Cognition Releases Coding Model SWE-2

Coding agency Cognition has launched SWE-2, claiming it is the closest self-developed coding model to the frontier, performing comparably to recent frontier models on mainstream evaluations, with costs reduced by up to 70%. The official blog provides a detailed comparison: FrontierCode 1.1 scored 50.0%, just 1 percentage point behind Fable 5.1's 50.9%, with a price approximately 64% lower.

The company claims to have achieved reinforcement learning at a trillion-parameter scale for the first time, adding algorithms that can cover all inference intensities in a single training session on the infrastructure of SWE-1.7, thereby advancing both capability and cost on the Pareto curve. SWE-2 continues training from the Kimi K3 model (approximately 2.8 trillion parameters) that has already undergone process agent coding reinforcement learning, and it is said to be able to raise scores by another 5 to 6 points, shifting the entire cost-performance curve.

Comparison table: SWE-2 scored 73.0% on DeepSWE 1.1, higher than Kimi K3's 68.5%, Grok 4.6's 67.5%, and Fable 5.1's 67.4%, and is close to GPT-5.6 Sol's 72.7% and GPT-6 Astra's 74.1%; Terminal-Bench 2.1 scored 92.8%; Terminal-Bench 4 scored 27.3%, significantly lower than Fable 5.1's 55.8% and Astra's 57.9%. The company states that SWE-2 outperforms both SWE-1.7 and Grok 4.6 in scores and costs on FrontierCode and DeepSWE, and approaches Astra at about one-quarter of the cost.

Cognition's product is the software engineer agent Devin. A recent investor letter stated that Devin's internal code production share has risen from about 13% to over 90%. SWE-2 is a model layer update, still targeting workloads for agents writing code, rather than general chat. The evaluations are based on publicly referenced benchmarks, with the drop in Terminal-Bench 4 indicating that long-range terminal tasks have yet to align with the strongest frontiers.

In market mechanics, this represents a price war in coding inference. The buyers are engineering teams looking to reduce token bills; the sellers are application labs using open-source large bases combined with proprietary reinforcement learning to offer cheaper scores. Funding is shifting from calling closed-source frontier interfaces to replaceable coding-specific weights. Beneficiaries are Devin clients who have embedded agents into their repositories; those under pressure are general models selling scores close to FrontierCode at high prices. The event is driven by model releases.

On a supplementary note, the tweet stating "up to 70% lower" and the blog post stating "64% lower" are not the same statement. The ownership of the K3 base and commercial terms depend on licensing, which is not clearly stated in the release post.

Source: Public Information