zai-org/GLM-5.1

Hugging Face
TEXT GENERATIONConcurrent Unit Cost:4Model Size:754BQuant:FP8Context Size:198kTool Calling:SupportedPublished:Apr 3, 2026License:mitArchitecture:Transformer1.8K Open Weights Warm

GLM-5.1 is a flagship model developed by zai-org, specifically engineered for advanced agentic tasks and significantly stronger coding capabilities. It excels at complex problem-solving over long horizons, demonstrating improved judgment and sustained productivity through iterative reasoning and thousands of tool calls. The model achieves state-of-the-art performance on SWE-Bench Pro and leads its predecessor, GLM-5, on NL2Repo and Terminal-Bench 2.0, making it ideal for sophisticated code generation and real-world terminal tasks.

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GLM-5.1: Next-Generation Agentic Engineering Model

GLM-5.1, developed by zai-org, is a flagship model designed for advanced agentic engineering, showcasing significantly enhanced coding capabilities compared to its predecessor, GLM-5. This model is distinguished by its ability to maintain effectiveness over much longer task horizons, handling ambiguous problems with superior judgment and sustaining productivity through extended sessions.

Key Capabilities

  • Advanced Agentic Problem Solving: Breaks down complex problems, runs experiments, interprets results, and identifies blockers with high precision.
  • Sustained Optimization: Revisits reasoning and revises strategies through repeated iteration, enabling optimization over hundreds of rounds and thousands of tool calls.
  • Superior Coding Performance: Achieves state-of-the-art results on SWE-Bench Pro (58.4%) and significantly outperforms GLM-5 on NL2Repo (42.7%) for repository generation and Terminal-Bench 2.0 (63.5%) for real-world terminal tasks.
  • Robust Benchmark Scores: Demonstrates strong performance across various benchmarks, including CyberGym (68.7%) and BrowseComp (68.0%), indicating broad utility in complex environments.

Good For

  • Agentic Applications: Ideal for scenarios requiring models to perform multi-step, iterative tasks with tool use and complex decision-making.
  • Code Generation and Refinement: Excels in generating and refining code, particularly for intricate software engineering problems.
  • Real-world Terminal Tasks: Highly effective for automating and solving problems within terminal environments.
  • Long-Horizon Problem Solving: Suitable for tasks that demand sustained reasoning and adaptation over extended periods, where other models might plateau early.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p