GLM-5.1 Complete Guide — Architecture, Benchmarks, and What Makes It Different
Z.ai (formerly Zhipu AI) just released GLM-5.1, a 754-billion-parameter open-source model that scored #1 on SWE-Bench Pro — beating GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. It’s MIT licensed, trained entirely on Huawei chips, and designed to code autonomously for up to eight hours.
Here’s everything you need to know.
What is GLM-5.1?
GLM-5.1 is the latest flagship model from Z.ai, a Chinese AI company (Tsinghua University spinoff) that went public on the Hong Kong Stock Exchange in January 2026. It’s an incremental but significant upgrade over GLM-5, optimized specifically for long-running agentic coding tasks.
The tagline: “From Vibe Coding to Agentic Engineering.”
Where most AI coding tools generate snippets or handle single-file edits, GLM-5.1 is designed to plan, execute, test, debug, and iterate across entire codebases over extended sessions.
Architecture
GLM-5.1 uses the same base architecture as GLM-5:
- Total parameters: 754 billion (744B in some sources — the difference is likely embedding layers)
- Active parameters per token: ~40 billion
- Architecture: Mixture-of-Experts (MoE) with 256 experts, 8 activated per token (5.9% sparsity)
- Context window: 200K tokens
- Attention: DeepSeek Sparse Attention (DSA) for efficient long-context processing
- Training data: 28.5 trillion tokens
- Training hardware: 100,000 Huawei Ascend 910B chips — zero NVIDIA dependency
- License: MIT (fully open, commercial use allowed)
The MoE architecture is key to understanding GLM-5.1’s efficiency. Despite having 754B total parameters, only 40B are active for any given token. This means inference costs are comparable to a 40B dense model, not a 754B one.
Benchmarks
GLM-5.1’s headline numbers:
| Benchmark | GLM-5.1 | GPT-5.4 | Claude Opus 4.6 | Gemini 3.1 Pro | GLM-5 |
|---|---|---|---|---|---|
| SWE-Bench Pro | 58.4 | 57.7 | 57.3 | 55.1 | 49.2 |
| AIME | 95.3 | — | — | — | 89.7 |
| Terminal-Bench 2.0 | Strong | — | — | — | 61.1 |
| NL2Repo | Leading | — | — | — | Baseline |
SWE-Bench Pro is the harder variant of SWE-bench that tests multi-file, multi-step issue resolution — the kind of real-world coding that separates capable agents from autocomplete engines.
The 58.4 score puts GLM-5.1 roughly a full point ahead of GPT-5.4 and 1.1 points ahead of Claude Opus 4.6. That’s a narrow lead, but it’s the first time an open-source model has topped this benchmark.
Z.ai also claims GLM-5.1 reaches 94.6% of Claude Opus 4.6’s coding performance on their internal evaluation using Claude Code as the harness.
What’s new vs GLM-5?
GLM-5.1 doesn’t change the base architecture. The improvements are in training optimization for agentic workflows:
- Longer productive sessions: GLM-5 would apply familiar strategies, make early progress, then hit a wall. GLM-5.1 can rethink its approach across hundreds of iterations.
- Better goal alignment: Maintains coherence over thousands of tool calls instead of drifting off-task.
- Improved planning: Breaks complex problems down, runs experiments, reads results, and identifies blockers with better precision.
- 28% coding improvement: Scored 45.3 on Z.ai’s internal coding eval vs GLM-5’s 35.4.
The practical difference: GLM-5.1 can work autonomously on a single coding task for up to eight hours. In a demo, it built a full Linux desktop environment from scratch.
The Huawei story
GLM-5.1 (and GLM-5) were trained entirely on Huawei Ascend 910B chips using the MindSpore framework. Zero NVIDIA hardware was used.
This matters because Zhipu AI has been on the U.S. Entity List since January 2025, which bans access to H100/H200 GPUs. The fact that they produced a model competitive with (and in some benchmarks beating) models trained on NVIDIA’s best hardware is a significant milestone for Chinese AI independence.
How to access GLM-5.1
Several options:
- Hugging Face — Download weights directly from zai-org/GLM-5.1 (MIT license)
- GLM Coding Plan — Z.ai’s subscription service ($3-10/month), supports GLM-5.1 on all tiers (Max, Pro, Lite)
- OpenRouter — Available as an API endpoint
- Self-hosted — Via vLLM or similar inference servers (requires significant hardware — see our how to run GLM-5.1 locally guide)
- Claude Code integration — GLM-5.1 provides an Anthropic-compatible API, so it works as a drop-in replacement in Claude Code
Who should use GLM-5.1?
GLM-5.1 is best for:
- Agentic coding workflows — If you’re building AI agents that need to work autonomously for extended periods
- Cost-conscious teams — MIT license means no per-token costs if you self-host
- Privacy-sensitive deployments — Run it on your own infrastructure with no data leaving your network
- Complex multi-file refactors — The SWE-Bench Pro score reflects real-world multi-step engineering tasks
It’s less ideal for:
- Quick completions — For fast autocomplete, smaller models like Gemma 4 or GLM-5-Turbo are more practical
- Consumer hardware — At 754B parameters, even quantized versions need hundreds of GB of memory
- Non-coding tasks — GLM-5.1 is optimized for coding; for general chat, other models may be better
Bottom line
GLM-5.1 is the most capable open-source coding model available today. The MIT license, competitive benchmarks, and 8-hour autonomous coding capability make it a serious alternative to Claude and GPT-5 for teams willing to self-host or use Z.ai’s affordable Coding Plan.
The fact that it was trained entirely on Chinese hardware without NVIDIA chips adds a geopolitical dimension that will shape the AI industry for years.
Related: GLM-5.1 vs Claude vs GPT-5 for Coding · How to Use GLM-5.1 with Claude Code · Best Open-Source Coding Models 2026