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Cognition launches new SWE-2 model, Rivaling Fable 5.1 and GPT-Astra
Today we’re introducing SWE-2, our most advanced coding model yet. It pushes the Pareto frontier of capability and cost, achieving 50.0% on FrontierCode 1.1 Main1, within one point of Fable 5.1 while being 64% cheaper.
With SWE-2, we scaled RL to the multi-trillion-parameter regime for the first time, building on the SWE-1.72 training infrastructure and recipe. The key addition is an RL algorithm that trains all reasoning-effort levels in a single run, advancing the whole cost–performance frontier.
The result is our closest model yet to the frontier. On FrontierCode 1.1 Main and DeepSWE 1.1, SWE-2 beats SWE-1.7 and Grok 4.6 on both score and cost, matches GPT-5.6 Sol and Fable 5/5.1 at a fraction of their price, and comes within a few points of GPT-6 Astra at a quarter of the cost.
SWE-2 is post-trained from Kimi K33, a 2.8T-parameter model that had already undergone extensive RL for agentic coding. As with SWE-1.7, our RL still finds substantial headroom, adding 5–6 points on many benchmarks and shifting K3’s entire cost–performance frontier.
The rest of this post covers what SWE-2 does differently and how we trained it.
We begin with SWE-2’s behavior, focusing on the characteristics that make it more efficient and intelligent compared to our previous models. Then, we detail the post-training advances behind SWE-2:
SWE-2 is available starting today in Devin Desktop and CLI. We’re also rolling it out on Devin Web and Fusion.
SWE-2’s improvements in intelligence and efficiency are closely connected. Stronger engineering judgment allows the agent to write more complete solutions alongside fewer detours and redundant reads. On FrontierCode 1.1 Main, we see that SWE-2 medium scores higher than SWE-1.7 while taking 58% fewer turns and costing 81% less on average.
In our previous post2, we observed SWE-1.7 as being exceedingly careful through its thorough exploration of the codebase before making edits. While boosting performance, this led to user feedback that SWE-1.7 tended to over-explore and overthink on simple tasks. Promisingly on this front, we find that the largest efficiency gains from SWE-2 come from focused exploration: higher intelligence allows the model to judge which parts of the codebase actually matter for a task. This allows SWE-2 to begin implementation sooner: on FrontierCode 1.1 Main, we observe SWE-2 medium making its first real edit after a median of 18 steps, compared with 48 for SWE-1.7.
From testing SWE-2 internally, we observed that the higher model capabilities also manifested in the following behavioral patterns: