TL;DR
GLM-5.1 from Zhipu AI is a 744B-parameter MoE model with 40-44B active parameters, a 200K context window, and MIT-licensed open weights. It reaches 77.8% on SWE-bench versus Claude Opus 4.6 at 80.8%, while costing $1.00/$3.20 per million input/output tokens versus Claude Opus 4.6 at $15.00/$75.00. For teams building coding agents or developer tooling where cost matters, GLM-5.1 is one of the strongest open-weight options to evaluate.
Why GLM-5.1 matters
GLM-5.1 was released by Zhipu AI on March 27, 2026. Its significance is not only benchmark performance.
Two implementation details matter for engineering teams:
- Open weights under the MIT license
- Training on 100,000 Huawei Ascend 910B chips, without Nvidia GPUs
If your team needs model customization, self-hosting options, or reduced dependency on a single hardware supply chain, those details are as important as benchmark numbers.
Model specifications
| Spec | GLM-5.1 |
|---|---|
| Parameters | 744B total, MoE |
| Active parameters per token | 40-44B |
| Expert architecture | 256 experts, 8 active per token |
| Context window | 200K tokens |
| Max output | 131,072 tokens |
| Training data | 28.5 trillion tokens |
| Training hardware | 100,000 Huawei Ascend 910B |
| License | MIT, open weights |
GLM-5.1 uses a Mixture-of-Experts architecture. That means the model has 744B total parameters, but only 40-44B are active for each token. In practice, this gives the model large total capacity while keeping inference more efficient than activating the full model on every token.
Benchmark comparison
Reasoning and knowledge
| Benchmark | GLM-5 / GLM-5.1 baseline | Claude Opus 4.6 | Notes |
|---|---|---|---|
| AIME 2025 | 92.7% | ~88% | GLM-5 leads |
| GPQA Diamond | 86.0% | 91.3% | Claude leads |
| MMLU | 88-92% | ~90%+ | Comparable |
Coding
| Benchmark | GLM-5.1 | Claude Opus 4.6 |
|---|---|---|
| SWE-bench | 77.8% | 80.8% |
| LiveCodeBench | 52.0% | Higher |
GLM-5.1 reaches 77.8% on SWE-bench, which is 3 percentage points behind Claude Opus 4.6. The reported 28% coding improvement from GLM-5 to GLM-5.1 came from post-training refinement rather than architectural changes.
Human preference
On LMArena, GLM-5 ranks #1 among open-weight models in both Text and Code arenas. Among all models, it is competitive with top closed models.
Pricing comparison
| Model | Input, per 1M tokens | Output, per 1M tokens |
|---|---|---|
| GLM-5.1 | $1.00 | $3.20 |
| DeepSeek V3.2 | $0.27 | $1.10 |
| Claude Sonnet 4.6 | $3.00 | $15.00 |
| GPT-5.2 | $3.00 | $12.00 |
| Claude Opus 4.6 | $15.00 | $75.00 |
| Gemini 2.5 Pro | $1.25 | $10.00 |
GLM-5.1 delivers approximately 94.6% of Claude Opus 4.6’s coding performance at 1/15 the input-token cost, based on Zhipu AI’s internal claims. Independent verification is still pending for that exact 94.6% figure.
For production coding agents, this price gap matters. If you run large volumes of code generation, refactoring, test generation, or repo analysis, token cost can become a major part of your infrastructure budget.
Open-weights tradeoffs
GLM-5.1 is designed as an open-weight model under the MIT license. That gives teams the option to:
- Self-host the model
- Fine-tune on domain-specific data
- Control data handling and infrastructure
- Modify the model or post-training pipeline for internal use cases
- Use the model commercially under MIT license terms
The practical constraint is infrastructure. Full BF16 storage requires approximately 1.49TB, and running a 744B-parameter MoE model requires substantial compute. For most engineering teams, API access is the more practical starting point.
Limitations to account for
Before using GLM-5.1 in production, account for these constraints.
Text-only input
GLM-5.1 processes text only. It does not support image, audio, or video understanding. For multimodal workflows, models like GPT-5.2 or Gemini 2.5 Pro are better fits.
Benchmark independence
Some coding benchmark results use Claude Code as the evaluation framework. Independent verification on non-Claude evaluation infrastructure is still pending.
GLM-5.1 weights availability
As of publication, only GLM-5 weights are public. GLM-5.1 is available via API, but the GLM-5.1 weights have not yet been released.
Self-hosting requirements
The model requires approximately 1.49TB for full BF16 storage. Self-hosting is possible, but only realistic if your team already has significant model-serving infrastructure.
How to test GLM-5.1 with Apidog
A practical way to compare GLM-5.1 with Claude Opus 4.6 is to send the same coding task to both APIs and evaluate the output.
Use the same prompt, same max token budget, and similar temperature settings.
Request to GLM-5.1 via WaveSpeedAI
POST https://api.wavespeed.ai/api/v1/chat/completions
Authorization: Bearer {{WAVESPEED_API_KEY}}
Content-Type: application/json
{
"model": "glm-5",
"messages": [
{
"role": "user",
"content": "{{coding_task}}"
}
],
"temperature": 0.2,
"max_tokens": 4096
}
Request to Claude Opus 4.6
POST https://api.anthropic.com/v1/messages
x-api-key: {{ANTHROPIC_API_KEY}}
anthropic-version: 2023-06-01
Content-Type: application/json
{
"model": "claude-opus-4-6",
"max_tokens": 4096,
"messages": [
{
"role": "user",
"content": "{{coding_task}}"
}
]
}
Suggested test prompt
Use a concrete implementation task instead of a vague benchmark prompt:
You are working in a Node.js TypeScript API.
Implement a rate limiter middleware with:
- Redis-backed counters
- Sliding window behavior
- Per-user limits when userId exists
- IP-based fallback when userId is missing
- Unit tests for allowed, blocked, and reset-window behavior
Return production-ready code.
Compare the outputs
Evaluate both responses against the same checklist:
- Does the code run?
- Are edge cases handled?
- Is the implementation readable?
- Are tests included and meaningful?
- Are dependencies clearly stated?
- Is the response concise?
- How many tokens were used?
At $1.00/$3.20 versus $15.00/$75.00 per million input/output tokens, the same coding task can cost roughly 20-25x more on Claude Opus 4.6 depending on prompt and output length.
When to use GLM-5.1
GLM-5.1 is a strong fit if you are building:
- Coding agents
- Test generation tools
- Refactoring assistants
- Internal developer copilots
- Long-context codebase analysis workflows
- Compliance-sensitive applications that benefit from open weights
- Multilingual or China-market developer products
It is especially worth testing when you need frontier-adjacent coding performance but cannot justify premium closed-model pricing for every request.
When to choose another model
Use another model if your primary requirement is different:
| Requirement | Better option |
|---|---|
| Multimodal input | GPT-5.2 or Gemini 2.5 Pro |
| Maximum reasoning quality regardless of cost | Claude Opus 4.6 |
| Lowest possible token cost | DeepSeek V3.2 |
| Full current self-hosting of released weights | GLM-5, until GLM-5.1 weights are released |
FAQ
Is GLM-5.1 available through an OpenAI-compatible API?
GLM models use an API format compatible with common SDK patterns. Check Zhipu AI’s current documentation for exact endpoint details.
Why is Huawei hardware training significant?
Most frontier models are trained on Nvidia A100 or H100 clusters. GLM-5.1 showing frontier-adjacent performance on Huawei Ascend hardware indicates that competitive large-model training is possible outside Nvidia-based infrastructure.
Does the MIT license allow commercial use?
Yes. The MIT license allows commercial use, modification, and distribution. It is more permissive than many frontier-model licenses.
How does GLM-5.1 compare with other open-weight models?
GLM-5 ranks #1 on LMArena among open-weight models, ahead of Llama, Qwen, and other open alternatives.
What is the 200K context window useful for?
A 200K-token context window can hold roughly 150,000 words. That is enough for large documents, multi-file code reviews, long technical specs, or substantial parts of a codebase. For many long-context developer workflows, that is sufficient without chunking.
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