DeepSeek-V3.1
Introduction
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:
Hybrid thinking mode: One model supports both thinking mode and non-thinking mode by changing the chat template.
Smarter tool calling: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
Higher thinking efficiency: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.
DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format to ensure compatibility with microscaling data formats.
Model Downloads
| Model | #Total Params | #Activated Params | Context Length | Download |
|---|---|---|---|---|
| DeepSeek-V3.1-Base | 671B | 37B | 128K | HuggingFace | ModelScope |
| DeepSeek-V3.1 | 671B | 37B | 128K | HuggingFace | ModelScope |
Chat Template
The details of our chat template is described in tokenizer_config.json and assets/chat_template.jinja. Here is a brief description.
Non-Thinking
First-Turn
Prefix:
<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>
With the given prefix, DeepSeek V3.1 generates responses to queries in non-thinking mode. Unlike DeepSeek V3, it introduces an additional token </think>.
Multi-Turn
Context:
<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>
Prefix:
<｜User｜>{query}<｜Assistant｜></think>
By concatenating the context and the prefix, we obtain the correct prompt for the query.
Thinking
First-Turn
Prefix:
<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜><think>
The prefix of thinking mode is similar to DeepSeek-R1.
Multi-Turn
Context:
<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>
Prefix:
<｜User｜>{query}<｜Assistant｜><think>
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.
ToolCall
Toolcall is supported in non-thinking mode. The format is:
<｜begin▁of▁sentence｜>{system prompt}{tool_description}<｜User｜>{query}<｜Assistant｜></think> where the tool_description is
## Tools
You have access to the following tools:
### {tool_name1}
Description: {description}
Parameters: {json.dumps(parameters)}
IMPORTANT: ALWAYS adhere to this exact format for tool use:
<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>tool_call_name<｜tool▁sep｜>tool_call_arguments<｜tool▁call▁end｜>{{additional_tool_calls}}<｜tool▁calls▁end｜>
Where:
- `tool_call_name` must be an exact match to one of the available tools
- `tool_call_arguments` must be valid JSON that strictly follows the tool's Parameters Schema
- For multiple tool calls, chain them directly without separators or spaces
Code-Agent
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.
Search-Agent
We design a specific format for searching toolcall in thinking mode, to support search agent.
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.
Please refer to the assets/search_tool_trajectory.html and assets/search_python_tool_trajectory.html for the detailed template.
Evaluation
| Category | Benchmark (Metric) | DeepSeek V3.1-NonThinking | DeepSeek V3 0324 | DeepSeek V3.1-Thinking | DeepSeek R1 0528 |
|---|---|---|---|---|---|
| General | |||||
| MMLU-Redux (EM) | 91.8 | 90.5 | 93.7 | 93.4 | |
| MMLU-Pro (EM) | 83.7 | 81.2 | 84.8 | 85.0 | |
| GPQA-Diamond (Pass@1) | 74.9 | 68.4 | 80.1 | 81.0 | |
| Humanity's Last Exam (Pass@1) | - | - | 15.9 | 17.7 | |
| Search Agent | |||||
| BrowseComp | - | - | 30.0 | 8.9 | |
| BrowseComp_zh | - | - | 49.2 | 35.7 | |
| Humanity's Last Exam (Python + Search) | - | - | 29.8 | 24.8 | |
| SimpleQA | - | - | 93.4 | 92.3 | |
| Code | |||||
| LiveCodeBench (2408-2505) (Pass@1) | 56.4 | 43.0 | 74.8 | 73.3 | |
| Codeforces-Div1 (Rating) | - | - | 2091 | 1930 | |
| Aider-Polyglot (Acc.) | 68.4 | 55.1 | 76.3 | 71.6 | |
| Code Agent | |||||
| SWE Verified (Agent mode) | 66.0 | 45.4 | - | 44.6 | |
| SWE-bench Multilingual (Agent mode) | 54.5 | 29.3 | - | 30.5 | |
| Terminal-bench (Terminus 1 framework) | 31.3 | 13.3 | - | 5.7 | |
| Math | |||||
| AIME 2024 (Pass@1) | 66.3 | 59.4 | 93.1 | 91.4 | |
| AIME 2025 (Pass@1) | 49.8 | 51.3 | 88.4 | 87.5 | |
| HMMT 2025 (Pass@1) | 33.5 | 29.2 | 84.2 | 79.4 |
Note:
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.
SWE-bench is evaluated with our internal code agent framework.
HLE is evaluated with the text-only subset.
Usage Example
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1")
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Who are you?"},
{"role": "assistant", "content": "<think>Hmm</think>I am DeepSeek"},
{"role": "user", "content": "1+1=?"}
]
tokenizer.apply_chat_template(messages, tokenize=False, thinking=True, add_generation_prompt=True)
# '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜><think>'
tokenizer.apply_chat_template(messages, tokenize=False, thinking=False, add_generation_prompt=True)
# '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜></think>'
How to Run Locally
The model structure of DeepSeek-V3.1 is the same as DeepSeek-V3. Please visit DeepSeek-V3 repo for more information about running this model locally.
License
This repository and the model weights are licensed under the MIT License.
Citation
@misc{deepseekai2024deepseekv3technicalreport,
title={DeepSeek-V3 Technical Report},
author={DeepSeek-AI},
year={2024},
eprint={2412.19437},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.19437},
}
Contact
If you have any questions, please raise an issue or contact us at [email protected].
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