Commonsense · Hugging Face

SmolLM3 on HellaSwag: score, rank and what it means

SmolLM3 scores 77.7 on HellaSwag, placing it #405 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
77.7
0–100 scale
Rank
#405
of 600
Percentile
33th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
18.4
points

Score distribution

The chart shows the top 10 models on HellaSwag, with SmolLM3 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 sits at #405 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 18.4 points. The median model in this index scores 83.6, which puts SmolLM3 below the midpoint.

Hugging Face models average 70.2 on this benchmark. SmolLM3 is 7.5 points above that provider line, so it is a stronger result than the average Hugging Face model.

Among open-weight models, the average is 80.2. SmolLM3 is 2.5 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 77.7 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 $1.71 per million input tokens, SmolLM3 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 Chat Allen Institute for AI 32768 $2.40 66.7
Nemotron Nano NVIDIA 8192 $2.24 66.6
DeepSeek-R1 8B DeepSeek 16384 $1.87 64.3
Reka Flash Mini Reka AI 16384 $5.77 65.5
SmolLM3 Hugging Face 8192 $1.71 63.1
Molmo Base Allen Institute for AI 4096 $2.02 65.0
Fusion Mini OpenRouter 4096 $4.89 63.3

Other benchmarks for SmolLM3

BenchmarkScoreRankPercentileRelative
MMLU-Pro 69.2 405 33th
GPQA Diamond 61.0 398 34th
SWE-bench Verified 47.3 444 26th
HumanEval 69.5 403 33th
MATH-500 68.1 400 33th
AIME 2025 52.9 436 27th
LiveCodeBench 49.8 451 25th
IFEval 72.7 421 30th
TruthfulQA 67.9 415 31th
MMMU 58.3 454 24th
Arena Elo 1294.1 427 29th

FAQ

What score does SmolLM3 get on HellaSwag?

SmolLM3 scores 77.7 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does SmolLM3 rank on HellaSwag?

It ranks #405 of 600 models, in the 33rd percentile. The leader is GPT-5.2 Max at 96.1.

Is SmolLM3 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 cost?

SmolLM3 costs $1.71 per million input tokens and $8.86 per million output tokens.

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