GoKawiilIntroducing SWE-2: Pushing the Pareto Frontier By The Cognition Team 09.10.26
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.
base model end of training
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.
Coding benchmark results Benchmark SWE-2 Kimi K3 Grok 4.6 Fable 5.1 GPT-5.6 Sol GPT-6 Astra SWE-1.7 FrontierCode 1.1 Main 50.0 % 44.2 % 48.0 % 50.9 % 47.5 % 53.3 % 42.0 % DeepSWE 1.1 73.0 % 68.5 % 67.5 % 67.4 % 72.7 % 74.1 % 37.7 % Terminal-Bench 2.1 92.8 % 88.3 % 88.4 % 91.4 % 88.8 % 89.9 % 81.5 % Terminal-Bench 4 27.3 % 21.5 % 20.3 % 55.8 % 37.3 % 57.9 % 7.6 %
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: Cost penalties. We apply a linear cost penalty per effort level in a single RL run, with each penalty tuned to the local slope of the base model’s Pareto frontier. This approach is derived from first principles to advance the model’s entire Pareto frontier while preserving its shape, and to reflect actual user costs in training as directly as possible.
Reward baselines. We derive the length-weighted reward baseline we have used since SWE-1.6 and show how it significantly stabilizes training.
RL rollout serving. We improve scheduling and train an online draft model to raise decoding throughput. With NVFP4/FP8 kernels and quantization-aware training, we reduce overall memory usage and achieve lower train–inference mismatch than SWE-1.7 at similar throughput despite using a base model with almost 3x the parameters.
Training data. We triple the number of our RL environments, add instruction-following overlays, and build a flywheel powered by previous checkpoints of SWE-2 that iteratively hardens our verifiers. SWE-2 is available starting today in Devin Desktop and CLI. We’re also rolling it out on Devin Web and Fusion.
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