Not uncensored — safety filters apply, which may limit certain sensitive or controversial prompts.
  • Incremental upgrade over GLM-4.6 rather than a leap; for cutting-edge coding SOTA, GLM-5.1 is now Z.AI's flagship.
  • Large parameter count demands significant VRAM for local self-hosting compared to smaller open models.
  • Benchmark coverage is mixed — some categories remain thinly measured by independent third parties.

GLM 4.7 capabilities

How to use GLM 4.7 via API

Venice exposes an OpenAI-compatible API. Swap your base URL and call e2ee-glm-4-7-p.

curl https://api.venice.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "e2ee-glm-4-7-p",
    "messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
  }'

Specifications

MakerZ.AI
ReleasedDecember 22, 2025
ArchitectureTransformer-based GLM (General Language Model)
Parameters358 billion
Open weightsYes — MIT license
InputText
Context window128K tokens
Max output32.768K tokens
CapabilitiesReasoning, Web search, Code-optimized
Privacy on VenicePrivate — zero retention
Available on Venice sinceMar 2026

Pricing

Billed per token on Venice: $1.10 per 1M input tokens and $4.15 per 1M output tokens.

Input / 1M tokens
$1.10
Output / 1M tokens
$4.15

New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.

GLM 4.7 vs alternatives

ModelContext windowOpen weightsPrice (Venice)Best for
GLM 4.7128K tokensYes$1.10 in · $4.15 out / 1MAgentic coding & reasoning
GLM 5.1200K tokensYes$1.10 in · $4.15 out / 1MLatest GLM flagship
DeepSeek V3.2160K tokensYes$0.33 in · $0.48 out / 1MBudget reasoning
Claude Sonnet 4.61M tokensNo$3.60 in · $18 out / 1MClosed high-capacity
Kimi K31M tokensYes$3.75 in · $18.75 out / 1MLong-context open

The balanced open-weight choice for coding and reasoning with private TEE inference.

What is GLM 4.7 good for?

  • Agentic software development with autonomous requirement decomposition and multi-stack integration.
  • Terminal-based automation and shell scripting in multiple languages.
  • Conversational UI generation, webpage building, and slide design.
  • Complex mathematical reasoning and step-by-step problem solving.
  • Research workflows augmented with web search and structured data extraction.

Prompting tips

  • Break complex coding tasks into discrete steps — GLM-4.7 handles multi-file and multi-stack integration better with explicit decomposition.
  • Describe the full task context up front — it excels at end-to-end task completion rather than single-shot snippets.
  • For coding agents, specify the framework to align invocation patterns with Cline, Roo Code, or Claude Code.
  • Request structured JSON output explicitly when integrating the model into external pipelines.

Version history

GLM-4.6
2025

Predecessor with lighter coding and reasoning capabilities.

GLM-4.7
2025-12

CurrentCurrent — improved agentic coding, web search, and multi-mode reasoning.

Frequently asked questions

GLM 4.7 is Z.AI's open-weight language model released in December 2025. It is optimized for agentic coding, complex reasoning, and web search, and is licensed under MIT for self-hosting or fine-tuning.

Venice bills GLM 4.7 at $1.10 per 1 million input tokens and $4.15 per 1 million output tokens, with no subscription required.

Yes. GLM 4.7 ships with open weights under the MIT license, so you can self-host, audit, or fine-tune it. The weights are available on Hugging Face.

You can try it on Venice with free-tier credits; ongoing use is billed per token. Self-hosting the open weights is free of licensing fees, though you pay for your own compute.

GLM 4.7 is a proven, cost-efficient workhorse for coding and reasoning. GLM 5.1 offers a larger 200K context and stronger SOTA performance on complex software engineering tasks. Many teams route simple tasks to 4.7 and hard tasks to 5.1.

Yes. On Venice, GLM 4.7 supports web search alongside reasoning and code-optimized output, enabling grounded research and complex coding workflows without leaving the chat.

No. GLM 4.7 is not uncensored and includes safety filters. For fully uncensored open-weight inference, you would need to use a different model or fine-tune the weights yourself.

On Venice, GLM 4.7 runs in a TEE with end-to-end encryption and zero retention — your prompts are not stored, profiled, or used for training. Alternatively, download the MIT-licensed weights and self-host for complete data sovereignty.

Related models

Run GLM 4.7 privately.

No prompt logging. No data used for training. Free to start — no credit card.

Room