Mathematics · Databricks

DBRX 8B on MATH-500: score, rank and what it means

DBRX 8B scores 55.8 on MATH-500, placing it #508 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
55.8
0–100 scale
Rank
#508
of 600
Percentile
15th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
40.9
points

Score distribution

The chart shows the top 10 models on MATH-500, with DBRX 8B 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 8B sits at #508 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 40.9 points. The median model in this index scores 76.0, which puts DBRX 8B below the midpoint.

Databricks models average 63.8 on this benchmark. DBRX 8B is 8.0 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 8B is 2.1 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 55.8 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 $0.82 per million input tokens, DBRX 8B 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
Mixtral 8B Mistral AI 8192 $0.91 55.4
Gemma 3 7B Google DeepMind 32768 $0.90 55.9
Llama 3 Hyperbolic 8B Hyperbolic 16384 $0.92 56.0
Llama 3 Replicate 7B Replicate 32768 $0.91 56.0
DBRX 8B Databricks 4096 $0.82 54.1
Llama 3.1 Cerebras 7B Cerebras 32768 $0.95 56.5
Llama 3.1 Turbo 7B Together AI 8192 $0.98 56.4

Other benchmarks for DBRX 8B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 61.1 511 15th
GPQA Diamond 49.5 531 12th
SWE-bench Verified 37.4 540 10th
HumanEval 54.7 545 9th
AIME 2025 45.7 491 18th
LiveCodeBench 38.6 545 9th
IFEval 64.8 534 11th
TruthfulQA 61.9 505 16th
HellaSwag 70.3 530 12th
MMMU 55.0 496 17th
Arena Elo 1243.0 503 16th

FAQ

What score does DBRX 8B get on MATH-500?

DBRX 8B scores 55.8 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does DBRX 8B rank on MATH-500?

It ranks #508 of 600 models, in the 15th percentile. The leader is GPT-5.2 Max at 96.7.

Is DBRX 8B 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 8B cost?

DBRX 8B costs $0.82 per million input tokens and $3.15 per million output tokens.

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