# GLM-5.1

> A 754B open-weight Mixture-of-Experts model from China that beats GPT-5.4 and Claude Opus 4.6 on SWE-Bench Pro — under an MIT license.

- Org: Z.ai
- Date: 2026-04-07
- Category: model
- Source: https://huggingface.co/zai-org/GLM-5.1
- Canonical page: https://ai-tldr.dev/releases/zai-glm-5-1/

## In short

Open-weight 754B MoE model from Z.ai focused on long-horizon agentic coding. Scores 58.4 on SWE-Bench Pro — ahead of GPT-5.4 and Opus 4.6, under an MIT license.

## What it is

GLM-5.1 is the next-generation flagship model from Z.ai (formerly Zhipu), aimed squarely at agentic engineering — the workflow where a model reads an issue, plans fixes, writes code, runs tests, and iterates. It is the first open-weight model to top SWE-Bench Pro, scoring 58.4% versus 57.7% for GPT-5.4 and 57.3% for Claude Opus 4.6.

## How it works

GLM-5.1 is a 754B-parameter Mixture-of-Experts model built on the GLM-5 base architecture with DeepSeek Sparse Attention. Z.ai specifically trained it for sustained work: the docs claim it can stay productive across hundreds of turns and thousands of tool calls, breaking a task down, running experiments, reading results and revising strategy. Weights ship on Hugging Face (BF16 and FP8) under an MIT license.

## Why it matters

A frontier-grade open-weight coder under MIT is the cleanest challenge yet to closed-model pricing for agentic coding. Teams running long-horizon agents on Claude Code or OpenAI Codex can now self-host a model that is at least competitive on SWE-Bench Pro without the per-token bill.

## Who it's for

Self-hosters, agent framework authors, anyone building around Claude Code or Codex who wants an open fallback.

## Try it

huggingface.co/zai-org/GLM-5.1

## Links

- [Model card](https://huggingface.co/zai-org/GLM-5.1)
- [Repo](https://github.com/zai-org/GLM-5)
- [Coverage](https://www.marktechpost.com/2026/04/08/z-ai-introduces-glm-5-1-an-open-weight-754b-agentic-model-that-achieves-sota-on-swe-bench-pro-and-sustains-8-hour-autonomous-execution/)