coding-models

Poolside has released Laguna M.1, its flagship coding agent model available for free on OpenRouter. The model features a 128K context window, up to 8K output tokens, and is optimized for agentic coding workflows with tool calling and reasoning capabilities.

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 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.

Z.ai has released GLM-5.1, a model with a 202,752 token context window and significantly improved coding capabilities. The model claims the ability to work autonomously on single tasks for over 8 hours, handling long-horizon projects with continuous planning and execution.

Kwaipilot released KAT-Coder-Pro V2, the latest model in its KAT-Coder series, on March 27, 2026. The model features a 256,000-token context window and is priced at $0.30 per million input tokens and $1.20 per million output tokens. It targets enterprise-grade software engineering with focus on multi-system coordination and web aesthetics generation.

MiniMax released M2.7, a model that autonomously participated in its own development through self-optimization loops. The model ran over 100 optimization rounds on internal coding tasks, achieving a 30% performance boost, and scored 66.6% on OpenAI's MLE Bench Lite—competitive with Gemini 3.1 Pro and GPT-5.4.

Cursor acknowledged that its newly launched Composer 2 coding model was built on Moonshot AI's open-source Kimi 2.5 model, not developed independently. The admission came after user scrutiny revealed code references to Kimi. Cursor claims only 25% of compute came from the base model, with 75% from its own training and reinforcement learning.