Introducing SWE-2, our closest model yet to the frontier. On leading evals, it scores on par with recent frontier models – at up to 70% lower cost. We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.

Last week we published a factorization of RSA-260. Today, we’re sharing the methodology of how Devin and a Cognition researcher built the world’s fastest GPU optimized lattice siever, to make factoring numbers 10x cheaper than the previous state of the art: cognition.com/blog/factoring…

The world needs far more software than it can build. Cognition exists to change that. We’ve just raised over $2B at a $48B valuation, led by a16z, Accel, Founders Fund, General Catalyst, and Avenir. Since our round in May, run-rate revenue has grown from $492 M to almost $900 M.

At Cognition, one of our values is to go for it all: when faced with a tradeoff, pick the ambition-maximizing direction. When we launched Devin in 2024, we envisioned a future where every team had an infinite army of junior engineers. We were early and Devin wasn't good enough. Now it is! The only constraint left is how ambitious you're willing to be.

Introducing SWE-1.7, the most capable model we’ve trained yet. It scores within a few points of the strongest frontier models at a fraction of the cost, and is now available at 1000 tok/s. RL is not hitting its limit: after refining our recipe, we keep seeing gains as we scale