@rohanpaul_ai: Alibaba dropped the weights for Qwen3.8-27B as a 27B open-weight multimodal model built for local deployment. - Apache …

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Summary

Alibaba released Qwen3.8-27B, a 27B open-weight multimodal model for local deployment, which shows frontier-class performance on coding benchmarks like SWE-bench Pro and OSWorld, outperforming some larger models.

Alibaba dropped the weights for Qwen3.8-27B as a 27B open-weight multimodal model built for local deployment. - Apache 2.0 weights and support for Transformers, vLLM, SGLang, and local quantizations, Qwen3.8-27B puts unusually capable multimodal agent work within single-machine deployment range. - It has 262k tokens of native context, extendable to 1M with YaRN, while reasoning can be disabled or adjusted per request. - AMD says Qwen3.8-27B reached up to 51.8 tokens/sec in its initial testing on a single Radeon AI PRO R9700; roughly 24GB VRAM - For coding, Qwen3.8-27B surprisingly close to, and sometimes above, Claude Opus 4.6 Max: SWE-bench Pro 61.7 vs 53.4, CoWorkBench 70.7 vs 68.2, and OSWorld 84.3 vs 72.7. So this 27B local model is legitimately frontier-class on several coding/agent benchmarks
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Cached at: 08/15/26, 03:44 AM

Alibaba dropped the weights for Qwen3.8-27B as a 27B open-weight multimodal model built for local deployment.

  • Apache 2.0 weights and support for Transformers, vLLM, SGLang, and local quantizations, Qwen3.8-27B puts unusually capable multimodal agent work within single-machine deployment range.

  • It has 262k tokens of native context, extendable to 1M with YaRN, while reasoning can be disabled or adjusted per request.

  • AMD says Qwen3.8-27B reached up to 51.8 tokens/sec in its initial testing on a single Radeon AI PRO R9700; roughly 24GB VRAM

  • For coding, Qwen3.8-27B surprisingly close to, and sometimes above, Claude Opus 4.6 Max: SWE-bench Pro 61.7 vs 53.4, CoWorkBench 70.7 vs 68.2, and OSWorld 84.3 vs 72.7.

So this 27B local model is legitimately frontier-class on several coding/agent benchmarks

Qwen (@Alibaba_Qwen): We promised open weights for Qwen3.8. Now, time to meet them! 🎉

⚡ Qwen3.8-27B:

  • A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows.
  • 262K native context, easily extendable to 1M

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