GLM 5.1
Z.AI's open-weights MoE flagship for agentic coding and long-horizon reasoning, runnable privately on Venice.
Overview
What is GLM 5.1
GLM 5.1 is Z.AI's open-weights flagship text model for agentic engineering and long-horizon coding tasks. Released in March 2026, it uses a 744B-parameter MoE architecture with 40-44B active parameters per token, supports reasoning and web search, and is designed to sustain autonomous execution over extended sessions.
Running it privately on Venice
On Venice, GLM 5.1 runs inside a TEE with end-to-end encryption and zero retention — your prompts are not stored or profiled. You get the full open-weights model with its reasoning and web-search capabilities, permissionless and privately, without Big-Tech surveillance.
Assessment
Strengths and limitations
- Open-weights MIT-licensed MoE flagship with 744B parameters, enabling self-hosting, auditability, and model sovereignty.
- Built for long-horizon agentic coding and engineering tasks — sustains optimization over hundreds of rounds and thousands of tool calls.
- Strong reasoning and web-search capabilities on Venice for autonomous research, planning, and current-information retrieval.
- Efficient inference despite massive scale: only 40-44B parameters activate per token, keeping frontier-class performance cost-effective.
- Achieves open-source SOTA on SWE-Bench Pro and competitive alignment with Claude Opus 4.6 on coding benchmarks.
- Runs privately on Venice inside a TEE with end-to-end encryption and zero retention.
- Not fully uncensored: Venice hosts the open weights, but the model retains safety alignment from its original training.
- Long-horizon autonomy requires careful scaffolding; it is not plug-and-play for all agentic workflows.
- Massive 744B parameter count demands significant VRAM for self-hosting, even with MoE sparsity.
- Some benchmark claims (e.g., 8-hour autonomous runs) reflect idealized conditions rather than typical user experience.
- Vision and multimodal input are not supported on the Venice text endpoint; for image tasks consider a dedicated vision model.
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
- Released
- March 2026
- Architecture
- Mixture-of-Experts (MoE), 256 experts, 8 active per token
- Parameters
- 744B total, 40-44B active per token
- Open weights
- Yes — MIT license
- Context window
- 200K tokens
- Max output
- 32.768K tokens
- Capabilities
- Reasoning, Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Apr 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-5-1",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $1.10 per 1M input tokens and $4.15 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 | Strongest at | Open weights | Price (Venice) |
|---|---|---|---|---|
| GLM 5.1 | 200K tokens | Long-horizon agentic coding | Yes | $1.54 in · $4.84 out / 1M |
| DeepSeek V3.2 | 160K tokens | General reasoning / value | Yes | $0.33 in · $0.48 out / 1M |
| Kimi K2.6 | 256K tokens | Long-context open reasoning | Yes | $0.75 in · $3.50 out / 1M |
| Claude Opus 4.8 | 1M tokens | Closed-frontier coding | No | $6 in · $30 out / 1M |
The open-weights MoE flagship built for sustained autonomous engineering and reasoning.
Use cases
What it is good for
- 01Autonomous coding agents and long-horizon software engineering loops that iterate over many turns.
- 02Repo generation, complex debugging, and real-world terminal tasks over extended sessions.
- 03Private research and analysis where zero retention, open-source auditability, and data sovereignty matter.
- 04Tool-heavy agent workflows that benefit from structured JSON output and external data integration.
- 05Cost-efficient frontier-class reasoning via MoE architecture for startups and lean teams.
Prompting
Getting better results
Frame tasks as iterative engineering loops — the model improves with extended reasoning and repeated execution.
Use structured output formats like JSON when integrating with external data sources or building pipelines.
For long sessions, rely on the model's intelligent caching mechanism to maintain context across thousands of turns.
Be explicit about engineering standards and multi-platform constraints to leverage its agentic coding strengths.
Version history
Predecessor with strong initial coding capabilities.
Current — optimized for long-horizon agentic engineering and sustained execution.
FAQ
Frequently asked questions
GLM 5.1 is Z.AI's open-weights flagship text model released in March 2026. It is built on a 744B-parameter MoE architecture and is optimized for long-horizon agentic coding, reasoning, and autonomous engineering tasks that require sustained execution.
On Venice, GLM 5.1 costs $1.10 per 1 million input tokens and $4.15 per 1 million output tokens. You pay per token with credits and there is no subscription required.
The model weights are open-source under an MIT license on Hugging Face, so you can download and self-host them. Inference through Venice is billed per token and is not free.
Yes. On Venice, GLM 5.1 supports both reasoning and web search, allowing it to retrieve current information and ground its responses.
GLM 5.1 matches or exceeds Claude Opus 4.6 on many coding benchmarks and is significantly cheaper, while Opus 4.8 offers a 1M-token context and closed-frontier polish. Choose GLM 5.1 for open-weights sovereignty and cost; choose Opus 4.8 if you need the longest context and a proprietary model.
It runs in a private tier with zero retention, inside a trusted execution environment with end-to-end encryption. Your prompts are not stored, profiled, or used for training.
GLM 5.1 supports up to 200,000 tokens of context and can generate up to 32,768 tokens in a single response on Venice.
Yes. The weights are available on Hugging Face under an MIT license. However, the full 744B MoE requires substantial GPU resources, so most users will find Venice's API more practical.
Run GLM 5.1 privately
No prompt logging. No data used for training.