# DeepSeek-V3.2

> GPT-5-level open-weight MoE that bakes thinking into tool-use, with DeepSeek Sparse Attention for cheap long context.

- Maker: DeepSeek
- Line: DeepSeek V3
- Canonical page: https://ai-tldr.dev/models/deepseek-v3-2/
- Updated: 2026-06-18

## In short

DeepSeek-V3.2 is the flagship model of DeepSeek's V3 line, released December 1, 2025. It is a Mixture-of-Experts model with 685B total parameters and roughly 37B active per token, published openly under the MIT license alongside an API-only high-compute sibling, DeepSeek-V3.2-Speciale. It has a 128K-token context window and costs $0.30 per million input tokens and $0.45 per million output tokens. You can use it through DeepSeek and OpenRouter.

## Facts

| Fact | Value | Source |
| --- | --- | --- |
| Context window | 128K (max output 64K) | [1] |
| Input price | $0.30 per 1M tokens (cached $0.135) | [2] |
| Output price | $0.45 per 1M tokens | [2] |
| Licence | MIT · open weights | [1] |
| Released | 1 December 2025 | [3] |
| Parameters | 685B total / 37B active (MoE) | [1] |
| Knowledge cutoff | Not disclosed | [1] |

## Sources

1. [DeepSeek-V3.2 — Hugging Face model card](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)
2. [DeepSeek V3.2 — Artificial Analysis](https://artificialanalysis.ai/models/deepseek-v3-2-reasoning)
3. [DeepSeek-V3.2 Release — API docs](https://api-docs.deepseek.com/news/news251201)

## Overview

DeepSeek-V3.2 is the flagship model of DeepSeek's V3 line, released December 1, 2025. It is a Mixture-of-Experts model with 685B total parameters and roughly 37B active per token, published openly under the MIT license alongside an API-only high-compute sibling, DeepSeek-V3.2-Speciale. DeepSeek positions V3.2 as a 'daily driver at GPT-5-level performance' while keeping inference cheap.

The headline technical change is DeepSeek Sparse Attention (DSA), the sparse-attention mechanism first trialled in V3.2-Exp and now productized: it sharply reduces the compute of long-context inference without measurably hurting quality. V3.2 is also DeepSeek's first model to integrate thinking directly into tool-use, and it supports tool calls in both thinking and non-thinking modes — trained with an agentic data-synthesis pipeline spanning 1,800+ environments and 85k+ complex instructions.

V3.2 ships on the DeepSeek app, web chat, and API, and its weights and technical report are public. It is the successor to DeepSeek-V3.2-Exp and the current top of the V3 series, sitting just below the later DeepSeek-V4 line.

## Benchmarks

- MMLU-Pro: 85% ([source](https://huggingface.co/deepseek-ai/DeepSeek-V3.2))
- GPQA-Diamond: 82.4% ([source](https://huggingface.co/deepseek-ai/DeepSeek-V3.2))
- SWE-bench Verified: 70% ([source](https://huggingface.co/deepseek-ai/DeepSeek-V3.2))
- Artificial Analysis Intelligence Index: 33% ([source](https://artificialanalysis.ai/models/deepseek-v3-2-reasoning))

## Pricing

- Input: $0.30 per 1M tokens
- Cached input: $0.135 per 1M tokens
- Output: $0.45 per 1M tokens

Reasoning (deepseek-reasoner) tier; cache-hit input is discounted ~55%. The non-thinking deepseek-chat tier is cheaper still. Prices reflect the Dec 2025 release.

Source: https://artificialanalysis.ai/models/deepseek-v3-2-reasoning

## Strengths

- Open weights under the permissive MIT license — fully self-hostable
- DeepSeek Sparse Attention makes long-context inference markedly cheaper than dense attention
- First DeepSeek model to fold reasoning directly into tool-use, in both thinking and non-thinking modes
- GPT-5-level general performance at a fraction of frontier-model API pricing
- Strong math, coding, and agentic results from training across 1,800+ environments

## Best for

- Cost-sensitive agentic workflows that need tool-calling plus reasoning
- Long-document and large-codebase tasks where sparse attention lowers cost
- Self-hosted deployments that require open weights and a permissive license
- Math and competitive-programming problem solving
- General assistant and coding workloads at GPT-5-class quality

## How to access

- [DeepSeek](https://api-docs.deepseek.com/news/news251201) — model id `deepseek-chat / deepseek-reasoner (DeepSeek-V3.2)`
- [OpenRouter](https://openrouter.ai/deepseek/deepseek-v3.2) — model id `deepseek/deepseek-v3.2`

## DeepSeek V3 versions

- DeepSeek-V3.2 (1 December 2025) — this page
- [DeepSeek-V3.2-Speciale](https://ai-tldr.dev/models/deepseek-v3-2-speciale/) (1 December 2025)
- [DeepSeek-V3.2-Exp](https://ai-tldr.dev/models/deepseek-v3-2-exp/) (29 September 2025)
- [DeepSeek-V3.1-Terminus](https://ai-tldr.dev/models/deepseek-v3-1-terminus/) (22 September 2025)
- [DeepSeek-V3.1](https://ai-tldr.dev/models/deepseek-v3-1/) (21 August 2025)
- [DeepSeek-V3-0324](https://ai-tldr.dev/models/deepseek-v3-0324/) (24 March 2025)
- [DeepSeek-V3](https://ai-tldr.dev/models/deepseek-v3/) (26 December 2024)
- [DeepSeek-V2.5](https://ai-tldr.dev/models/deepseek-v2-5/) (5 September 2024)
- [DeepSeek-V2](https://ai-tldr.dev/models/deepseek-v2/) (May 2024)

## FAQ

### Is DeepSeek-V3.2 open source?

The weights are released openly under the MIT license, so you can download, run, and fine-tune the model yourself. DeepSeek also published a technical report describing the architecture and training.

### What is DeepSeek Sparse Attention (DSA)?

DSA is the fine-grained sparse-attention mechanism at the core of V3.2. It substantially reduces the compute needed for long-context inference while preserving model quality, which is what lets V3.2 run long contexts cheaply compared with dense-attention models.

### How does V3.2 differ from V3.2-Speciale?

V3.2 is the balanced 'daily driver' positioned at GPT-5-level performance and available on the app, web, and API. V3.2-Speciale is a high-compute, API-only variant that maxes out reasoning (gold-medal-level results on the 2025 IMO and IOI) but uses far more tokens.

### Does V3.2 support tool use and reasoning together?

Yes. V3.2 is DeepSeek's first model to integrate thinking directly into tool-use, and it supports tool calls in both thinking and non-thinking modes. It was trained with an agentic data pipeline covering 1,800+ environments and 85k+ complex instructions.

## Links

- [DeepSeek — official site](https://www.deepseek.com)
- [DeepSeek-V3.2 — Hugging Face model card](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)
- [DeepSeek-V3.2 Release — API docs](https://api-docs.deepseek.com/news/news251201)
- [Models & Pricing — DeepSeek API docs](https://api-docs.deepseek.com/quick_start/pricing)
- [DeepSeek-V3.2: Pushing the Frontier of Open LLMs (arXiv)](https://arxiv.org/abs/2512.02556)
- [DeepSeek V3.2 — Artificial Analysis](https://artificialanalysis.ai/models/deepseek-v3-2-reasoning)
- [DeepSeek V3.2 — OpenRouter (API pricing)](https://openrouter.ai/deepseek/deepseek-v3.2)
- [DeepSeek Chat — playground](https://chat.deepseek.com)

Source: AI/TLDR LLM registry, https://ai-tldr.dev/models/deepseek-v3-2/