GLM / Z.ai (Zhipu AI)
A Tier · 8.0/10
Zhipu AI's open-weights family -- GLM-5.1 (launched 2026-04-07) is 744B MoE / 40B active, topped SWE-Bench Pro at 58.4 (beating GPT-5.4 and Claude Opus 4.6), MIT licensed, 200K context. Trained entirely on 100K Huawei Ascend 910B chips -- first frontier model with zero Nvidia in the training stack
Score Breakdown
Benchmark Scores
Benchmarks for GLM-5.1 (744B MoE / 40B active)
| Benchmark | Description | Score | |
|---|---|---|---|
| SWE-Bench Pro | 58.4% | ||
| MMLU-Pro | Harder multi-subject reasoning | 81.2% | |
| GPQA Diamond | Graduate-level science questions | 74.5% | |
| HumanEval | Python code generation | 89.1% | |
| SWE-Bench Verified | 64.2% | ||
| BFCL (function calling) | 88% |
Last updated: 2026-04-17
Personality & Tone
The Z.ai research model
Tone: Academic and structured. GLM-4.6's instruction-tuned chat tends toward outlined, bullet-heavy responses and leans on established phrasing rather than casual voice.
Quirks: Strong on multilingual and tool use, weaker at playful conversation. Smaller community fine-tuning ecosystem than Llama or Qwen, so fewer 'flavored' checkpoints to pick from -- most deployments run the base instruction-tune.
The Good and the Bad
What we like
- +GLM-5.1 (2026-04-07) topped SWE-Bench Pro at 58.4 -- beating GPT-5.4, Claude Opus 4.6, and every other open-weight model on that benchmark. The result is externally verified and is the strongest agentic-coding signal from any Chinese open-weight model in 2026
- +First frontier model trained entirely on 100,000 Huawei Ascend 910B chips with zero Nvidia in the training stack -- a genuine proof point that non-Nvidia training pipelines can reach frontier quality, with big implications for US-China compute strategy
- +True MIT license -- one of the few frontier-tier open-weights models with zero commercial restrictions
- +GLM-5.1 is SOTA among open models for agentic tool-use and function calling; GLM-4.6V is #1 open-source on MMBench, MathVista, OCRBench among multimodal models
- +200K context window handles long documents reliably. Strong Chinese + English performance (unlike DeepSeek which is English-biased)
What could be better
- −Smaller Western community than Qwen or DeepSeek -- fewer tutorials, quants, fine-tunes
- −English tone is noticeably more stilted than Claude or Mistral for creative writing
- −PRC content filters apply to politically sensitive topics
- −Ollama support lags behind Qwen/Llama/Mistral release cycles
Pricing
Self-hosted (Free)
- ✓MIT license -- truly open, no MAU clauses
- ✓Full weights on Hugging Face
- ✓Commercial use fully permitted
API (Z.ai / OpenRouter, GLM-5.1)
- ✓GLM-5.1 (launched 2026-04-07): 744B MoE / 40B active, $0.60 in / $2.20 out
- ✓GLM-4.6V (vision): tiered
- ✓200K context
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| GLM-5.1 (744B MoE / 40B active, launched 2026-04-07)MIT license. Trained entirely on Huawei Ascend 910B, not Nvidia | 256 GB RAM + 48 GB GPU (Q2 offload, production builds still landing) | 8× H100 FP8 or 4× H200 |
| GLM-4.6 (355B MoE, legacy)MIT license -- zero commercial restrictions | 128 GB RAM + 24 GB GPU (Q3 offload) | 4× H100 FP8 |
| GLM-4.6V (multimodal)Vision tower adds ~4 GB on top of base footprint | 128 GB RAM + 28 GB GPU (Q3 + vision tower) | 4× H100 FP8 |
| GLM-4-9B (small) | 6 GB VRAM (Q4) | 24 GB VRAM FP16 |
Known Issues
- GLM-4.6 requires specific tokenizer and chat template -- several community llama.cpp quants initially had broken tool-use until fixes landedSource: Hugging Face discussions, GitHub issues · 2026-03
- Refuses discussion of Tiananmen, Taiwan, Xi Jinping -- same PRC content filters as DeepSeek and QwenSource: Reddit r/LocalLLaMA · 2026-02
Best for
Teams that need genuine MIT-licensed frontier open weights with no commercial strings. Especially strong for agentic workflows and vision (GLM-4.6V).
Not for
Consumer-facing English content generation (Mistral or Claude write better), or ultra-low-resource deployment (use Gemma 4 or Phi-4 instead).
Our Verdict
GLM-4.6 is the most under-appreciated frontier open-weights model in 2026. The true MIT license puts it ahead of Llama 4 on licensing, and the agentic tool-use performance beats most of its open-weight peers. GLM-4.6V is legitimately the best open multimodal model on several benchmarks. The weakness is purely ecosystem: fewer Western fine-tunes and less Ollama coverage. If you're building an agent or multimodal product and want clean licensing, GLM is the pick.
Sources
- Winbuzzer: Z.ai releases GLM-5.1 754B tops SWE-Bench Pro (accessed 2026-04-17)
- TestingCatalog: Zhipu AI launches GLM-5.1 open-source model for coding (accessed 2026-04-17)
- Z.ai blog: GLM-4.6 and GLM-4.6V (accessed 2026-04-17)
- Hugging Face THUDM collection (accessed 2026-04-17)
- Artificial Analysis open-weights leaderboard (accessed 2026-04-17)
- OpenRouter pricing (accessed 2026-04-17)
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