Mathematics · Databricks

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

DBRX 7B scores 50.5 on MATH-500, placing it #552 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
50.5
0–100 scale
Rank
#552
of 600
Percentile
8th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
46.2
points

Score distribution

The chart shows the top 10 models on MATH-500, with DBRX 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, DBRX 7B sits at #552 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 46.2 points. The median model in this index scores 76.0, which puts DBRX 7B below the midpoint.

Databricks models average 63.8 on this benchmark. DBRX 7B is 13.3 points below that provider line, so it is a weaker result than the average Databricks model.

Among small open-weight models, the average is 53.7. DBRX 7B is 3.2 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 50.5 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 $0.65 per million input tokens, DBRX 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
OLMo 2 8B Allen Institute for AI 4096 $0.64 52.5
QwQ 1.5B Alibaba Cloud 32768 $0.61 51.6
Mixtral Turbo 7B Together AI 8192 $0.68 53.2
Nemotron 4 1.5B NVIDIA 32768 $0.49 50.8
DBRX 7B Databricks 8192 $0.65 51.9
Llama 3 Novita 1.5B Novita AI 4096 $0.41 49.1
Nemotron Nano 8B NVIDIA 8192 $0.46 48.1

Other benchmarks for DBRX 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 60.7 517 14th
GPQA Diamond 50.0 523 13th
SWE-bench Verified 35.7 548 9th
HumanEval 54.5 546 9th
AIME 2025 40.7 539 10th
LiveCodeBench 38.4 546 9th
IFEval 62.6 548 9th
TruthfulQA 58.7 542 10th
HellaSwag 70.7 527 12th
MMMU 48.2 562 6th
Arena Elo 1214.7 539 10th

FAQ

What score does DBRX 7B get on MATH-500?

DBRX 7B scores 50.5 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does DBRX 7B rank on MATH-500?

It ranks #552 of 600 models, in the 8th percentile. The leader is GPT-5.2 Max at 96.7.

Is DBRX 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 DBRX 7B cost?

DBRX 7B costs $0.65 per million input tokens and $3.08 per million output tokens.

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