OpenAI builds and deploys advanced AI models like GPT-4o for autonomous agents and workflows.
SWE-2
0Popularity
About SWE-2
SWE-2 is a post-trained coding model designed to improve both capability and cost efficiency in software engineering tasks. It builds on the SWE-1.7 infrastructure and introduces a reinforcement learning algorithm that trains multiple reasoning-effort levels in a single run, advancing the cost–performance frontier. The model is derived from the Kimi K3 base model, which underwent extensive reinforcement learning for agentic coding. SWE-2 achieves higher scores on coding benchmarks such as FrontierCode 1.1 Main and DeepSWE 1.1 while maintaining lower costs compared to several leading models. It demonstrates stronger engineering judgment, enabling more complete solutions with fewer detours and reduced exploration time. The model is available in Devin Desktop, CLI, Web, and Fusion platforms, supporting a range of software development workflows.
Key features
- Multi-level reasoning-effort training in a single RL run
- Pareto-informed cost penalties for efficiency
- Improved reward baselines for stable training
- Enhanced RL rollout serving for higher decoding throughput
- Quantization-aware training for reduced memory usage
- Tripled number of RL environments
- Instruction-following overlays for better alignment
- Flywheel-powered verifier hardening
Use cases
- Automated software engineering tasks
- Codebase exploration and editing
- Test writing and regression checking
Pros
- Trains multiple reasoning-effort levels in a single run
- Achieves higher scores on coding benchmarks at lower costs
- Demonstrates stronger engineering judgment with fewer detours
- Reduces exploration time on simple tasks
- Improves test coverage and verification discipline
Cons
- Requires the Devin platform for access
- No mention of free tier or open-source availability
- Limited to specific use cases within software engineering
Frequently asked questions about SWE-2
What is SWE-2 and what does it do?
SWE-2 is an advanced coding model designed to improve both capability and cost efficiency in software engineering tasks. It builds on the SWE-1.7 infrastructure and introduces a reinforcement learning algorithm that trains multiple reasoning-effort levels in a single run, advancing the cost–performance frontier.
Who is SWE-2 designed for?
SWE-2 is designed for developers, engineering teams, and organizations seeking to automate or augment software development workflows with a model that balances high performance and lower costs.
How does SWE-2 improve upon previous models like SWE-1.7?
SWE-2 introduces a reinforcement learning algorithm that trains all reasoning-effort levels in a single run, improving the model's engineering judgment, reducing exploration time, and achieving higher scores on coding benchmarks while maintaining lower costs.
What platforms is SWE-2 available on?
SWE-2 is available in Devin Desktop, CLI, Web, and Fusion platforms, supporting a range of software development workflows.
How does SWE-2 achieve cost efficiency?
SWE-2 applies a linear cost penalty per effort level in a single reinforcement learning run, with each penalty tuned to the local slope of the base model’s Pareto frontier, reflecting actual user costs in training.
What benchmarks does SWE-2 perform well on?
SWE-2 achieves higher scores on coding benchmarks such as FrontierCode 1.1 Main and DeepSWE 1.1, demonstrating stronger engineering judgment and more complete solutions compared to several leading models.
