GLM 5.1
Z.AI's open-weights flagship LLM for agentic engineering and long-horizon coding tasks.
Overview
What is GLM 5.1
GLM 5.1 is Z.AI's open-weights flagship LLM for agentic engineering and long-horizon software tasks. Released in April 2026 under the MIT license, it is a 744B-parameter Mixture-of-Experts text model with tool use, reasoning, and web search capabilities, designed to sustain autonomous execution over extended sessions with iterative refinement.
Running it privately on Venice
On Venice, GLM 5.1 runs with zero retention — your prompts are not stored, profiled, or used for training. You get the full open-weights model with tool use, reasoning, and web search capabilities, plus structured JSON output, all processed privately without Big-Tech surveillance. It is available at $1.54 per 1M input tokens and $4.84 per 1M output tokens.
Assessment
Strengths and limitations
- Open-weights under MIT license, enabling self-hosting, fine-tuning, and full model sovereignty.
- Built for long-horizon agentic tasks: iteratively refines strategy over extended sessions with tool use and reasoning.
- State-of-the-art coding performance on SWE-Bench Pro and strong real-world engineering workflows.
- Supports function calling, reasoning, web search, and structured JSON output for flexible system integration.
- Runs privately on Venice with zero retention: no prompt storage or profiling.
- Not uncensored: content policies apply.
- MoE architecture requires significant VRAM for self-hosting the full 744B model, though active parameters are smaller.
- Benchmarks show it trails Claude Opus 4.6 on some reasoning tasks like GPQA Diamond and KernelBench L3.
- No end-to-end encryption or TEE execution on Venice (standard private tier).
- Long-horizon autonomy can mean higher token consumption and cost on extended runs.
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
- April 7, 2026
- Architecture
- Mixture-of-Experts (MoE)
- Parameters
- 744B total (40-44B active per token)
- Open weights
- Yes — MIT license
- Context window
- 200K tokens
- Max output
- 80K tokens
- Capabilities
- Function calling, 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": "zai-org-glm-5-1",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $1.54 per 1M input tokens and $4.84 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 | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| GLM 5.1 | Agentic coding & long-horizon tasks | 200K tokens | Yes | $1.54 in · $4.84 out / 1M |
| Claude Opus 4.8 | General reasoning & terminal tasks | 1M tokens | No | $6 in · $30 out / 1M |
| DeepSeek V3.2 | Efficient open-weights coding | 160K tokens | Yes | $0.33 in · $0.48 out / 1M |
| Kimi K2.6 | Long-context open-weights tasks | 256K tokens | Yes | $0.75 in · $3.50 out / 1M |
The leading open-weights choice for sustained agentic coding and iterative software engineering.
Use cases
What it is good for
- 01Autonomous coding agents and long-horizon software engineering (e.g., repo generation, debugging).
- 02Complex engineering optimization and multi-stage development workflows.
- 03Tool-augmented research assistants that leverage web search and function calling.
- 04Building private, self-hosted LLM infrastructure with open weights.
- 05Iterative agentic tasks requiring sustained execution and structured output.
Prompting
Getting better results
Use explicit planning prompts for long tasks — ask GLM 5.1 to break the problem into steps and revisit strategy periodically.
Leverage tool use and web search by clearly defining available functions and when to invoke them.
For coding, provide repository context and engineering standards upfront to improve long-horizon adherence.
Use structured JSON output mode for system integration and automated pipelines.
Iterate on ambiguous problems by asking the model to run experiments, read results, and identify blockers.
Version history
Predecessor base model with shorter agentic horizon.
Current — open-weights flagship with sustained long-horizon execution and advanced coding.
FAQ
Frequently asked questions
GLM 5.1 is Z.AI's open-weights flagship LLM released in April 2026. It is a 744B-parameter Mixture-of-Experts model designed for agentic engineering, long-horizon coding, and autonomous task execution with tool use, reasoning, and web search.
On Venice, GLM 5.1 costs $1.54 per 1M input tokens and $4.84 per 1M output tokens, with cached input at $0.29 per 1M. There is no subscription — you pay only for the tokens you consume.
Yes. GLM 5.1 is released under the MIT license with open weights available on Hugging Face, enabling self-hosting, fine-tuning, and full model sovereignty.
The open weights are free to download and self-host on your own hardware. On Venice, inference is billed per token with no subscription required, so you only pay for what you use.
GLM 5.1 leads for open-weights agentic coding, long-horizon execution, and cost efficiency. Claude Opus 4.8 offers a 1M-token context and stronger results on some reasoning and terminal benchmarks, but it is proprietary and significantly more expensive.
Yes. GLM 5.1 supports function calling, reasoning, web search, and structured JSON output, making it well suited for building autonomous agents and complex engineering workflows.
Yes. Because it is MIT-licensed open weights, you can download the model from Hugging Face and run it locally. Note that the full 744B MoE architecture requires substantial GPU memory, though active parameters per token are smaller.
Venice runs GLM 5.1 under its private tier with zero retention — prompts are not stored, profiled, or used for training. It does not currently run inside a TEE or with end-to-end encryption.
No. GLM 5.1 is not uncensored and operates under standard content policies. If you need an uncensored model, browse Venice's catalog for alternatives marked as unrestricted.
Run GLM 5.1 privately
No prompt logging. No data used for training.