DeepSeek-V3.2 Chat on MMMU: score, rank and what it means
DeepSeek-V3.2 Chat scores 76.0 on MMMU, placing it #140 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.
Score distribution
The chart shows the top 10 models on MMMU, with DeepSeek-V3.2 Chat highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 4 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 5 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 6 | Grok 4 Pro | xAI | 1000000 | $19.00 | 85.4 |
| 7 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 8 | Claude Sonnet 4.5 Pro | Anthropic | 1000000 | $18.73 | 86.7 |
| 9 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 10 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
What this score means
On MMMU, DeepSeek-V3.2 Chat sits at #140 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 10.9 points. The median model in this index scores 70.0, which puts DeepSeek-V3.2 Chat above the midpoint.
DeepSeek models average 68.9 on this benchmark. DeepSeek-V3.2 Chat is 7.1 points above that provider line, so it is a stronger result than the average DeepSeek model.
Among multimodal models, the average is 74.3. DeepSeek-V3.2 Chat is 1.7 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 76.0 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $5.80 per million input tokens, DeepSeek-V3.2 Chat is above the median input price of $4.00 across the 600 models scored on MMMU. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Nemotron 4 | NVIDIA | 65536 | $4.18 | 76.6 |
| Sonar Pro Reasoning | Perplexity | 128000 | $13.94 | 80.5 |
| Yi-Lightning | 01.AI | 32768 | $4.22 | 77.2 |
| Codestral | Mistral AI | 65536 | $4.19 | 77.6 |
| DeepSeek-V3.2 Chat | DeepSeek | 1000000 | $5.80 | 82.5 |
| Qwen3 Base | Alibaba Cloud | 65536 | $4.33 | 77.4 |
| Mistral Large 3 Base | Mistral AI | 65536 | $4.47 | 78.2 |
Other benchmarks for DeepSeek-V3.2 Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 82.5 | 114 | 81th | |
| GPQA Diamond | 78.0 | 85 | 86th | |
| SWE-bench Verified | 69.6 | 120 | 80th | |
| HumanEval | 87.9 | 74 | 88th | |
| MATH-500 | 90.2 | 46 | 92th | |
| AIME 2025 | 81.0 | 75 | 88th | |
| LiveCodeBench | 77.9 | 65 | 89th | |
| IFEval | 88.6 | 91 | 85th | |
| TruthfulQA | 86.3 | 45 | 93th | |
| HellaSwag | 89.2 | 107 | 82th | |
| Arena Elo | 1462.2 | 48 | 92th |
FAQ
What score does DeepSeek-V3.2 Chat get on MMMU?
DeepSeek-V3.2 Chat scores 76.0 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.
How does DeepSeek-V3.2 Chat rank on MMMU?
It ranks #140 of 600 models, in the 77th percentile. The leader is GPT-5.2 Max at 86.9.
Is DeepSeek-V3.2 Chat good at MMMU?
Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.
How is the MMMU score calculated?
MMMU is a multimodal benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does DeepSeek-V3.2 Chat cost?
DeepSeek-V3.2 Chat costs $5.80 per million input tokens and $26.43 per million output tokens.