GLM-5.1

Overview. GLM-5.1 was Z.ai's flagship open-weight model at launch, a 754-billion-parameter Mixture-of-Experts architecture that activates roughly 40 billion parameters per token. Released in April 2026 under the MIT licence, it was purpose-built for long-horizon agentic engineering work, pairing a 200K-token context window with integrated reasoning that runs automatically on every request. Developers reach for it when a task needs sustained, multi-step problem-solving rather than a single quick answer.

Key strengths. GLM-5.1 posted a state-of-the-art SWE-Bench Pro score of 58.4, a clear jump from its predecessor's 55.1, putting it among the strongest models available for real-world software engineering tasks. Its post-training pipeline combines supervised fine-tuning on agentic and coding data with reinforcement learning specifically targeted at reasoning and tool-use, which shows up as consistently strong performance on code and agentic benchmarks. The reasoning is always-on and structured, letting the model iterate on a problem internally before producing output.

When to use it. Use GLM-5.1 for complex coding and software-engineering tasks that benefit from deep, multi-step reasoning — refactors, multi-file changes, or agentic tool-loop workflows where the model needs to plan and revise across several turns. It fits batch or asynchronous pipelines well, since the always-on reasoning trace and large 131K-token output ceiling favour throughput over sub-second responsiveness. It's a strong choice when engineering quality matters more than raw response speed.

Limitations. Reasoning is baked in with no user-selectable effort toggle, so you can't dial it down for latency-sensitive, low-complexity calls the way you can with later GLM releases. The model is text-only, with no vision or audio input or output support, and Z.ai has not published a training data cutoff date. It has also been superseded by newer GLM-5.2 and GLM-5.3 releases, though it remains fully supported.

Sovereignty note. GLM-5.1's weights are released under the MIT licence and are openly downloadable, so organisations that need full control over data handling can self-host it on their own Sovereign infrastructure rather than depending on a third-party API's training-on-input or residency policies.