GLM-5.1 Complete Guide — The Free Model That Rivals Claude (2026)

#glm #zai #opensource #coding

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.

FAQ

Is GLM-5.1 free?

Yes. GLM-5.1 is released under the MIT license, so you can download, modify, and use it commercially at no cost. If you prefer not to self-host, Z.ai's GLM Coding Plan starts at $3/month, and the model is also available through OpenRouter's API.

How does GLM-5.1 compare to Claude Opus?

On SWE-Bench Pro, GLM-5.1 scores 58.4 vs Claude Opus 4.6's 57.3 — a narrow but meaningful lead on multi-file coding tasks. Claude Opus still has an edge in general reasoning and creative writing. For a broader breakdown, see our AI model comparison.

Can I run GLM-5.1 locally?

Yes, but you'll need serious hardware. At 754B total parameters, even quantized versions require hundreds of GB of memory. Check our how to run GLM-5.1 locally guide for specific hardware requirements and setup instructions.

What is Z.ai?

Z.ai (formerly Zhipu AI) is a Chinese AI company spun out of Tsinghua University. It went public on the Hong Kong Stock Exchange in January 2026. Z.ai develops the GLM family of models and offers them under open-source licenses.