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大模型 tokenstead.ai · @Hacker News

Cognition 发布 SWE-2:Terminal-Bench 2.1 自报 92.8

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Cognition 推出 SWE-2,基于 Kimi K3 做后训练,首次将 RL 扩展到万亿参数级,官方称在多数基准上再提升 5–6 分。模型为 MoE,总参 2.8T、每 token 激活 104B,采用 NVFP4/FP8 量化感知训练推理,SpecForge 重训草稿模型使接受长度增加 15%,prefill 延迟器提升每 GPU TPM 与单请求吞吐 10–20%(TTFT 变差)。自报成绩:FrontierCode 1.1 Main 50.0、DeepSWE 1.1 73.0、Terminal-Bench 2.1 92.8、Terminal-Bench 4.0 27.3;FrontierCode 落后 Claude Fable 5.1(50.9)1 分、落后 GPT-6 Astra(53.3)3.3 分,但声称成本低 64%。权重闭源,无本地运行与按 token API,现已在 Devin Desktop 和 CLI 上线,所有数据均为厂商自报、待独立验证。

AI推荐理由 Cognition 发布 SWE-2,基于 Kimi K3 后训练,官方自报 Terminal-Bench 2.1 达 92.8、FrontierCode 1.1 Main 50.0,并披露 MoE 规模(2.8T 总参、104B 激活)、NVFP4/FP8 量化推理与 SpecForge 草稿模型等工程细节,对 agentic coding 模型与推理栈有参考价值;但所有分数均为厂商自报、待独立复现,权重闭源不可本地运行,且 Terminal-Bench 4.0 仅 27.3 明显落后前沿,长程 agent 能力差距仍在。

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Models / SWE-2 SWE-2 MoE premier Cognition → Find for my hardware 2.8T total params, 104B active per token (MoE) - the Kimi K3 base with Cognition’s post-training on top, and the first time Cognition has scaled RL into the multi-trillion-parameter regime. The base had already been RL-heavy for agentic coding; Cognition’s pass added another 5 to 6 points on most benchmarks. Serving stack: MoE inference on NVFP4 and FP8 kernels with quantization-aware training; FP8 carries K, Q, V, and score computations in the MLA layers. A draft model retrained with SpecForge gives 15% longer accept lengths, and a prefill delayer lifts TPM per GPU and tokens/sec per request by 10 to 20% (TTFT takes the hit). Effort levels: mean steps per run 53 (medium), 80 (high), 98 (max), against 127 for SWE-1.7. Medium posts a higher FrontierCode score than SWE-1.7 with 58% fewer turns and 81% lower average cost, and lands its first real edit at a median of step 18 (SWE-1.7: 48). Benchmarks (Cognition self-reported): FrontierCode 1.1 Main 50.0, DeepSWE 1.1 73.0, Terminal-Bench 2.1 92.8, Terminal-Bench 4.0 27.3. The headline: 50.0 on FrontierCode is one point behind Claude Fable 5.1 (50.9) and 3.3 behind GPT-6 Astra (53.3) - at a claimed 64% lower cost than Fable 5.1 and a quarter of Astra’s. Terminal-Bench 2.1 is the highest number in the published table. The soft spot is Terminal-Bench 4.0, where SWE-2’s 27.3 trails Fable 5.1 (55.8) and GPT-6 Astra (57.9) by a wide margin - long-horizon agentic work is where the gap to the frontier still lives. Proprietary weights, no local run. Cognition has not published SWE-2 weights, so there is nothing to download and no quant ladder to wait for. It is available today in Devin Desktop and CLI, with rollout on Devin Web and Fusion. Cognition publishes no per-token API for SWE-2, so the cost-per-task comparisons (64% cheaper than Fable 5.1 at FrontierCode parity) are the pricing surface, not a $/1M rate card. Every figure here is Cognition’s own number, pending independent replication. coding agentic Parameters 2800.0B License proprietary Developer Cognition Origin 🇺🇸 USA Released Sep 2026 What people are building with SWE-2 Real demos from X Launch post: SWE-2, post-trained from Kimi K3 - FrontierCode 1.1 Main 50.0, Terminal-Bench 2.1 92.8 View on X → Benchmark scores Vendor-reported - from the developer's own model card / tech report DeepSWE 73.0 FrontierCode 1.1 Main 50.0 Terminal-Bench 2.1 92.8 Terminal-Bench 4.0 27.3 Vendor-reported - from the developer's own model card / tech report Or run it in the cloud No per-token API provider pricing tracked for SWE-2 yet. For flagship list prices, see the calculator. PRICE HISTORY Inference cost over time Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API. Loading price history...

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