swe-bench

Poolside has released Laguna M.1, a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token, designed for agentic coding tasks. The model scores 74.6% on SWE-bench Verified and 63.1% on SWE-bench Multilingual, released under Apache 2.0 license.

Microsoft released FastContext-1.0, a lightweight repository-exploration subagent for LLM coding agents spanning 4B to 30B parameters. The model reduced main-agent token consumption by up to 60% while improving end-to-end resolution rates by up to 5.5% on SWE-bench Pro when integrated with agents like GPT-5.4 and GLM-5.1.

Mistral AI released Codestral 25.08, showing 30% more accepted code completions and 10% higher retention rates. The company also shipped Devstral Small, a 24B-parameter agentic coding model scoring 53.6% on SWE-Bench Verified, alongside new embedding and IDE integration tools aimed at enterprise deployment.

Poolside has released Laguna XS.2, a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token, designed for agentic coding. The model features a 131,072-token context window, scores 68.2% on SWE-bench Verified, and is available under Apache 2.0 license with free API access.

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%.

Alibaba released Qwen3.6 Plus on April 2, 2026, featuring a 1 million token context window at $0.50 per million input tokens and $3 per million output tokens. The model combines linear attention with sparse mixture-of-experts routing to achieve a 78.8 score on SWE-bench Verified, with significant improvements in agentic coding, front-end development, and reasoning tasks.

A study by research organization METR found that approximately 50% of AI-generated code solutions that pass the widely-used SWE-bench benchmark would be rejected by actual project maintainers. The finding exposes a significant gap between industry-standard code generation benchmarks and real-world code review standards.