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Cognition's SWE-2 model scores 92.8 on Terminal-Bench 2.1, trails on longer tasks

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GoKawiil Brief

Cognition released SWE-2, a coding model built by post-training on a 2.8-trillion-parameter Kimi K3 base with 104B active parameters, marking its first RL push into the multi-trillion-parameter range. The model posts a class-leading 92.8 on Terminal-Bench 2.1 and competitive FrontierCode scores at lower cost than rivals like Claude Fable 5.1 and GPT-6 Astra, but falls well behind both on the harder Terminal-Bench 4.0 benchmark (27.3 vs 55.8 and 57.9). SWE-2 is closed-weight and available now through Devin Desktop and CLI, with no local deployment option.

Why It Matters

GoKawiil's interpretation of the reporting above, not reported fact.

The results suggest Cognition can match or beat frontier labs on short-horizon coding tasks while running cheaper, thanks to serving optimizations like FP8/NVFP4 quantization and speculative decoding tweaks. But the steep drop-off on long-horizon agentic benchmarks shows that scaling RL on a big MoE base hasn't closed the gap on sustained, multi-step reasoning, which remains the harder problem for autonomous coding agents. Keeping weights proprietary also means enterprises evaluating SWE-2 must rely entirely on Cognition's hosted product rather than independent verification or self-hosting.

Key Takeaways
Worth a Look

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Source: tokenstead.ai, 2026-09-10

Published there as: “Cognition's SWE-2 achieves 92.8 on Terminal-Bench 2.1”

Read the original report → The summary and analysis above are GoKawiil's own, written from reporting by the source above. Facts and quotes belong to the original publisher.