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Today, we’re launching SWE-1.7, the most capable model we’ve trained so far. It reaches frontier-level intelligence at a much lower cost, advancing the cost-performance Pareto curve.

SWE-1.7 is the result of broad improvements across our RL pipeline: better infrastructure, more stable training, higher-quality data, and new techniques for long-horizon tasks. Since SWE-1.7 was trained from a Kimi K2.7 base, which had already undergone extensive RL post-training, the large additional gains from our own training challenge the idea of a ‘post-training ceiling’ and suggest that RL can push capabilities much further than previously believed.

At Cognition, we have been formulating and refining principles for good agentic software engineering both in evaluation, with FrontierCode 1 , 2 , and now in training, with SWE-1.7. Our model is particularly optimized for longer-horizon asynchronous tasks, an important component of high-quality software engineering.

SWE-1.7 is available today in Devin ( Web , Desktop , and CLI ) via Cerebras at 1000 TPS. We encourage you to try it for yourself!

The rest of this post covers how we trained SWE-1.7: the infrastructure, algorithms, and data work behind our model. We cover four important components that stand out.

Finally, we conclude by sharing some observations on interesting behavioral tendencies, such as careful exploration and concise reasoning, that the model acquired as a result of our training setup.