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...Its flagship version, Qwen3-Coder-480B-A35B-Instruct, features a massive 480 billion-parameter Mixture-of-Experts architecture with 35 billion active parameters, delivering top-tier performance on coding and agentic tasks. This model sets new state-of-the-art benchmarks among open models for agentic coding, browser-use, and tool-use, matching performance comparable to leading models like Claude Sonnet. Qwen3-Coder supports an exceptionally long context window of 256,000 tokens, extendable to 1 million tokens using Yarn, enabling repository-scale code understanding and generation. ...

...It demonstrates strong generalization across agent frameworks and consistently improves upon its predecessor, MiniMax-M2. Benchmarks show that it rivals or approaches top proprietary models while remaining fully open for local deployment and customization.

MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before generating code. The model supports full-stack development across web, mobile, and desktop platforms, covering the entire lifecycle from system design to testing and code review. ...

...It also integrates with common developer flows, offering autocompletion and step-by-step plans before running potentially destructive actions. The CLI targets macOS and Linux and is designed to coexist with standard tools rather than replace them, acting as a smart layer on top. Team-focused features center on repeatability and transparency so generated changes can be reviewed, amended, and committed like any other code.

...However, we do have cloud instances & VMs available for running larger jobs and for testing our cloud services. We bundle a lot of pre-computed datasets & machine learning models into the Kite app through the use of a custom filemap & encoding on top of go-bindata. The data, located in kite-go/client/datadeps, is kept in Git-LFS.

...Our specialized DeepCode AI is built and refined by top-tier researchers that use training data from millions of open source projects, never customer data. DeepCode AI's hybrid approach uses multiple models and security-specific training sets for one purpose, to secure applications.