Z.AI, the AI platform developed by the team behind the GLM model family, has released GLM-5.1 – its next-generation flagship model developed specifically for agentic engineering. Unlike models optimized for clean, single-turn benchmarks, GLM-5.1 is built for agentic tasks, has significantly stronger coding capabilities than its predecessor, and achieves state-of-the-art performance on SWE-Bench Pro, while outperforming GLM-5 by wide margins on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).
Architecture: DSA, MOE, and asynchronous RL
Before considering what the GLM-5.1 can do, it is important to understand what it is built on – as its architecture is meaningfully different from a standard compact transformer.
GLM-5 adopts DSA to significantly reduce training and inference costs while maintaining long context fidelity. The model uses a glm_moe_dsa architecture (Mix of Experts (MOE) Joint model with DSA). For AI developers evaluating whether to self-host, this matters: MOE models activate only a subset of their parameters per forward pass, which can make inference significantly more efficient than dense models of comparable size, although they require specific service infrastructure.
On the training side, GLM-5 implements a new asynchronous reinforcement learning infrastructure that significantly improves post-training efficiency by separating generation from training. Novel asynchronous agent RL algorithms further improve RL quality, enabling models to learn more effectively from complex, long-horizon interactions. This is what allows models to handle agentic tasks with the kind of consistent judgment that single-turn RL training struggles to produce.
The plateau problem of GLM-5.1 is being solved
To understand what makes GLM-5.1 different at inference time, it helps to understand a typical failure mode in the LLMs used as agents. Previous models – including the GLM-5 – exhaust their repertoire quickly: they apply familiar techniques for quick initial gains, then plateau. Giving them more time doesn’t help.
This is a structural limitation for any developer attempting to use LLM as a coding agent. The model applies the same playbook it knows, hits a wall, and stops making progress no matter how long it goes on. In contrast, GLM-5.1 is designed to be effective on agentic tasks over longer periods of time. The model handles ambiguous problems with better judgment and remains productive over long sessions. It breaks down complex problems, runs experiments, reads results, and identifies bottlenecks with real accuracy. By revisiting its logic and modifying its strategy through repeated iterations, GLM-5.1 maintains optimization over hundreds of rounds and thousands of tool calls.
Consistent performance requires more than a large reference window. This capability requires the model to maintain goal alignment over extended execution, reducing strategy drift, error accumulation, and ineffective trial and error, enabling truly autonomous execution for complex engineering tasks.
Benchmark: where GLM-5.1 stands
On SWE-Bench Pro, GLM-5.1 achieved a score of 58.4, outperforming GPT-5.4, Cloud Opus 4.6 and Gemini 3.1 Pro, setting a new state-of-the-art result.
The comprehensive benchmark profile shows a well-rounded model. GLM-5.1 scores 95.3 on AIME 2026, 94.0 on HMMT November 2025, 82.6 on HMMT February 2026, and 86.2 on GPQA-Diamond – a graduate-level science reasoning benchmark. On agentic and tool-usage benchmarks, GLM-5.1 scores 68.7 on CyberGym (significantly higher than GLM-5’s 48.3), 68.0 on BrowseComp, 70.6 on τ³-Bench, and 71.8 on MCP-Atlas (public set) – the last score is particularly relevant given the growing role of MCP in production agent systems. On Terminal-Bench 2.0, the model’s score is 63.5, which increases to 66.5 when evaluated with the cloud code as scaffolding.
In 12 representative benchmarks covering reasoning, coding, agents, tool usage, and browsing, GLM-5.1 demonstrates a broad and well-balanced capability profile. This shows that GLM-5.1 is not a single-metric improvement – it makes simultaneous advances in general intelligence, real-world coding, and complex task execution.
In terms of the overall situation, the general capability and coding performance of GLM-5.1 are overall aligned with Cloud Opus 4.6.
8-Hour Continuous Performance: What It Really Means
The most significant difference in the GLM-5.1 is its ability to perform long-range tasks. GLM-5.1 can work autonomously for up to 8 hours on a single task, completing the entire process from planning and execution to testing, fixing and delivery.
For developers building autonomous agents, this changes the scope of what’s possible. Instead of orchestrating a model over dozens of short-term tool calls, you can hand GLM-5.1 a complex objective and let it run a full ‘experiment-analyze-optimize’ loop autonomously.
Solid engineering performances epitomize this: GLM-5.1 can build a complete Linux desktop environment from scratch in 8 hours; Perform 178 rounds of autonomous iteration on a vector database task and improve the performance of the initial version by 1.5×; And optimize the CUDA kernel, increasing the speedup from 2.6× to 35.7× through continuous tuning.
The CUDA kernel result is notable for ML engineers: improving the kernel from a 2.6× to 35.7× speedup through autonomous iterative optimization is a level of depth that would take a skilled human engineer significant time to replicate manually.
Model Specifications and Deployment
GLM-5.1 is a 754 billion-parameter MoE model released under the MIT license on HuggingFace. It operates with a 200K context window and supports 128K maximum output tokens – both important for long-horizon tasks that need to keep large codebases or extended logic chains in memory.
GLM-5.1 supports Thinking Mode (offering multiple Thinking Modes for different scenarios), streaming output, function calling, context caching, structured output, and MCP to integrate external tools and data sources.
For local deployment, the following open-source frameworks support GLM-5.1: SGLang (v0.5.10+), vLLM (v0.19.0+), xLLM (v0.8.0+), Transformers (v0.5.3+), and KTransformers (v0.5.3+).
As for API access, the model is available through Z.AI’s API platform. Must be installed to get started zai-sdk via pip and initialization a ZaiClient With your API key. .
key takeaways
- GLM-5.1 sets a new state of the art on SWE-Bench Pro With a score of 58.4, it became one of the strongest publicly available benchmark models for real-world software engineering tasks at the time of release – outperforming GPT-5.4, Cloud Opus 4.6, and Gemini 3.1 Pro.
- This model is designed for long-horizon autonomous executionCapable of working for up to 8 hours on a single complex task – running experiments, modifying strategies, and repeating hundreds of rounds and thousands of tool calls without human intervention.
- GLM-5.1 uses MoE + DSA architecture trained with asynchronous reinforcement learningWhich reduces training and inference costs compared to Dense Transformer while maintaining long-context fidelity – a worthwhile consideration for teams evaluating self-hosting.
- It is open-sourced under the MIT license (754B parameters, 200K context window, 128K max output tokens) and supports local deployment via SGlang, VLLM, XLLM, Transformer and KTransformer, as well as API access via the Z.AI platform with OpenAI SDK compatibility.
- GLM-5.1 goes beyond coding – It shows strong improvements in front-end prototyping, creation of artifacts, and office productivity tasks (Word, Excel, PowerPoint, PDF), establishing it as a general-purpose foundation for both agentic systems and high-quality content workflows.
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