GLM-5.1

GLM-5.1 is Z.AI's next-generation flagship model for agentic engineering, positioned to keep working effectively over long, multi-hour sessions rather than plateauing after early gains. It is trained with multi-turn supervised fine-tuning, reinforcement learning, and a process-quality evaluation framework aimed at improving stability, consistency, and tool use across extended tasks, with the goal of running autonomously for up to eight hours in a single pass. Compared with its predecessor GLM-5, it shows stronger engineering intelligence across autonomous planning, sustained execution, bug fixing, and strategy iteration.

In practice, the model is aimed at complex software engineering and repository-level development workflows. Its release benchmarks report a state-of-the-art score of 58.4 on SWE-Bench Pro for complex software engineering tasks, and a wide lead over GLM-5 on NL2Repo repo generation and Terminal-Bench 2.0 real-world terminal tasks. Weights are published openly under an MIT-style distribution on Hugging Face at zai-org/GLM-5.1, with code mirrored at github.com/zai-org/GLM-5, making GLM-5.1 a natural fit for teams building coding agents, deep debuggers, and other long-horizon developer tools that need durable judgment and iteration rather than one-shot answers.

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Provider: Tempr Gateway
Model key: zai/glm-5.1
Release date: Apr 7, 2026
Last updated: Apr 7, 2026
Input modalities
Output modalities
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Cost

Input token cost: $1.40
Output token cost: $4.40

Limits

Output tokens: 131,072 tokens
Context window: 200,000 tokens

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about GLM-5.1

Merge Gateway

CoverageBenchmark

InferenceX provides a technical deep-dive covering GLM-5 and GLM-5.1, noting that GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active) with 28.5T pre-training tokens, and that GLM-5.1 is a point release on the same architecture with stronger coding and SOTA SWE-Bench Pro. The article cites the GLM-5.1's distinguishing claim per the coverage is long-horizon durability: sustaining optimization over hundreds of rounds and thousands of tool calls where earlier models exhaust their repertoire early. The article notes GLM-5 integrates DeepSeek Sparse Attention (DSA) to reduce deployment cost while preserving long-

Merge Gateway

Official sourceRelease Notes

Z.ai's official release-notes index explicitly lists GLM-5.1 under the 2026-04-07 entry, describing it as designed for long-horizon tasks with the ability to work independently for up to 8 hours in a single run, covering the full loop from planning and execution to iterative refinement and final delivery. The entry not The release notes confirm GLM-5.1 was built with multi-turn SFT, RL, and process-based training methods, and serves as the documented baseline against which later releases GLM-5.2 (2026-06-16) and GLM-5.3 (2026-08-18) are compared in subsequent entries. This provides authoritative dating and positioning of GLM-5.1 with