Z.AI, the AI platform developed by the workforce behind the GLM mannequin household, has launched GLM-5.1 — its next-generation flagship mannequin developed particularly for agentic engineering. In contrast to fashions optimized for clear, single-turn benchmarks, GLM-5.1 is constructed for agentic duties, with considerably stronger coding capabilities than its predecessor, and achieves state-of-the-art efficiency on SWE-Bench Professional whereas main GLM-5 by a large margin on NL2Repo (repo technology) and Terminal-Bench 2.0 (real-world terminal duties).
Structure: DSA, MoE, and Asynchronous RL
Earlier than diving into what GLM-5.1 can do, it’s value understanding what it’s constructed on — as a result of the structure is meaningfully completely different from a normal dense transformer.
GLM-5 adopts DSA to considerably scale back coaching and inference prices whereas sustaining long-context constancy. The mannequin makes use of a glm_moe_dsa structure (Combination of Specialists (MoE) mannequin mixed with DSA). For AI devs evaluating whether or not to self-host, this issues: MoE fashions activate solely a subset of their parameters per ahead move, which might make inference considerably extra environment friendly than a comparably-sized dense mannequin, although they require particular serving infrastructure.
On the coaching facet, GLM-5 implements a brand new asynchronous reinforcement studying infrastructure that drastically improves post-training effectivity by decoupling technology from coaching. Novel asynchronous agent RL algorithms additional enhance RL high quality, enabling the mannequin to study from advanced, long-horizon interactions extra successfully. That is what permits the mannequin to deal with agentic duties with the type of sustained judgment that single-turn RL coaching struggles to supply.
The Plateau Drawback GLM-5.1 is Fixing
To grasp what makes GLM-5.1 completely different at inference time, it helps to grasp a selected failure mode in LLMs used as brokers. Earlier fashions — together with GLM-5 — are likely to exhaust their repertoire early: they apply acquainted methods for fast preliminary good points, then plateau. Giving them extra time doesn’t assist.
It is a structural limitation for any developer making an attempt to make use of an LLM as a coding agent. The mannequin applies the identical playbook it is aware of, hits a wall, and stops making progress no matter how lengthy it runs. GLM-5.1, in contrast, is constructed to remain efficient on agentic duties over for much longer horizons. The mannequin handles ambiguous issues with higher judgment and stays productive over longer classes. It breaks advanced issues down, runs experiments, reads outcomes, and identifies blockers with actual precision. By revisiting its reasoning and revising its technique via repeated iteration, GLM-5.1 sustains optimization over a whole bunch of rounds and hundreds of software calls.
The sustained efficiency requires greater than a bigger context window. This functionality requires the mannequin to keep up aim alignment over prolonged execution, decreasing technique drift, error accumulation, and ineffective trial and error, enabling really autonomous execution for advanced engineering duties.
Benchmarks: The place GLM-5.1 Stands
On SWE-Bench Professional, GLM-5.1 achieves a rating of 58.4, outperforming GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Professional, setting a brand new state-of-the-art consequence.
The broader benchmark profile reveals a well-rounded mannequin. GLM-5.1 scores 95.3 on AIME 2026, 94.0 on HMMT Nov. 2025, 82.6 on HMMT Feb. 2026, and 86.2 on GPQA-Diamond — a graduate-level science reasoning benchmark. On agentic and tool-use benchmarks, GLM-5.1 scores 68.7 on CyberGym (a considerable soar from GLM-5’s 48.3), 68.0 on BrowseComp, 70.6 on τ³-Bench, and 71.8 on MCP-Atlas (Public Set) — the final one significantly related given MCP’s rising function in manufacturing agent techniques. On Terminal-Bench 2.0, the mannequin scores 63.5, rising to 66.5 when evaluated with Claude Code because the scaffolding.
Throughout 12 consultant benchmarks masking reasoning, coding, brokers, software use, and looking, GLM-5.1 demonstrates a broad and well-balanced functionality profile. This reveals that GLM-5.1 will not be a single-metric enchancment — it advances concurrently throughout common intelligence, real-world coding, and sophisticated job execution.
When it comes to general positioning, GLM-5.1’s common functionality and coding efficiency are general aligned with Claude Opus 4.6.
8-Hour Sustained Execution: What That Really Means
Crucial distinction in GLM-5.1 is its capability for long-horizon job execution. GLM-5.1 can work autonomously on a single job for as much as 8 hours, finishing the complete course of from planning and execution to testing, fixing, and supply.
For builders constructing autonomous brokers, this adjustments the scope of what’s potential. Relatively than orchestrating a mannequin over dozens of short-lived software calls, you’ll be able to hand GLM-5.1 a fancy goal and let it run an entire ‘experiment–analyze–optimize’ loop autonomously.
The concrete engineering demonstrations make this tangible: GLM-5.1 can construct an entire Linux desktop setting from scratch in 8 hours; carry out 178 rounds of autonomous iteration on a vector database job and enhance efficiency to 1.5× the preliminary model; and optimize a CUDA kernel, rising speedup from 2.6× to 35.7× via sustained tuning.
That CUDA kernel result’s notable for ML engineers: enhancing a kernel from 2.6× to 35.7× speedup via autonomous iterative optimization is a degree of depth that will take a talented human engineer vital time to duplicate manually.
Mannequin Specs and Deployment
GLM-5.1 is a 754-billion-parameter MoE mannequin launched underneath the MIT license on HuggingFace. It operates with a 200K context window and helps as much as 128K most output tokens — each essential for long-horizon duties that want to carry giant codebases or prolonged reasoning chains in reminiscence.
GLM-5.1 helps pondering mode (providing a number of pondering modes for various eventualities), streaming output, operate calling, context caching, structured output, and MCP for integrating exterior instruments and knowledge sources.
For native deployment, the next open-source frameworks help GLM-5.1: SGLang (v0.5.10+), vLLM (v0.19.0+), xLLM (v0.8.0+), Transformers (v0.5.3+), and KTransformers (v0.5.3+).
For API entry, the mannequin is out there via Z.AI’s API platform. Getting began requires putting in zai-sdk through pip and initializing a ZaiClient along with your API key. .
Key Takeaways
- GLM-5.1 units a brand new state-of-the-art on SWE-Bench Professional with a rating of 58.4, outperforming GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Professional — making it one of many the strongest publicly benchmarked mannequin for real-world software program engineering duties on the time of launch.
- The mannequin is constructed for long-horizon autonomous execution, able to engaged on a single advanced job for as much as 8 hours — working experiments, revising methods, and iterating throughout a whole bunch of rounds and hundreds of software calls with out human intervention.
- GLM-5.1 makes use of a MoE + DSA structure educated with asynchronous reinforcement studying, which reduces coaching and inference prices in comparison with dense transformers whereas sustaining long-context constancy — a significant consideration for groups evaluating self-hosting.
- It’s open-weight underneath the MIT license (754B parameters, 200K context window, 128K max output tokens) and helps native deployment through SGLang, vLLM, xLLM, Transformers, and KTransformers, in addition to API entry via the Z.AI platform with OpenAI SDK compatibility.
- GLM-5.1 goes past coding — it additionally reveals sturdy enhancements in front-end prototyping, artifacts technology, and workplace productiveness duties (Phrase, Excel, PowerPoint, PDF), positioning it as a general-purpose basis for each agentic techniques and high-quality content material workflows.
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