Mathematics · DeepSeek

DeepSeek-Coder on MATH-500: score, rank and what it means

DeepSeek-Coder scores 79.9 on MATH-500, placing it #209 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
79.9
0–100 scale
Rank
#209
of 600
Percentile
65th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
16.8
points

Score distribution

The chart shows the top 10 models on MATH-500, with DeepSeek-Coder highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg 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-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, DeepSeek-Coder sits at #209 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 16.8 points. The median model in this index scores 76.0, which puts DeepSeek-Coder above the midpoint.

DeepSeek models average 74.2 on this benchmark. DeepSeek-Coder is 5.7 points above that provider line, so it is a stronger result than the average DeepSeek model.

Among coding models, the average is 71.0. DeepSeek-Coder is 9.0 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 79.9 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 $4.55 per million input tokens, DeepSeek-Coder is above the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Yi-Lightning 01.AI 32768 $4.22 77.2
Llama 3 Replicate Replicate 200000 $3.75 75.7
Phi-4 Instruct Microsoft 128000 $3.79 75.3
QwQ Alibaba Cloud 128000 $4.57 78.6
DeepSeek-Coder DeepSeek 32768 $4.55 78.1
GPT-5.1 Mini OpenAI 128000 $12.19 77.7
Kimi K1.5 Base Moonshot AI 65536 $4.34 77.4

Other benchmarks for DeepSeek-Coder

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.4 229 62th
GPQA Diamond 74.0 167 72th
SWE-bench Verified 68.4 136 77th
HumanEval 81.5 195 68th
AIME 2025 78.7 105 83th
LiveCodeBench 68.2 223 63th
IFEval 86.6 142 76th
TruthfulQA 81.5 140 77th
HellaSwag 87.3 162 73th
MMMU 74.1 183 70th
Arena Elo 1403.6 198 67th

FAQ

What score does DeepSeek-Coder get on MATH-500?

DeepSeek-Coder scores 79.9 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does DeepSeek-Coder rank on MATH-500?

It ranks #209 of 600 models, in the 65th percentile. The leader is GPT-5.2 Max at 96.7.

Is DeepSeek-Coder good at MATH-500?

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 MATH-500 score calculated?

MATH-500 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 cost?

DeepSeek-Coder costs $4.55 per million input tokens and $24.30 per million output tokens.

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