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# Cognition's new coding model undercuts its rival on price
- URL: https://globalfeed.ai/en/cognition-releases-swe-2-50-on-frontiercode-64-cheaper-than-fable-5-1/
- Published: 2026-09-11T17:56:51.000Z
- Updated: 2026-09-11T17:56:51.000Z
- Description: Cognition says its new coding model SWE-2 scores 50.0% on FrontierCode 1.1 Main, within a point of Fable 5.1 while costing 64% less.
- Author: GlobalFeed Editor
- Tags: Cognition, SWE-2, coding models, x-cognition_labs, dil-en, denetim-gerekli, video

Cognition has released **SWE-2**, its new coding model. According to the company's blog post, SWE-2 scores 50.0% on FrontierCode 1.1 Main, landing within one point of Fable 5.1 while being 64% cheaper.

SWE-2 is post-trained from **Kimi K3**, a 2.8-trillion-parameter model that had already gone through extensive reinforcement learning (RL) for agentic coding. Cognition says its own RL stage still added 5 to 6 points on many benchmarks. The company also states it scaled RL to the multi-trillion-parameter regime for the first time.

## Cost and behaviour

The comparisons Cognition reports: 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.

On efficiency, the company reports that SWE-2 medium outscores SWE-1.7 while taking 58% fewer turns and costing 81% less on average. SWE-2 medium makes its first real edit after a median of 18 steps, compared with 48 for SWE-1.7\. Cognition notes user feedback that SWE-1.7 over-explored on simple tasks, and says most of the new efficiency comes from focused exploration. The high and max effort levels plan more and run more verification on complex tasks.

## All effort levels in one run

The main methodological change is an RL algorithm that trains every reasoning-effort level in a single run. Cognition uses a cost-penalised reward of the form R = S - λ\_e C, where S marks whether a rollout succeeded, C is the rollout cost (a mix of inference cost in USD and rollout time), and λ\_e is tuned to the slope of the base model's Pareto curve at that effort level. The company argues a linear penalty is the only form that keeps the objective consistent before and after averaging.

SWE-2 is available from today in Devin Desktop and CLI, with rollout underway on Devin Web and Fusion. Full details are in [Cognition's announcement](https://cognition.com/blog/swe-2?ref=globalfeed.ai).