AI Agent Hub
Back to models
DeepSeek-V3.1-Terminus logo

DeepSeek-V3.1-Terminus

open-source deepseek-ai Released 2025-09-22
-- 163.8K context Open Source

About this model

DeepSeek-V3.1-Terminus

DeepSeek-V3

Introduction

This update maintains the model's original capabilities while addressing issues reported by users, including:

  • Language consistency: Reducing instances of mixed Chinese-English text and occasional abnormal characters;
  • Agent capabilities: Further optimizing the performance of the Code Agent and Search Agent.
Benchmark DeepSeek-V3.1 DeepSeek-V3.1-Terminus
Reasoning Mode w/o Tool Use
MMLU-Pro 84.8 85.0
GPQA-Diamond 80.1 80.7
Humanity's Last Exam 15.9 21.7
LiveCodeBench 74.8 74.9
Codeforces 2091 2046
Aider-Polyglot 76.3 76.1
Agentic Tool Use
BrowseComp 30.0 38.5
BrowseComp-zh 49.2 45.0
SimpleQA 93.4 96.8
SWE Verified 66.0 68.4
SWE-bench Multilingual 54.5 57.8
Terminal-bench 31.3 36.7

The template and tool-set of search agent have been updated, which is shown in assets/search_tool_trajectory.html.

How to Run Locally

The model structure of DeepSeek-V3.1-Terminus is the same as DeepSeek-V3. Please visit DeepSeek-V3 repo for more information about running this model locally.

For the model's chat template other than search agent, please refer to the DeepSeek-V3.1 repo.

Here we also provide an updated inference demo code in the inference folder to help the community get started with running our model and understand the details of model architecture.

NOTE: In the current model checkpoint, the parameters of self_attn.o_proj do not conform to the UE8M0 FP8 scale data format. This is a known issue and will be corrected in future model releases.

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 service@deepseek.com.

Benchmark Scores

HLE
15.9
GPQA
74.24
MMLU-Pro
84.8
SimpleQA
96.8
BrowseComp
30.0
CodeForces
2091.0
GPQA-Diamond
80.1
LiveCodeBench
74.8
SWE-Bench-Multilingual
54.5

Technical Specs

  • Architecture: transformers
  • Context Window: 163,840 tokens
  • Input Modalities: text

Hardware Requirements

  • API-only (no local hardware needed)

Pricing

Input Output Currency
0.27 / 1M tokens 1.00 / 1M tokens USD