Commonsense · Databricks

DBRX Base on HellaSwag: score, rank and what it means

DBRX Base scores 79.4 on HellaSwag, placing it #391 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
79.4
0–100 scale
Rank
#391
of 600
Percentile
35th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
16.7
points

Score distribution

The chart shows the top 10 models on HellaSwag, with DBRX Base highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.1
GPT-5.2 Turbo 95.5
GPT-5.2 Pro 95.3
Claude Opus 4.5 Pro 95.2
Claude Sonnet 4.5 Reasoning 95.2
Grok 4 Reasoning 95.2
GPT-5.2 Vision 94.6
Gemini 3 Pro Turbo 94.6
GPT-5.1 Reasoning 94.4
o4 Pro 94.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
8 Gemini 3 Pro Turbo Google DeepMind 400000 $19.62 86.4
9 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
10 o4 Pro OpenAI 1000000 $19.37 87.3

What this score means

On HellaSwag, DBRX Base sits at #391 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 16.7 points. The median model in this index scores 83.6, which puts DBRX Base below the midpoint.

Databricks models average 76.4 on this benchmark. DBRX Base is 3.0 points above that provider line, so it is a stronger result than the average Databricks model.

Among open-weight models, the average is 80.2. DBRX Base is 0.8 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 79.4 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 $2.63 per million input tokens, DBRX Base is below the median input price of $4.00 across the 600 models scored on HellaSwag. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Molmo Instruct Allen Institute for AI 32768 $2.53 68.3
Nemotron Nano Base NVIDIA 8192 $1.85 65.0
Phi-3.5 Base Microsoft 65536 $2.24 66.6
Reka Core Nano Reka AI 16384 $5.45 65.1
DBRX Base Databricks 32768 $2.63 69.1
Molmo Allen Institute for AI 32768 $2.49 68.5
Nova Lite Mini Amazon 16384 $5.43 65.0

Other benchmarks for DBRX Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 74.4 343 43th
GPQA Diamond 66.5 338 44th
SWE-bench Verified 55.0 385 36th
HumanEval 75.9 329 45th
MATH-500 70.9 379 37th
AIME 2025 63.5 363 40th
LiveCodeBench 58.3 384 36th
IFEval 76.7 380 37th
TruthfulQA 70.7 393 35th
MMMU 69.0 324 46th
Arena Elo 1352.8 359 40th

FAQ

What score does DBRX Base get on HellaSwag?

DBRX Base scores 79.4 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does DBRX Base rank on HellaSwag?

It ranks #391 of 600 models, in the 35th percentile. The leader is GPT-5.2 Max at 96.1.

Is DBRX Base good at HellaSwag?

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 HellaSwag score calculated?

HellaSwag is a commonsense 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 Base cost?

DBRX Base costs $2.63 per million input tokens and $11.61 per million output tokens.

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