GLM-5.1
GLM-5.1 is a 753B-parameter open-source LLM from zai-org optimized for agentic tasks, coding, and tool use. It is distributed under the MIT license without gating restrictions. The model shows strong performance on software engineering benchmarks (SWE-Bench Pro: 58.4%) and extended reasoning tasks. Local deployment is supported via vLLM, SGLang, Transformers, and other frameworks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | zai-org |
| Parameters | 753.9B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 101.2k |
| Likes | 1.8k |
| Last updated | 2026-05-13 |
| Source | zai-org/GLM-5.1 |
What GLM-5.1 is
GLM-5.1 is a 753.9B-parameter mixture-of-experts model using the glm_moe_dsa architecture. It supports text generation with bilingual (English/Chinese) capabilities and is optimized for conversational and agentic use cases. The model can be served locally or via the Z.ai API platform. Context length is not specified. Last updated May 2026.
Run GLM-5.1 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="zai-org/GLM-5.1")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
753B parameters with unknown precision. Rough VRAM estimate: ~1.5TB in FP32 (infeasible for single node), ~750GB in FP16 (2–4× A100-80GB required), ~375–450GB with INT8 quantization (2× H100). Exact requirements depend on serving framework optimization (vLLM tensor parallelism, SGLang kernel fusions). Cluster deployment expected.
Not addressed in card. Model card does not mention LoRA, QLoRA, instruction tuning, or fine-tuning procedures. Recommend checking GitHub repository (zai-org/GLM-5) or contacting maintainers. Mixture-of-experts architectures often have specialized fine-tuning requirements.
When to avoid it — and what to weigh
- Strict context length requirements — Context length is not documented. If your use case requires known maximum sequence length guarantees, contact zai-org or review the technical report before deployment.
- Real-time inference with latency budgets <100ms — Model size (753B) requires significant compute. Local inference without quantization and optimization (SGLang/vLLM tuning) will incur multi-second latencies. Confirm inference SLAs before adoption.
- Deployment without GPU infrastructure — A model of this scale requires enterprise-grade GPU clusters (A100, H100) or comparable hardware. CPU inference is impractical; quantized variants not mentioned in card.
- Use cases requiring guaranteed safety/alignment — No safety evaluation, alignment guarantees, or responsible AI framework details are stated in the card. Recommended for internal/controlled deployments; review for production guardrails separately.
License & commercial use
MIT license. Permissive OSI-approved license allowing modification, distribution, and private use without royalty or attribution requirement (though attribution is customary).
MIT license permits commercial use without restriction. However, users deploying this model commercially should: (1) verify no gating restrictions are enforced at runtime (currently gated=false), (2) review the technical report (arxiv:2602.15763) for any unstated dependencies or terms, (3) audit the Z.ai platform terms if using managed inference, (4) ensure compliance with any data/training source licenses not stated in this card. No warranty or liability disclaimers are noted; standard MIT disclaimers apply.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security assessment, vulnerability disclosure process, or adversarial robustness evaluation provided in card. As a large-scale agent model with tool-calling capability, consider: (1) prompt injection and jailbreak resilience (not evaluated), (2) output validation before passing to external tools or shell execution, (3) sandboxing for agentic deployment, (4) audit of training data provenance and potential bias/toxicity. Recommend threat modeling before production use.
Alternatives to consider
DeepSeek-V3.2
Slightly weaker on SWE-Bench Pro (≈57.7 vs 58.4) but smaller and may offer better cost/latency trade-off. Open-source but license clarity varies by variant.
Claude Opus 4.6 (proprietary managed service)
Stronger on HLE (36.7 vs 31.0) and NL2Repo (49.8 vs 42.7); managed API eliminates infrastructure burden. Closed-source; no local deployment option.
Qwen 3.6-Plus (proprietary managed service)
Competitive on GPQA-Diamond (90.4 vs 86.2) and HMMT Feb 2026 (87.8 vs 82.6). Proprietary; evaluation focuses on academic benchmarks rather than agentic tasks.
Ship GLM-5.1 with senior software developers
Start with a local test using vLLM or SGLang on A100/H100 infrastructure, or explore Z.ai's managed API. Review the technical report and GitHub repository for context length, fine-tuning details, and operational recipes. Contact zai-org for production SLAs and support.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
GLM-5.1 FAQ
Can I use GLM-5.1 commercially?
What GPU hardware do I need to run this locally?
Can I fine-tune GLM-5.1 on my own data?
What is the context length?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If GLM-5.1 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy GLM-5.1?
Start with a local test using vLLM or SGLang on A100/H100 infrastructure, or explore Z.ai's managed API. Review the technical report and GitHub repository for context length, fine-tuning details, and operational recipes. Contact zai-org for production SLAs and support.