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  "text": "⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight!\n\nThe production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens.\n\n125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency.\n\nWhat's new: 🥳\n- Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4.\n- Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks.\n- Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI).\n- 262K native context, extensible to 1M with YaRN.\n\nWe’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀\n\nWe can't wait to see what you build with Qwen3.8-Flash!👀👇\n- Blog: https://t.co/M5hYypFLgJ\n- Technical Report: https://t.co/IF0gObIkQO\n- Hugging Face: https://t.co/6ow8QVAABt\n- ModelScope: https://t.co/tDOn2jNuFG",
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