Commonsense · Hugging Face

SmolLM3 1.5B on HellaSwag: score, rank and what it means

SmolLM3 1.5B scores 58.1 on HellaSwag, placing it #599 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
58.1
0–100 scale
Rank
#599
of 600
Percentile
1th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
38.0
points

Score distribution

The chart shows the top 10 models on HellaSwag, with SmolLM3 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, SmolLM3 1.5B sits at #599 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 38.0 points. The median model in this index scores 83.6, which puts SmolLM3 1.5B below the midpoint.

Hugging Face models average 70.2 on this benchmark. SmolLM3 1.5B is 12.1 points below that provider line, so it is a weaker result than the average Hugging Face model.

Among small open-weight models, the average is 70.2. SmolLM3 1.5B is 12.1 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 58.1 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 $0.040 per million input tokens, SmolLM3 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
Pythia 8B EleutherAI 32768 $0.16 41.4
Zephyr 1.5B Hugging Face 32768 $0.10 38.0
StableLM 2 1.5B Stability AI 16384 $0.13 38.3
GPT-NeoX 1.5B EleutherAI 32768 $0.06 36.4
SmolLM3 1.5B Hugging Face 8192 $0.04 36.8
Pythia 1.5B EleutherAI 4096 $0.10 35.8

Other benchmarks for SmolLM3 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 46.7 599 1th
GPQA Diamond 31.9 599 1th
SWE-bench Verified 18.4 599 1th
HumanEval 39.8 597 1th
MATH-500 37.7 596 1th
AIME 2025 21.7 597 1th
LiveCodeBench 20.0 600 1th
IFEval 50.2 599 1th
TruthfulQA 44.2 600 1th
MMMU 35.8 599 1th
Arena Elo 1103.0 599 1th

FAQ

What score does SmolLM3 1.5B get on HellaSwag?

SmolLM3 1.5B scores 58.1 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does SmolLM3 1.5B rank on HellaSwag?

It ranks #599 of 600 models, in the 1st percentile. The leader is GPT-5.2 Max at 96.1.

Is SmolLM3 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 SmolLM3 1.5B cost?

SmolLM3 1.5B costs $0.040 per million input tokens and $0.17 per million output tokens.

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