GoKawiilSWE-1.7: Frontier Intelligence at a Fraction of the Cost Ben Pan*, Carlo Baronio*, Rohan Choudhury, Eric Lu, Ryan Kim, Deniz Birlikci, TC Qin, Sam Lee, Fermi Ma, Allen Liu, Yang Liu, Sampriti Panda, Jacob Teo, Ray Wang, Gary Chang, Steven Cao, Silas Alberti *equal contribution 07.08.26
Today, we’re launching SWE-1.7, the most capable model we’ve trained so far. It reaches frontier-level intelligence at a much lower cost, advancing the cost-performance Pareto curve. SWE-1.7 is the result of broad improvements across our RL pipeline: better infrastructure, more stable training, higher-quality data, and new techniques for long-horizon tasks. Since SWE-1.7 was trained from a Kimi K2.7 base, which had already undergone extensive RL post-training, the large additional gains from our own training challenge the idea of a ‘post-training ceiling’ and suggest that RL can push capabilities much further than previously believed.
At Cognition, we have been formulating and refining principles for good agentic software engineering both in evaluation, with FrontierCode1,2, and now in training, with SWE-1.7. Our model is particularly optimized for longer-horizon asynchronous tasks, an important component of high-quality software engineering. SWE-1.7 is available today in Devin (Web, Desktop, and CLI) via Cerebras at 1000 TPS. We encourage you to try it for yourself!
Coding benchmark results Pass rate (%) on agentic coding benchmarks. Benchmark SWE-1.7 Kimi K2.7 Code GPT-5.5 Opus 4.8 Opus 4.7 GLM-5.2 Composer 2.5 SWE-1.6 FrontierCode 1.1 Main 42.3 % 30.1 % 43.0 % 46.5 % 38.5 % 24.5 % 25.6 % 9.4 % Terminal-Bench 2.1 81.5 % 72.7 % 84.2 % 86.9 % 83.0 % 81.0 % 76.0 % 39.7 % SWE-Bench Multilingual 77.8 % 73.5 % 76.8 % 84.4 % 80.5 % 74.5 % 71.6 % 58.3 %
The rest of this post covers how we trained SWE-1.7: the infrastructure, algorithms, and data work behind our model. We cover four important components that stand out. Preserving entropy and stabilizing training: Long RL runs face two challenging problems: entropy collapse, and instability due to numerical drift between training and inference. We hunted down and addressed causes of each, which enabled training to keep improving well past where earlier runs stalled.
Multi-cluster training and fault tolerance: RL doesn’t need all of its inference compute in one cluster. We trained on clusters across three continents, shipped weight updates through object storage, and built fault tolerance so that hardware failures never stalled the run.
Curating high-quality data: We built an extensive data-quality pipeline that runs each task through automated execution tests, filters out tasks with low learning signal, and hardens tasks to prevent reward-hacking.
Self-compaction for long-horizon tasks: The model learns to summarize its working state and resume from the summary, extending task horizons past the raw context window. We use an alternating length penalty to incentivize concise output without sacrificing correctness. Finally, we conclude by sharing some observations on interesting behavioral tendencies, such as careful exploration and concise reasoning, that the model acquired as a result of our training setup.
Preserving Entropy and Stabilizing Training # We found training stability to be a key contributor to predictable improvement at scale. When training with asynchronous RL3, one of the most problematic issues we encountered was the KL divergence mismatch between inference and training4, since the trainer policy is usually different from the sampling policy. In the past, to correct for this (albeit at smaller scale), we used importance-sampling5 and quantization-aware training for low-precision rollouts in NVFP4 + experts routing replay6,7. Here we present additional interventions that become more important at larger scale. We find that top-p sampling8 contributes significantly to staving off entropy collapse9,10, where a strong model stops exploring and reward plateaus within a few hundred steps. Very low probability tokens are often part of trajectories that have gone off track or out of distribution. These trajectories are likely to produce low reward, and properties of the softmax function lead to these tokens sharpening the token probability distribution. Indeed, suppose we have three tokens with logits x 1 > x 2 ≫ x 3 x_1>x_2\gg x_3 x1>x2≫x3 and probabilities p i = e x i e x 1 + e x 2 + e x 3 p_i = \frac{e^{x_i}}{e^{x_1} + e^{x_2} + e^{x_3}} pi=ex1+ex2+ex3exi, where token 3 is a low probability token that leads to low reward. If we sample token 3, the gradient of its logprob with respect to the logits x 1 , x 2 , x 3 x_1, x_2, x_3 x1,x2,x3 is: ∇ log p 3 = ∇ log [ e x 3 e x 1 + e x 2 + e x 3 ] = [ − p 1 − p 2 p 1 + p 2 ]
abla \log p_3=
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