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 on both model weights and activations to ensure compatibility with microscaling data formats. Please refer to DeepGEMM for more details.
With the given prefix, DeepSeek V3.1 generates responses to queries in non-thinking mode. Unlike DeepSeek V3, it introduces an additional token </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}\n\n{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
python
1import transformers
23tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1")45messages =[6{"role":"system","content":"You are a helpful assistant"},7{"role":"user","content":"Who are you?"},8{"role":"assistant","content":"<think>Hmm</think>I am DeepSeek"},9{"role":"user","content":"1+1=?"}10]1112tokenizer.apply_chat_template(messages, tokenize=False, thinking=True, add_generation_prompt=True)13# '<｜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>'1415tokenizer.apply_chat_template(messages, tokenize=False, thinking=False, add_generation_prompt=True)16# '<｜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.
Usage Recommendations:
The mlp.gate.e_score_correction_bias parameters should be loaded and computed in FP32 precision.
Ensure that FP8 model weights and activations are formatted using the UE8M0 scale format.
License
This repository and the model weights are licensed under the MIT License.
