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README.md
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---
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license: mit
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library_name: transformers
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---
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# DeepSeek-V3.1
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DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects:
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- **Hybrid thinking mode**: One model supports both thinking mode and non-thinking mode by changing the chat template.
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- **Smarter tool calling**: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
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Prefix:
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`<|begin▁of▁sentence|>{system prompt}<|User|>{query}<|Assistant|><think>`
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The prefix of thinking mode is similar to DeepSeek-R1.
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#### Multi-Turn
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Prefix:
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`<|User|>{query}<|Assistant|><think>`
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The multi-turn template is the same with non-thinking multi-turn chat template. It means the thinking token in the last turn will be dropped but the `</think>` is retained in every turn of context.
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### ToolCall
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Toolcall is supported in non-thinking mode. The format is:
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`<|begin▁of▁sentence|>{system prompt}{tool_description}<|User|>{query}<|Assistant|></think>` where the tool_description is
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```
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## Tools
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We support various code agent frameworks. Please refer to the above toolcall format to create your own code agents. An example is shown in `assets/code_agent_trajectory.html`.
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### Search-Agent
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We design a specific format for searching toolcall in thinking mode, to support search agent.
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For complex questions that require accessing external or up-to-date information, DeepSeek-V3.1 can leverage a user-provided search tool through a multi-turn tool-calling process.
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| | MMLU-Pro (EM) | 83.7 | 81.2 | 84.8 | 85.0
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| | GPQA-Diamond (Pass@1) | 74.9 | 68.4 | 80.1 | 81.0
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| | Humanity's Last Exam (Pass@1) | - | - | 15.9 | 17.7
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|Search Agent|
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| | BrowseComp | - | - | 30.0 | 8.9
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| | BrowseComp_zh | - | - | 49.2 | 35.7
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| | Humanity's Last Exam (Python + Search) |- | - | 29.8 | 24.8
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| | AIME 2025 (Pass@1) | 49.8 | 51.3 | 88.4 | 87.5
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| | HMMT 2025 (Pass@1) | 33.5 | 29.2 | 84.2 | 79.4 |
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Note:
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- Search agents are evaluated with our internal search framework, which uses a commercial search API + webpage filter + 128K context window. Seach agent results of R1-0528 are evaluated with a pre-defined workflow.
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- SWE-bench is evaluated with our internal code agent framework.
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```
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@misc{deepseekai2024deepseekv3technicalreport,
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title={DeepSeek-V3 Technical Report},
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author={DeepSeek-AI},
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year={2024},
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eprint={2412.19437},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2412.19437},
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}
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```
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---
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license: mit
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library_name: transformers
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base_model: deepseek-ai/DeepSeek-V3.1
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---
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# DeepSeek-V3.1
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DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects:
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|
| 50 |
+
- **Hybrid thinking mode**: One model supports both thinking mode and non-thinking mode by changing the chat template.
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- **Smarter tool calling**: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
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|
|
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Prefix:
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`<|begin▁of▁sentence|>{system prompt}<|User|>{query}<|Assistant|><think>`
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+
The prefix of thinking mode is similar to DeepSeek-R1.
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#### Multi-Turn
|
|
|
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Prefix:
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`<|User|>{query}<|Assistant|><think>`
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+
The multi-turn template is the same with non-thinking multi-turn chat template. It means the thinking token in the last turn will be dropped but the `</think>` is retained in every turn of context.
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### ToolCall
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+
Toolcall is supported in non-thinking mode. The format is:
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+
`<|begin▁of▁sentence|>{system prompt}{tool_description}<|User|>{query}<|Assistant|></think>` where the tool_description is
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```
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## Tools
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We support various code agent frameworks. Please refer to the above toolcall format to create your own code agents. An example is shown in `assets/code_agent_trajectory.html`.
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### Search-Agent
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+
We design a specific format for searching toolcall in thinking mode, to support search agent.
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| 137 |
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For complex questions that require accessing external or up-to-date information, DeepSeek-V3.1 can leverage a user-provided search tool through a multi-turn tool-calling process.
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|
|
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| | MMLU-Pro (EM) | 83.7 | 81.2 | 84.8 | 85.0
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| 148 |
| | GPQA-Diamond (Pass@1) | 74.9 | 68.4 | 80.1 | 81.0
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| 149 |
| | Humanity's Last Exam (Pass@1) | - | - | 15.9 | 17.7
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+
|Search Agent|
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| | BrowseComp | - | - | 30.0 | 8.9
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| | BrowseComp_zh | - | - | 49.2 | 35.7
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| | Humanity's Last Exam (Python + Search) |- | - | 29.8 | 24.8
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| | AIME 2025 (Pass@1) | 49.8 | 51.3 | 88.4 | 87.5
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| | HMMT 2025 (Pass@1) | 33.5 | 29.2 | 84.2 | 79.4 |
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Note:
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- Search agents are evaluated with our internal search framework, which uses a commercial search API + webpage filter + 128K context window. Seach agent results of R1-0528 are evaluated with a pre-defined workflow.
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- SWE-bench is evaluated with our internal code agent framework.
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```
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@misc{deepseekai2024deepseekv3technicalreport,
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title={DeepSeek-V3 Technical Report},
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author={DeepSeek-AI},
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year={2024},
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eprint={2412.19437},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2412.19437},
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}
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```
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