GLM 5.2
Z.ai's open-weights MIT-licensed flagship for long-horizon coding and reasoning, with a 524K context, native tool use, and permissionless self-hosting.
Overview
What is GLM 5.2
GLM 5.2 is Z.ai's open-weights flagship text model, released in June 2026 under the MIT license. It handles long-horizon engineering tasks across a 524K-token context, offers advanced coding with flexible reasoning modes, and supports function calling, web search, and structured output.
Running it privately on Venice
On Venice, GLM 5.2 runs inside a trusted execution environment with end-to-end encryption and zero retention — your prompts are never stored or profiled. You get the same open-weights MIT-licensed model with native tool use, reasoning, and web search, but with full data sovereignty and no Big-Tech surveillance.
Assessment
Strengths and limitations
- Open-weights MIT license enables permissionless self-hosting, modification, and audit without regional restrictions.
- Built for long-horizon tasks: stable performance across 524K+ tokens of context for project-scale engineering and codebase understanding.
- Strong coding and reasoning capabilities with multiple thinking modes, plus competitive scores on SWE-bench Pro and Terminal-Bench.
- Native tool use, web search, and function calling for agentic workflows and structured JSON output.
- Runs inside Venice's TEE with end-to-end encryption and zero retention — your prompts are not stored or used for training.
- Not uncensored: safety filters apply, so it will decline certain requests.
- Self-hosting requires massive compute infrastructure given the reported ~753B parameter scale.
- Output pricing is higher than budget open rivals such as DeepSeek V3.2 or Kimi K2.6.
- Closed-weight flagships like Claude Opus 4.8 still lead on the hardest frontier coding tasks.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Z.ai (Zhipu AI)
- Open weights
- Yes — MIT license
- License
- MIT
- Context window
- 512K tokens
- Released
- June 2026
- Architecture
- Sparse attention with IndexShare; MoE reported
- Parameters
- ~753B (reported)
- Max output
- 32K tokens
- Capabilities
- Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Jun 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-glm-5-2-p",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $1.75 per 1M input tokens and $5.75 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Context window | Open weights | Price (Venice) | Best for |
|---|---|---|---|---|
| GLM 5.2 | 524K tokens | Yes | $1.75 in · $5.75 out / 1M | Long-horizon coding & reasoning |
| Claude Opus 4.8 | 1M tokens | No | $6 in · $30 out / 1M | Frontier coding & safety |
| DeepSeek V3.2 | 160K tokens | Yes | $0.33 in · $0.48 out / 1M | Efficiency & value |
| Kimi K2.6 | 256K tokens | Yes | $0.75 in · $3.50 out / 1M | Agentic workflows |
| GLM 5.1 | 200K tokens | Yes | $1.10 in · $4.15 out / 1M | Balanced open-source tasks |
Z.ai's latest open flagship with the longest context in the GLM family and native tool support.
Use cases
What it is good for
- 01Project-level codebase understanding and long-horizon software engineering across entire repositories.
- 02Agentic automation with tool calling, web search, and external MCP integrations.
- 03Structured data extraction and JSON output from large documents or long conversations.
- 04Local or private deployment where data sovereignty and zero retention are mandatory.
Prompting
Getting better results
Use the highest thinking-effort mode for complex architecture decisions; switch to lower latency for quick code reviews.
Feed entire project directories into context — the model retains module boundaries and API contracts across long sessions.
Leverage function calling to connect GLM-5.2 to external MCP tools and data sources for agentic workflows.
For long documents, rely on the intelligent caching mechanism to maintain coherence across multi-turn conversations.
Version history
Predecessor with 200K context and strong open-weight performance.
Current — 524K context, IndexShare, MIT license, tool use.
FAQ
Frequently asked questions
GLM 5.2 is Z.ai's open-weights flagship text model, released in June 2026 under the MIT license. It handles long-horizon engineering tasks across a 524K-token context, offers advanced coding with flexible reasoning modes, and supports function calling, web search, and structured output.
Venice bills GLM 5.2 at $1.75 per 1M input tokens and $5.75 per 1M output tokens. There is no subscription required; you pay per token with credits.
Yes. GLM 5.2 is released under the MIT license with open weights, meaning you can download, self-host, and modify it without regional restrictions.
Yes. GLM 5.2 supports function calling, reasoning, web search, and structured JSON output, making it well-suited for agentic workflows and external integrations.
No. GLM 5.2 is not uncensored and includes safety filters. For fully uncensored inference, choose a model explicitly labeled as uncensored on Venice.
Choose GLM 5.2 for open-weights flexibility, long-context project work, and lower cost. Choose Claude Opus 4.8 if you need the absolute frontier on hardest coding tasks and do not mind a closed, premium-priced model.
GLM 5.2 supports up to 524,288 tokens of context and up to 32,768 tokens of output in a single generation.
Venice runs GLM 5.2 in a private TEE with end-to-end encryption and zero retention. Your prompts are not stored, profiled, or used for training.
Run GLM 5.2 privately
No prompt logging. No data used for training.