GLM-5.1 introduces iterative strategy refinement capabilities enabling hundreds of iterations on complex coding tasks. The model achieves 58.4% on SWE-Bench Pro and 6.1x performance improvements on vector database optimization through autonomous approach revision.
GLM-5.1 introduces sustained agentic reasoning over hundreds of iterations with improved performance on SWE-Bench Pro (58.4%, +3.3pp vs. GLM-5) and NL2Repo (42.7%, +6.8pp vs. GLM-5). The model maintains productivity across longer problem-solving sessions through iterative experimentation and strategy revision.
Zhipu AI released GLM-5.1, a 754-parameter model optimized for agentic engineering tasks. The model scores 58.4% on SWE-Bench Pro, outperforming Claude 3.5 Sonnet (57.3%), and demonstrates sustained reasoning capability over hundreds of iterations.
Zhipu AI has released GLM-5.1, a freely available open-weight model designed for long-running programming tasks that achieves 58.4% on SWE-Bench Pro, edging out GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%). The model's core capability is iterative strategy refinement—it rethinks its approach across hundreds of iterations and thousands of tool calls, recognizing dead ends and shifting tactics without human intervention. However, GLM-5.1 trails on reasoning and knowledge benchmarks, scoring 31% on Humanity's Last Exam compared to Gemini 3.1 Pro's 45%.
Zhipu AI has released GLM-5.1, a 754-billion parameter model designed for agentic engineering with significantly improved coding capabilities over its predecessor. The model achieves 58.4% on SWE-Bench Pro and demonstrates sustained performance improvement over hundreds of tool calls and iterations, unlike earlier models that plateau quickly.