Mathematics · DeepSeek

DeepSeek-Coder 7B on MATH-500: score, rank and what it means

DeepSeek-Coder 7B scores 56.9 on MATH-500, placing it #498 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
56.9
0–100 scale
Rank
#498
of 600
Percentile
17th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
39.8
points

Score distribution

The chart shows the top 10 models on MATH-500, with DeepSeek-Coder 7B 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 7B sits at #498 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 39.8 points. The median model in this index scores 76.0, which puts DeepSeek-Coder 7B below the midpoint.

DeepSeek models average 74.2 on this benchmark. DeepSeek-Coder 7B is 17.3 points below that provider line, so it is a weaker result than the average DeepSeek model.

Among coding models, the average is 71.0. DeepSeek-Coder 7B is 14.1 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 56.9 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 $1.14 per million input tokens, DeepSeek-Coder 7B is below 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
Command R+ 8B Cohere 8192 $1.21 57.7
Llama 3 DeepInfra 8B DeepInfra 8192 $1.05 57.4
Yi-Large 7B 01.AI 4096 $0.83 55.0
Codestral 7B Mistral AI 32768 $1.05 57.0
DeepSeek-Coder 7B DeepSeek 32768 $1.14 57.3
Hunyuan-Large 7B Tencent 32768 $1.08 57.7
Mistral Large 3 1.5B Mistral AI 32768 $0.82 55.4

Other benchmarks for DeepSeek-Coder 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 61.7 506 16th
GPQA Diamond 54.1 478 20th
SWE-bench Verified 43.6 483 20th
HumanEval 62.0 468 22th
AIME 2025 43.8 511 15th
LiveCodeBench 48.3 462 23th
IFEval 70.1 459 24th
TruthfulQA 63.8 471 22th
HellaSwag 73.2 474 21th
MMMU 53.2 517 14th
Arena Elo 1260.7 473 21th

FAQ

What score does DeepSeek-Coder 7B get on MATH-500?

DeepSeek-Coder 7B scores 56.9 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does DeepSeek-Coder 7B rank on MATH-500?

It ranks #498 of 600 models, in the 17th percentile. The leader is GPT-5.2 Max at 96.7.

Is DeepSeek-Coder 7B good at MATH-500?

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 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 7B cost?

DeepSeek-Coder 7B costs $1.14 per million input tokens and $4.37 per million output tokens.

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