TL;DR
GLM-5.1 (744B MoE, 40-44B active parameters, MIT license) reaches 77.8% on SWE-bench versus Claude Opus 4.6’s 80.8%. Costs $1.00/$3.20 per million tokens versus Claude Opus 4.6 at $15.00/$75.00. It’s the most capable open-weights model in 2026, trained entirely on Huawei hardware without Nvidia GPUs. For cost-conscious teams that need frontier-adjacent coding performance, GLM-5.1 is the strongest open option.
Introduction
GLM-5.1 from Zhipu AI (released March 27, 2026) is significant for two reasons beyond raw benchmark performance: it’s open-weights under an MIT license, and it was trained on 100,000 Huawei Ascend 910B chips — no Nvidia hardware involved.
For organizations concerned about supply chain dependencies or requiring model customization, these factors matter as much as benchmark scores.
Specifications
| Spec | GLM-5.1 |
|---|---|
| Parameters | 744B total (MoE) |
| Active 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) |
The 744B total versus 40-44B active parameter structure is characteristic of MoE architecture: the model is large in total capacity but efficient per inference because only a fraction of parameters activate for each token.
Benchmark comparison
Reasoning and knowledge
| Benchmark | GLM-5 (5.1 baseline) | Claude Opus 4.6 | Notes |
|---|---|---|---|
| AIME 2025 | 92.7% | ~88% | GLM-5 outperforms |
| 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 — 3 points behind Claude Opus 4.6 but significantly ahead of GPT-5, Gemini, and DeepSeek on this specific benchmark. The 28% coding improvement from GLM-5 to 5.1 came through post-training refinement rather than architectural changes.
Human preference (LMArena)
GLM-5 ranks #1 among open-weights models on LMArena for both Text and Code arenas. Among all models, it’s 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 cost (based on Zhipu AI’s internal claims; independent verification pending for the 94.6% figure specifically).
For teams running production coding agents at scale, this cost difference changes the economics significantly.
The open-weights advantage
GLM-5.1 is available on Hugging Face under the MIT license. Teams can:
- Download and self-host (requires ~1.49TB for full BF16)
- Fine-tune on domain-specific data
- Deploy with full control over data handling and infrastructure
- Modify the model architecture or post-training for specific tasks
The 1.49TB storage requirement and GPU infrastructure for 744B parameters make full self-hosting expensive. For most teams, API access is more practical.
Limitations
Text-only: GLM-5.1 processes text input only. No image, audio, or video understanding. This limits use cases compared to multimodal models like GPT-5.2 and Gemini 2.5 Pro.
Benchmark independence: GLM-5.1’s coding benchmarks use Claude Code as the evaluation framework. Independent verification of the exact scores on non-Claude evaluation infrastructure is pending.
GLM-5.1 weights pending: Only GLM-5 weights are currently public. GLM-5.1 is available via API; the 5.1 weights have not been released as of publication.
Storage requirements: 1.49TB for self-hosting. Practical self-deployment requires substantial infrastructure investment.
Testing GLM-5.1 with Apidog
Via WaveSpeedAI (recommended for API access):
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
}
Compare with 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}}"}]
}
Use the same {{coding_task}} variable for both. Compare:
- Code correctness (does it work?)
- Code quality (is it readable and well-structured?)
- Response length (shorter = more focused)
- Token usage (check the response metadata)
At $1.00/$3.20 versus $15.00/$75.00, the same coding task costs approximately 20-25x more on Claude Opus 4.6.
Who should use GLM-5.1
Strong fit:
- Teams needing frontier coding performance at reduced cost
- Organizations requiring open-weights models for compliance or customization
- Developers building for Chinese market or multilingual use cases
- Research teams studying frontier-adjacent open models
Better alternatives exist:
- Multimodal use cases: GPT-5.2 or Gemini 2.5 Pro
- Maximum reasoning capability regardless of cost: Claude Opus 4.6
- Cheapest possible option: DeepSeek V3.2 at $0.27/$1.10
FAQ
Is GLM-5.1 available via an OpenAI-compatible API?
GLM models use an API format compatible with common SDKs. Check Zhipu AI’s current documentation for the exact endpoint format.
What makes the Huawei hardware training significant?
Most frontier models are trained on Nvidia A100/H100 clusters. GLM-5.1 demonstrating frontier-adjacent performance on Huawei Ascend hardware proves alternatives to Nvidia infrastructure are viable.
Does the MIT license allow commercial use?
Yes. MIT license permits commercial use, modification, and distribution. This is more permissive than the licenses on most other frontier models.
How does GLM-5.1 compare to the best open-source models?
GLM-5 ranks #1 on LMArena among open-weights models, ahead of Llama, Qwen, and other open alternatives.
What’s the 200K context window useful for?
200K tokens can hold approximately 150,000 words — a full book, a large codebase, or many documents simultaneously. For long-context applications like document analysis or large codebase review, this is sufficient for most practical use cases.
