DeepSeek-Coder Chat on AIME 2025: score, rank and what it means
DeepSeek-Coder Chat scores 70.1 on AIME 2025, placing it #255 of 600 models in the index. The benchmark average is 63.6 and the leader is Claude Opus 4.5 Pro at 91.4.
Score distribution
The chart shows the top 10 models on AIME 2025, with DeepSeek-Coder Chat highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 2 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 3 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 4 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 5 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
| 6 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
| 7 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 8 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
What this score means
On AIME 2025, DeepSeek-Coder Chat sits at #255 of 600. The leader, Claude Opus 4.5 Pro, scores 91.4, so the gap to the top is 21.3 points. The median model in this index scores 67.6, which puts DeepSeek-Coder Chat above the midpoint.
DeepSeek models average 65.1 on this benchmark. DeepSeek-Coder Chat is 5.0 points above that provider line, so it is a stronger result than the average DeepSeek model.
Among coding models, the average is 62.3. DeepSeek-Coder Chat is 7.8 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 70.1 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 $4.20 per million input tokens, DeepSeek-Coder Chat is above the median input price of $4.00 across the 600 models scored on AIME 2025. 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 |
|---|---|---|---|---|
| Llama 3 Hyperbolic Instruct | Hyperbolic | 200000 | $3.79 | 76.2 |
| Llama 3.3 Chat | Meta AI | 32768 | $4.18 | 76.9 |
| Hunyuan-Turbo | Tencent | 128000 | $10.55 | 75.8 |
| Llama 3 Novita Instruct | Novita AI | 32768 | $3.94 | 75.6 |
| DeepSeek-Coder Chat | DeepSeek | 200000 | $4.20 | 77.3 |
| Llama 3 DeepInfra Instruct | DeepInfra | 65536 | $3.84 | 75.5 |
| Hunyuan-Large Base | Tencent | 200000 | $3.64 | 74.0 |
Other benchmarks for DeepSeek-Coder Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 77.9 | 244 | 59th | |
| GPQA Diamond | 74.0 | 168 | 72th | |
| SWE-bench Verified | 64.5 | 221 | 63th | |
| HumanEval | 82.5 | 173 | 71th | |
| MATH-500 | 81.6 | 174 | 71th | |
| LiveCodeBench | 69.6 | 199 | 67th | |
| IFEval | 85.8 | 163 | 73th | |
| TruthfulQA | 79.9 | 180 | 70th | |
| HellaSwag | 88.1 | 137 | 77th | |
| MMMU | 76.5 | 124 | 79th | |
| Arena Elo | 1414.5 | 170 | 72th |
FAQ
What score does DeepSeek-Coder Chat get on AIME 2025?
DeepSeek-Coder Chat scores 70.1 on AIME 2025 in this index. The benchmark average is 63.6 and the top score is 91.4.
How does DeepSeek-Coder Chat rank on AIME 2025?
It ranks #255 of 600 models, in the 58th percentile. The leader is Claude Opus 4.5 Pro at 91.4.
Is DeepSeek-Coder Chat good at AIME 2025?
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 AIME 2025 score calculated?
AIME 2025 is a mathematics 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 Chat cost?
DeepSeek-Coder Chat costs $4.20 per million input tokens and $19.27 per million output tokens.