zai-org/GLM-5.1
text-generation model by zai-org
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Model card & documentation
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GLM-5.1 is our next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks). But the most meaningful leap goes beyond first-pass performance. Previous models—including GLM-5—tend to exhaust their repertoire early: they apply familiar techniques for quick initial gains, then plateau. Giving them more time doesn't help. GLM-5.1, by contrast, is built to stay effective on agentic tasks over much longer horizons. We've found that the model handles ambiguous problems with better judgment and stays productive over longer…
Read the full model card ↗ · Preview checked 2026-10-29T07:06:45.483Z
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Model overview
zai-org/GLM-5.1 is a text-generation model repository published by zai-org on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- transformers
- Recent downloads (30 days)
- 1,040,024
- Cumulative likes
- 1,852
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 753,864,139,008
- Architecture
- GlmMoeDsaForCausalLM
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2026-04-03T09:28:47.000Z
- Last modified
- 2026-05-13T08:10:58.000Z
Compatibility and lineage
Reported languages: en, zh
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:29.042Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.