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DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config) The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. It succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.
| 信号 | 强度 | 权重 | 影响 |
|---|---|---|---|
| Benchmarksjust now | 69 | 30% | +20.8 |
| Recencyjust now | 93 | 15% | +14.0 |
| Capabilitiesjust now | 67 | 20% | +13.3 |
| Context Windowjust now | 72 | 10% | +7.2 |
| Output Capacityjust now | 64 | 10% | +6.4 |
| Pricingjust now | 1 | 15% | +0.1 |
社区和从业者反馈在基准测试和价格之上增加了真实世界的信号。
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成本估算器
每月比类别平均节省$39.24
来自已验证的来源。