想体验成本效益高的多模态模型？试试Qwen3.8-Flash，它基于Qwen4架构，参数庞大，支持1M上下文扩展，API定价低至0.16/1M输入令牌！
QwenCloud推出Qwen3.8-Flash多模态MoE模型，基于Qwen4架构，参数125B，成本效率高，支持1M上下文扩展。Qwen3.8-Flash-Next权重已开放，支持GDN+QSA混合注意力机制。API定价0.16/1M输入令牌和0.47/1M输出令牌。
API is live on QwenCloud: https://t.co/nz3wibv0Lv 🙌Let's build something!
API is live on QwenCloud: qwencloud.com/models/qwen3.8… 🙌Let's build something! Qwen @Alibaba_Qwen ⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The 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. 125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency. What's new: 🥳 - Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4. - 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. - 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). - 262K native context, extensible to 1M with YaRN. We’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.🚀 We can't wait to see what you build with Qwen3.8-Flash!👀👇 - Bl qwen.ai/blog?id=qwen3.… FLgJ - Technical Repo github.com/QwenLM/Qwen3.8… IkQO - Hugging Fa huggingface.co/Qwen/Qwen3.8-F… AABt - ModelSco modelscope.cn/models/Qwen/Qw… NuFG 🔗 View Quoted Tweet 💬 13 🔄 8 ❤️ 165 👀 16661 📊 23 ⚡