DeepSeek-R1 1.5B on MMLU-Pro: score, rank and what it means
DeepSeek-R1 1.5B scores 60.7 on MMLU-Pro, placing it #518 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.
Score distribution
The chart shows the top 10 models on MMLU-Pro, with DeepSeek-R1 1.5B highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 2 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 3 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 4 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 5 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 6 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 7 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 8 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 9 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 10 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
What this score means
On MMLU-Pro, DeepSeek-R1 1.5B sits at #518 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 30.9 points. The median model in this index scores 76.1, which puts DeepSeek-R1 1.5B below the midpoint.
DeepSeek models average 74.9 on this benchmark. DeepSeek-R1 1.5B is 14.2 points below that provider line, so it is a weaker result than the average DeepSeek model.
Among small open-weight models, the average is 59.8. DeepSeek-R1 1.5B is 0.9 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 60.7 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 $1.04 per million input tokens, DeepSeek-R1 1.5B is below the median input price of $4.00 across the 600 models scored on MMLU-Pro. 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 |
|---|---|---|---|---|
| Gemma 3 7B | Google DeepMind | 32768 | $0.90 | 55.9 |
| Llama 3 Anyscale 8B | Anyscale | 32768 | $0.97 | 57.3 |
| Qwen3 1.5B | Alibaba Cloud | 4096 | $0.84 | 53.5 |
| DBRX 7B | Databricks | 8192 | $0.65 | 51.9 |
| DeepSeek-R1 1.5B | DeepSeek | 16384 | $1.04 | 56.6 |
| Llama 3 Hyperbolic 7B | Hyperbolic | 4096 | $0.94 | 55.6 |
| Llama 3 Replicate 7B | Replicate | 32768 | $0.91 | 56.0 |
Other benchmarks for DeepSeek-R1 1.5B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| GPQA Diamond | 54.3 | 475 | 21th | |
| SWE-bench Verified | 45.2 | 464 | 23th | |
| HumanEval | 59.7 | 508 | 15th | |
| MATH-500 | 58.5 | 484 | 19th | |
| AIME 2025 | 42.7 | 527 | 12th | |
| LiveCodeBench | 42.9 | 516 | 14th | |
| IFEval | 66.6 | 506 | 16th | |
| TruthfulQA | 61.4 | 514 | 14th | |
| HellaSwag | 74.2 | 452 | 25th | |
| MMMU | 56.2 | 484 | 19th | |
| Arena Elo | 1250.5 | 490 | 18th |
FAQ
What score does DeepSeek-R1 1.5B get on MMLU-Pro?
DeepSeek-R1 1.5B scores 60.7 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.
How does DeepSeek-R1 1.5B rank on MMLU-Pro?
It ranks #518 of 600 models, in the 14th percentile. The leader is GPT-5.2 Pro at 91.6.
Is DeepSeek-R1 1.5B good at MMLU-Pro?
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 MMLU-Pro score calculated?
MMLU-Pro is a knowledge 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-R1 1.5B cost?
DeepSeek-R1 1.5B costs $1.04 per million input tokens and $5.02 per million output tokens.