DeepSeek-Coder Base on MMMU: score, rank and what it means
DeepSeek-Coder Base scores 69.0 on MMMU, placing it #325 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-Coder Base 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-Coder Base sits at #325 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 17.9 points. The median model in this index scores 70.0, which puts DeepSeek-Coder Base below the midpoint.
DeepSeek models average 68.9 on this benchmark. DeepSeek-Coder Base is 0.1 points above that provider line, so it is a stronger result than the average DeepSeek model.
Among coding models, the average is 66.1. DeepSeek-Coder Base is 2.9 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 69.0 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 $3.53 per million input tokens, DeepSeek-Coder Base is below 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 |
|---|---|---|---|---|
| Reka Flash | Reka AI | 128000 | $9.95 | 74.1 |
| Claude Opus 4.5 Nano | Anthropic | 32768 | $9.38 | 73.3 |
| Llama 3 Modal Instruct | Modal | 128000 | $3.84 | 75.2 |
| DBRX Base | Databricks | 32768 | $2.63 | 69.1 |
| DeepSeek-Coder Base | DeepSeek | 32768 | $3.53 | 74.1 |
| OLMo 2 Chat | Allen Institute for AI | 16384 | $2.94 | 70.6 |
| Llama 3 Modal Base | Modal | 16384 | $3.21 | 72.2 |
Other benchmarks for DeepSeek-Coder Base
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 78.9 | 211 | 65th | |
| GPQA Diamond | 70.8 | 240 | 60th | |
| SWE-bench Verified | 59.3 | 337 | 44th | |
| HumanEval | 78.4 | 283 | 53th | |
| MATH-500 | 74.2 | 341 | 43th | |
| AIME 2025 | 71.7 | 223 | 63th | |
| LiveCodeBench | 67.3 | 242 | 60th | |
| IFEval | 81.6 | 282 | 53th | |
| TruthfulQA | 79.0 | 215 | 64th | |
| HellaSwag | 84.8 | 255 | 58th | |
| Arena Elo | 1388.5 | 255 | 58th |
FAQ
What score does DeepSeek-Coder Base get on MMMU?
DeepSeek-Coder Base scores 69.0 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.
How does DeepSeek-Coder Base rank on MMMU?
It ranks #325 of 600 models, in the 46th percentile. The leader is GPT-5.2 Max at 86.9.
Is DeepSeek-Coder Base good at MMMU?
It is below the median for this benchmark, so test it carefully before relying on it for this capability. 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-Coder Base cost?
DeepSeek-Coder Base costs $3.53 per million input tokens and $12.53 per million output tokens.