---
title: "DeepSeek V3.2"
url: "https://handyai.news/modeldrop/deepseek-v32"
published: "2025-12-01T17:00:00.000Z"
section: "Model Drop"
description: "DeepSeek V3.2, which DeepSeek shipped on December 1, 2025 as the official successor to the V3.2-Exp preview it released on September 29, is an open-weights…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"DeepSeek V3.2,\" December 1, 2025, https://handyai.news/modeldrop/deepseek-v32."
comments: "https://handyai.news/api/article/82360/comments"
---

# DeepSeek V3.2

*Published December 1, 2025 · Model Drop*

DeepSeek V3.2, which DeepSeek shipped on December 1, 2025 as the official successor to the V3.2-Exp preview it released on September 29, is an open-weights reasoning model built for agents and the first DeepSeek model that thinks while it calls tools.

- Model: DeepSeek V3.2 (`deepseek-ai/DeepSeek-V3.2`)
- Maker: DeepSeek
- Ship date: December 1, 2025
- Pricing: $0.28 / $0.42 per M tokens (DeepSeek API); free weights
- Context: 128K tokens
- Weights: Open (MIT)
- Where you can use it: DeepSeek chat, the apps and the API
- Open weights on Hugging Face

**When to reach for it**:

- Cheap agentic coding and tool use
- Self-hosting a strong MIT-licensed model
- Skip it for jobs that need over 128K tokens

**How it compares**:

- DeepSeek pitches it at GPT-5 level
- Open peers: Kimi K2 Thinking, MiniMax-M2

**What makes it different**:

- DeepSeek Sparse Attention for long inputs
- Thinks during tool calls, a DeepSeek first
- V3.2-Speciale sibling hit IMO gold level

**Early reactions**: Early sentiment was mixed, with some calling V3.2 "frontier at last" and others finding the chat experience underwhelming next to its benchmarks.

**Read more**: [DeepSeek-V3.2 Release](https://api-docs.deepseek.com/news/news251201), [technical report](https://huggingface.co/deepseek-ai/DeepSeek-V3.2/resolve/main/assets/paper.pdf), [model card](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)

## The benchmarks

DeepSeek's own runs of V3.2 in thinking mode (temperature 1.0, 128K context), against GPT-5 at high effort, Claude Sonnet 4.5, Gemini 3 Pro and Kimi K2 Thinking as DeepSeek printed them; Terminal-Bench 2.0 used the Claude Code harness, Humanity's Last Exam is the text-only subset, and BrowseComp uses DeepSeek's context management (51.4% without it).

#### DeepSeek's numbers

- SWE-bench Verified (real GitHub fixes): 73.1%. −4.1 under Claude Sonnet 4.5 (77.2%), −1.8 under GPT-5 (74.9%), −3.1 under Gemini 3 Pro (76.2%), +1.8 over Kimi K2 Thinking (71.3%).
- Terminal-Bench 2.0 (agentic terminal tasks): 46.4%. +3.6 over Claude Sonnet 4.5 (42.8%), +11.2 over GPT-5 (35.2%), −7.8 under Gemini 3 Pro (54.2%), +10.7 over Kimi K2 Thinking (35.7%).
- BrowseComp (hard web research): 67.6%. +12.7 over GPT-5 (54.9%), +43.5 over Claude Sonnet 4.5 (24.1%), +7.4 over Kimi K2 Thinking (60.2%).
- AIME 2025 (competition math): 93.1%. −1.5 under GPT-5 (94.6%), −1.9 under Gemini 3 Pro (95.0%), −1.4 under Kimi K2 Thinking (94.5%), +6.1 over Claude Sonnet 4.5 (87.0%).
- LiveCodeBench (fresh coding problems): 83.3%. −1.2 under GPT-5 (84.5%), −7.4 under Gemini 3 Pro (90.7%), +0.7 over Kimi K2 Thinking (82.6%).
- GPQA Diamond (PhD-level science): 82.4%. −3.3 under GPT-5 (85.7%), −9.5 under Gemini 3 Pro (91.9%), −2.1 under Kimi K2 Thinking (84.5%), −1.0 under Claude Sonnet 4.5 (83.4%).
- Humanity's Last Exam (no tools) (expert questions): 25.1%. −1.2 under GPT-5 (26.3%), −12.6 under Gemini 3 Pro (37.7%), +1.2 over Kimi K2 Thinking (23.9%), +11.4 over Claude Sonnet 4.5 (13.7%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2, AA estimate): 21.5. −0.5 under Kimi K2 Thinking (22.0), +2.9 over MiniMax-M2 (18.6), −1.5 under GPT-5 (23.0), +0.8 over Claude Sonnet 4.5 (20.7).


---

Source: https://handyai.news/modeldrop/deepseek-v32

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