微软发布 Decision-1 决策模型，基于 Qwen3.5-9B
Microsoft's Decision-1 model enters the fast-growing AI decision model race
- 1微软发布决策模型 Decision-1，基于 Qwen3.5-9B，面向分类、评估和路由等快速结构化决策。
- 2官方称在 36 项基准、近 15 万道题的测试中准确率居首，达 83.5%，延迟 85 ms（官方口径，暂无独立验证）。
- 3官方称速度比第二名 H2O-Lightning-4B 快 2.5 倍，Cloudflare 基于 Qwen 的 Clef 模型未纳入对比。
- 4Decision-1 已通过 Microsoft Foundry 和 OpenRouter 提供，输入 token 每百万 $0.042，输出免费。
微软用开源小模型快速复制 Jev 带火的决策模型品类，低门槛让先发者窗口很短，比拼集中在低延迟低价的高频决策调用。
- 影响：输出免费、输入每百万 $0.042 的定价明显压低分类和路由类高频调用的成本；开源小模型路线说明该品类容易被复刻，换供应商的门槛不高；官方性能数字缺少独立验证，选型前需自测。
- 参考：可考虑把智能体链路里的路由、分类环节列为试点，用自有数据对比 Decision-1 与 Jev 1.13.0 的准确率和延迟；值得按输入计费、输出免费的口径重新测算现有调用预算，再决定是否迁移。
- 依据：准确率 83.5%、延迟 85 ms、快 2.5 倍均出自微软自家基准对比，对比中领先 Jev 1.13.0，属官方口径暂无独立验证，Cloudflare 的 Clef 模型未纳入。
Microsoft now has its own decision model. Like the ones from OpenAI, Cloudflare, and the "original" from Jev, Decision-1 is built for fast, structured decisions. According to Microsoft, it handles classifications, evaluations, and routing decisions and has "the potential" to "guide and control agents through complex environments."
Decision-1 is based on Qwen3.5-9B. Microsoft says it's the most accurate model tested across 36 benchmarks covering nearly 150,000 questions. On speed, it's 2.5 times faster than the runner-up, H2O-Lightning-4B. Cloudflare's open-source Clef models, which are also based on Qwen, weren't included in the comparison.
Decision-1 is available through Microsoft Foundry and OpenRouter. Input tokens cost $0.042 per million, and output tokens are free. Jev, the startup that kicked off the decision model trend in mid-September, has likely had a rough few weeks. The idea caught on fast, but the tech was quickly adapted and surpassed using open small language models.
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来源：The Decoder：AI News · the-decoder.com