# DeepSeek-V3.2

DeepSeek-V3.2 is an AI model released by DeepSeek on Dec 1 2025. It has open weight. At release it scored 13% on BullshitBench v2.

## Facts

| Field | Value |
| --- | --- |
| Model | DeepSeek-V3.2 |
| Developer | DeepSeek |
| Release date | Monday, Dec 1 2025 |
| Licensing | Open Weight |

## Benchmark scores published at release

| Benchmark | Score | Source | What it measures |
| --- | --- | --- | --- |
| BullshitBench v2 | 13% | [BullshitBench](https://github.com/petergpt/bullshit-benchmark) | Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. |

## Questions and answers

### When was DeepSeek-V3.2 released?

DeepSeek-V3.2 was released by DeepSeek on Monday, Dec 1 2025.

### Who made DeepSeek-V3.2?

DeepSeek-V3.2 was built by DeepSeek. Chinese AI lab known for efficient, open-weight models. Gained attention for strong performance at lower cost.

### What benchmark scores did DeepSeek-V3.2 get?

DeepSeek-V3.2 reports 1 tracked benchmark score — BullshitBench v2: 13%. Scores are the figures published at release by DeepSeek.

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

Partly. DeepSeek-V3.2 is an open-weight model: the trained weights are free to download and run locally or on your own infrastructure, but the training data and code are not fully released and the license may restrict some commercial uses. It is not open source in the strict sense.

### What came before and after DeepSeek-V3.2?

DeepSeek's previous tracked release was DeepSeek V3.2 Exp on Sep 29 2025, 63 days earlier. It was followed by DeepSeek-V4-Pro on Apr 24 2026.


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Canonical page: https://aireleasetracker.com/model/deepseek/deepseek-v3.2
Full dataset: https://aireleasetracker.com/llms-full.txt · JSON: https://aireleasetracker.com/models.json
Source: AI Release Tracker (https://aireleasetracker.com). Most benchmark scores come from lab launch material; gathered results identify the leaderboard that published them.