Cognition just released SWE-1.7, the most capable model in their SWE series and the engine now running inside Devin. The headline is striking: a model that scores within a few points of Claude Opus 4.8 and GPT-5.5 on agentic coding benchmarks, at a cost of $1.97 per task, running at 1000 tokens per second. That combination , frontier-adjacent quality, fraction of the cost, and blazing inference speed , is what makes this release worth paying attention to.
The benchmark that actually matters
To understand SWE-1.7's performance, you first need to understand FrontierCode, Cognition's proprietary evaluation benchmark. Unlike SWE-bench, which asks whether a model can pass unit tests, FrontierCode asks a harder question: would a real maintainer merge this PR?
FrontierCode evaluates whether coding agents produce mergeable, production-quality pull requests, scoring correctness, tests, scope, style, and maintainability through maintainer-authored rubrics. Tasks were built with open-source maintainers, with each taking 40+ hours and evaluated on dimensions like regression safety, cleanliness, scope, test correctness, and maintainability.
On FrontierCode 1.1 Main, here's how the models stack up:
| Model | FrontierCode 1.1 Main | Terminal-Bench 2.1 | SWE-Bench Multilingual |
|---|---|---|---|
| SWE-1.7 | 42.3% | 81.5% | 77.8% |
| GPT-5.5 | 43.0% | 84.2% | 76.8% |
| Claude Opus 4.8 | 46.5% | 86.9% | 84.4% |
| Claude Opus 4.7 | 38.5% | 83.0% | 80.5% |
| Kimi K2.7 Code (base) | 30.1% | 72.7% | 73.5% |
| SWE-1.6 | 9.4% | 39.7% | 58.3% |
That jump from SWE-1.6's 9.4% to SWE-1.7's 42.3% on FrontierCode is not a rounding error. It reflects a fundamentally different training run , and a model that behaves differently in practice.
What it's actually good at , and where it falls short
SWE-1.7 was specifically optimized for long-horizon, asynchronous software engineering tasks. The model is particularly optimized for longer-horizon asynchronous tasks, an important component of high-quality software engineering. In practice, this shows up most clearly in bug investigation: SWE-1.7 is much more likely to investigate the root cause of a bug and consider edge cases, hypotheticals, adversarial inputs, and beyond-the-ask requirements than Kimi K2.7-Code.
The tradeoff is scope. The extra thinking comes at a small cost in increased change scope , since SWE-1.7 reasons more, it also does more: writing additional test cases and touching more files than the task naively requires. Cognition acknowledges this is a known pattern across the industry: more reasoning tends to mean wider blast radius. It's an axis they say they're actively working to improve.
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