DeepCoder

Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning, scaling test‑case generation for diverse coding challenges, and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and environment variables.

Devstral Small 2 is the compact, 24 billion-parameter variant of the new coding-focused model family from Mistral AI, released under the permissive Apache 2.0 license to enable both local deployment and API use. Alongside its larger sibling (Devstral 2), this model brings “agentic coding” capabilities to environments with modest compute: it supports a large 256K-token context window, enabling it to understand and make changes across entire codebases. On the standard code-generation benchmark (SWE-Bench Verified), Devstral Small 2 scores around 68.0%, placing it among open-weight models many times its size. Because of its reduced size and efficient design, Devstral Small 2 can run on a single GPU or even CPU-only setups, making it practical for developers, small teams, or hobbyists without access to data-center hardware. Despite its compact footprint, Devstral Small 2 retains key capabilities of larger models; it can reason across multiple files and track dependencies.