DeepSWE
DeepSWE is a fully open source, state-of-the-art coding agent built on top of the Qwen3-32B foundation model and trained exclusively via reinforcement learning (RL), without supervised finetuning or distillation from proprietary models. It is developed using rLLM, Agentica’s open source RL framework for language agents. DeepSWE operates as an agent; it interacts with a simulated development environment (via the R2E-Gym environment) using a suite of tools (file editor, search, shell-execution, submit/finish), enabling it to navigate codebases, edit multiple files, compile/run tests, and iteratively produce patches or complete engineering tasks. DeepSWE exhibits emergent behaviors beyond simple code generation; when presented with bugs or feature requests, the agent reasons about edge cases, seeks existing tests in the repository, proposes patches, writes extra tests for regressions, and dynamically adjusts its “thinking” effort.
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DeepCoder
DeepCoder is a fully open source code-reasoning and generation model released by Agentica Project in collaboration with Together AI. It is fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning, achieving a 60.6% accuracy on LiveCodeBench (representing an 8% improvement over the base), a performance level that matches that of proprietary models such as o3-mini (2025-01-031 Low) and o1 while using only 14 billion parameters. It was trained over 2.5 weeks on 32 H100 GPUs with a curated dataset of roughly 24,000 coding problems drawn from verified sources (including TACO-Verified, PrimeIntellect SYNTHETIC-1, and LiveCodeBench submissions), each problem requiring a verifiable solution and at least five unit tests to ensure reliability for RL training. To handle long-range context, DeepCoder employs techniques such as iterative context lengthening and overlong filtering.
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Composer 1.5
Composer 1.5 is the latest agentic coding model from Cursor that balances speed and intelligence for everyday code tasks by scaling reinforcement learning approximately 20x more than its predecessor, enabling stronger performance on real-world programming challenges. It’s designed as a “thinking model” that generates internal reasoning tokens to analyze a user’s codebase and plan next steps, responding quickly to simple problems and engaging deeper reasoning on complex ones, while remaining interactive and fast for daily development workflows. To handle long-running tasks, Composer 1.5 introduces self-summarization, allowing the model to compress and carry forward context when it reaches context limits, which helps maintain accuracy across varying input lengths. Internal benchmarks show it surpasses Composer 1 in coding tasks, especially on more difficult issues, making it more capable for interactive use within Cursor’s environment.
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Composer 1
Composer is Cursor’s custom-built agentic AI model optimized specifically for software engineering tasks and designed to power fast, interactive coding assistance directly within the Cursor IDE, a VS Code-derived editor enhanced with intelligent automation. It is a mixture-of-experts model trained with reinforcement learning (RL) on real-world coding problems across large codebases, so it can produce high-speed, context-aware responses, from code edits and planning to answers that understand project structure, tools, and conventions, with generation speeds roughly four times faster than similar models in benchmarks. Composer is specialized for development workflows, leveraging long-context understanding, semantic search, and limited tool access (like file editing and terminal commands) so it can solve complex engineering requests with efficient and practical outputs.
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