MiniMax M3.1 Flash 联手 SGLang Omni 打造可打断语音智能体

我们刚刚在 SGLang Omni 级联语音交互系统中试用了 @MiniMax_AI 的 M3.1 Flash，体验确实令人印象深刻！🐰🚀 我们原本的管线是 Qwen3-ASR → Qwen3 LLM → Qwen3-TTS。我们将本地部署的 LLM 替换为 MiniMax M3.1 Flash API，其余管线保持不变，并录制了一段演示，看看它在真实对话中的表现。 结果非常出色。M3.1 Flash 响应速度极快，很好地理解了我们的问题，并始终给出犀利、简洁、直接的答案。对话感觉非常流畅自然。 关于语音智能体，我越来越体会到的一点是：仅有智能是不够的。与文本不同，用户无法轻松浏览音频回复。一个能理解你的需求并直奔主题的模型，可以对整体交互体验产生巨大影响。 这也正是 SGLang Omni 的模块化架构让我如此兴奋的原因。我们可以替换单个组件，将本地语音模型与前沿 LLM API 连接起来，并立即探索不同模型如何改变端到端的交互体验。 向 MiniMax 团队致以热烈祝贺！工作确实令人印象深刻，我们也很期待一起探索更多。🔥

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We just tried @MiniMax_AI's M3.1 Flash in our SGLang Omni cascaded voice interaction system, and the experience was genuinely impressive! 🐰🚀 Our original pipeline was Qwen3-ASR → Qwen3 LLM → Qwen3-TTS. We replaced the locally hosted LLM with the MiniMax M3.1 Flash API while keeping the rest of the pipeline unchanged, and recorded a demo to see how it performs in real conversations. The results were excellent. M3.1 Flash responded incredibly fast, understood our questions well, and consistently delivered sharp, concise, and direct answers. The conversation felt remarkably smooth and natural. One thing I increasingly appreciate about voice agents is that intelligence alone isn't enough. Unlike text, users can't easily skim an audio response. A model that understands what you need and gets straight to the point can make a huge difference to the overall interaction experience. This is also what makes the modular architecture of SGLang Omni so exciting to me. We can swap individual components, connect local speech models with frontier LLM APIs, and immediately explore how different models change the end-to-end interaction experience. Huge congrats to the MiniMax team! Really impressive work, and we're excited to explore more together. 🔥