Qwen(@Alibaba_Qwen)
LM Performance：With only 27B parameters, Qwen3.6-27B outperforms the Qwen3.5-397B-A17B (397B total /...
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TL;DR · AI 摘要
Qwen3.6-27B 参数量仅 27B，但在多个编码基准测试中超越更大规模模型。
核心要点
- 在 SWE-bench 和 Terminal-Bench 等基准中表现优异。
- 性能优于参数量大 15 倍的 Qwen3.5-397B-A17B。
- 适合高要求的代码生成与分析任务。
#Qwen#编码模型#AI性能
打开原文Qwen on X: "LM Performance：With only 27B parameters, Qwen3.6-27B outperforms the Qwen3.5-397B-A17B (397B total / 17B active, ~15x larger!) on every major coding benchmark — including SWE-bench Verified (77.2 vs. 76.2), SWE-bench Pro (53.5 vs. 50.9), Terminal-Bench 2.0 (59.3 vs. 52.5), and https://t.co/kPJKh8ablz" / X
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LM Performance：With only 27B parameters, Qwen3.6-27B outperforms the Qwen3.5-397B-A17B (397B total / 17B active, ~15x larger!) on every major coding benchmark — including SWE-bench Verified (77.2 vs. 76.2), SWE-bench Pro (53.5 vs. 50.9), Terminal-Bench 2.0 (59.3 vs. 52.5), and SkillsBench (48.2 vs. 30.0). It also surpasses all peer-scale dense models by a wide margin.
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