Cognition's SWE-2 rivals Fable 5.1 at 64% lower cost

Cognition scored its new coding model, SWE-2, at 50.0% on FrontierCode 1.1 Main, a point behind Fable 5.1's 50.9%. It runs at 64% lower cost than Fable 5.1, per Cognition, changing the math for teams pointing agents at a large backlog of routine tickets.

SWE-2 is trained with reinforcement learning on Kimi K3, a 2.8-trillion-parameter base model, with three effort levels trained in a single run. On Terminal-Bench 2.1 it scores 92.8%, close to frontier-model territory on shorter, tool-use tasks.

The story flips on Terminal-Bench 4, built for longer, multi-step work. SWE-2 scores 27.3% there, less than half of Fable 5.1's 55.8% and GPT-6 Astra's 57.9%. Cognition's post doesn't say why the longer-horizon score falls off so hard.

I'd route routine tickets to SWE-2 and keep long, multi-step work on a frontier model. The FrontierCode near-tie and the Terminal-Bench 4 collapse both point at a model built for short, well-defined tasks over long ones.