Mathematics · Databricks

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

DBRX scores 76.3 on MATH-500, placing it #291 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
76.3
0–100 scale
Rank
#291
of 600
Percentile
52th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
20.4
points

Score distribution

The chart shows the top 10 models on MATH-500, with DBRX 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 sits at #291 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 20.4 points. The median model in this index scores 76.0, which puts DBRX above the midpoint.

Databricks models average 63.8 on this benchmark. DBRX is 12.5 points above that provider line, so it is a stronger result than the average Databricks model.

Among open-weight models, the average is 70.8. DBRX is 5.5 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 76.3 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 $3.29 per million input tokens, DBRX 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+ Chat Cohere 32768 $4.02 76.0
Firefunction Fireworks AI 16384 $8.16 72.3
Llama 3 Lambda Base Lambda 8192 $3.19 72.9
Firefunction Reasoning Fireworks AI 8192 $9.08 72.5
DBRX Databricks 65536 $3.29 73.3
Llama 3 Hyperbolic Hyperbolic 65536 $3.86 74.5
Magistral Base Mistral AI 65536 $3.86 75.6

Other benchmarks for DBRX

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.0 276 54th
GPQA Diamond 67.5 321 47th
SWE-bench Verified 60.1 324 46th
HumanEval 78.2 284 53th
AIME 2025 69.6 265 56th
LiveCodeBench 64.0 316 47th
IFEval 82.3 264 56th
TruthfulQA 77.9 256 57th
HellaSwag 84.5 264 56th
MMMU 69.3 320 47th
Arena Elo 1386.5 261 57th

FAQ

What score does DBRX get on MATH-500?

DBRX scores 76.3 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does DBRX rank on MATH-500?

It ranks #291 of 600 models, in the 52nd percentile. The leader is GPT-5.2 Max at 96.7.

Is DBRX 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 DBRX cost?

DBRX costs $3.29 per million input tokens and $16.96 per million output tokens.

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