DeepSeek-V3.2 by DeepSeek

DeepSeek-V3.2 by DeepSeek: model card

What it is

DeepSeek-V3.2 is a text model from DeepSeek. It ranks #125 of 421 on its Arena board, which puts it in the middle of the list. The model answers in text: writes, reasons, works with code and documents.

Key numbers: Arena score 1425; votes 47 778; price $0.26 / $0.38 per 1M tokens (input / output); context 163.8K; licence MIT.

What this card does not contain: our own test runs, latency measurements, user reviews or taste judgements. Everything stated here comes from the public Arena.ai leaderboard and specs published by the developer. A missing parameter is marked as not listed rather than guessed.

Leaderboard numbers

• Rank: #125 of 421
• Arena score: 1425
• Votes in pairwise comparisons: 47778
• Price: $0.26 / $0.38 per 1M tokens (input / output), as listed on Arena
• Context window: 163.8K
• Licence: MIT

Who it is for

• Teams choosing a default model for a product and wanting the whole board, not just the top row.
• Anyone comparing price per token with rank, looking for the point where a cheaper model stops failing.

How to read these numbers

An Arena score comes from blind pairwise comparisons, not from an absolute quality scale. A few points between neighbouring rows is often inside the error bars, so the top of a board is better read as ‘roughly one tier’. Price, context and licence are stated specs and do not feed the score.

What to check yourself

• Whether the model is available in your region and whether access terms changed since the snapshot.
• Whether you really need the stated context: attention quality on long input is often lower.
• How price behaves on your prompt mix: the input/output ratio can matter more than the headline figure.

Source

• Text Arena leaderboard at Arena.ai
• Developer page: DeepSeek

Leaderboard snapshot: 11 November 2026.

_Prepared with AI assistance from the public Arena.ai leaderboard. This is not first-hand experience with the model and not an independent review._