DeepSeek-V3.2: Efficient Reasoning & Agentic AI

Introduction

We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance. Our approach is built upon three key technical breakthroughs:

1. DeepSeek Sparse Attention (DSA): We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios.
2. Scalable Reinforcement Learning Framework: By implementing a robust RL protocol and scaling post-training compute, DeepSeek-V3.2 performs comparably to GPT-5. Notably, our high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro.
   • Achievement: 🥇 Gold-medal performance in the 2025 International Mathematical Olympiad (IMO) and International Olympiad in Informatics (IOI).
3. Large-Scale Agentic Task Synthesis Pipeline: To integrate reasoning into tool-use scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This facilitates scalable agentic post-training, improving compliance and generalization in complex interactive environments.

We have also released the final submissions for IOI 2025, ICPC World Finals, IMO 2025 and CMO 2025, which were selected based on our designed pipeline. These materials are provided for the community to conduct secondary verification. The files can be accessed at assets/olympiad_cases.

Chat Template

DeepSeek-V3.2 introduces significant updates to its chat template compared to prior versions. The primary changes involve a revised format for tool calling and the introduction of a "thinking with tools" capability.

To assist the community in understanding and adapting to this new template, we have provided a dedicated encoding folder, which contains Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model and how to parse the model's text output.

A brief example is illustrated below:

import transformers
# encoding/encoding_dsv32.py
from encoding_dsv32 import encode_messages, parse_message_from_completion_text

tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.2")

messages = [
    {"role": "user", "content": "hello"},
    {"role": "assistant", "content": "Hello! I am DeepSeek.", "reasoning_content": "thinking..."},
    {"role": "user", "content": "1+1=?"}
]
encode_config = dict(thinking_mode="thinking", drop_thinking=True, add_default_bos_token=True)

# messages -> string
prompt = encode_messages(messages, **encode_config)
# Output: "<｜begin▁of▁sentence｜><｜User｜>hello<｜Assistant｜></think>Hello! I am DeepSeek.<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜><think>"

# string -> tokens
tokens = tokenizer.encode(prompt)
# Output: [0, 128803, 33310, 128804, 128799, 19923, 3, 342, 1030, 22651, 4374, 1465, 16, 1, 128803, 19, 13, 19, 127252, 128804, 128798]