Commonsense · Google DeepMind

Gemma 3 1.5B on HellaSwag: score, rank and what it means

Gemma 3 1.5B scores 68.8 on HellaSwag, placing it #548 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
68.8
0–100 scale
Rank
#548
of 600
Percentile
9th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
27.3
points

Score distribution

The chart shows the top 10 models on HellaSwag, with Gemma 3 1.5B 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, Gemma 3 1.5B sits at #548 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 27.3 points. The median model in this index scores 83.6, which puts Gemma 3 1.5B below the midpoint.

Google DeepMind models average 86.9 on this benchmark. Gemma 3 1.5B is 18.1 points below that provider line, so it is a weaker result than the average Google DeepMind model.

Among small open-weight models, the average is 70.2. Gemma 3 1.5B is 1.4 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 68.8 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 $0.40 per million input tokens, Gemma 3 1.5B 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
DeepSeek-Coder 1.5B DeepSeek 16384 $0.58 51.0
Molmo 8B Allen Institute for AI 32768 $0.44 49.9
Phi-3.5 7B Microsoft 4096 $0.50 50.5
Qwen3 1.5B Alibaba Cloud 4096 $0.84 53.5
Gemma 3 1.5B Google DeepMind 32768 $0.40 48.9
Codestral 1.5B Mistral AI 8192 $0.52 49.9
Llama 3.3 1.5B Meta AI 4096 $0.61 52.2

Other benchmarks for Gemma 3 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 54.0 576 4th
GPQA Diamond 44.2 570 5th
SWE-bench Verified 32.6 573 5th
HumanEval 53.0 555 8th
MATH-500 46.0 576 4th
AIME 2025 39.2 546 9th
LiveCodeBench 34.0 573 5th
IFEval 62.3 552 8th
TruthfulQA 55.9 568 5th
MMMU 48.3 561 7th
Arena Elo 1193.8 561 7th

FAQ

What score does Gemma 3 1.5B get on HellaSwag?

Gemma 3 1.5B scores 68.8 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Gemma 3 1.5B rank on HellaSwag?

It ranks #548 of 600 models, in the 9th percentile. The leader is GPT-5.2 Max at 96.1.

Is Gemma 3 1.5B 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 Gemma 3 1.5B cost?

Gemma 3 1.5B costs $0.40 per million input tokens and $1.82 per million output tokens.

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