Multimodal · Hugging Face

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

SmolLM3 1.5B scores 35.8 on MMMU, placing it #599 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
35.8
0–100 scale
Rank
#599
of 600
Percentile
1th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
51.1
points

Score distribution

The chart shows the top 10 models on MMMU, with SmolLM3 1.5B highlighted. Longer bars mean higher scores.

GPT-5.2 Max 86.9
GPT-5.1 Reasoning 86.2
GPT-5.2 85.3
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.1 Max 85.1
Grok 4 Pro 84.9
Claude Opus 4.5 Reasoning 84.6
Claude Sonnet 4.5 Pro 84.5
GPT-5.2 Pro 84.4
GPT-5.2 Vision 84.4

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-5.2 OpenAI 2000000 $19.71 88.0
4 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
5 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
6 Grok 4 Pro xAI 1000000 $19.00 85.4
7 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
8 Claude Sonnet 4.5 Pro Anthropic 1000000 $18.73 86.7
9 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
10 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4

What this score means

On MMMU, SmolLM3 1.5B sits at #599 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 51.1 points. The median model in this index scores 70.0, which puts SmolLM3 1.5B below the midpoint.

Hugging Face models average 51.0 on this benchmark. SmolLM3 1.5B is 15.2 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 52.4. SmolLM3 1.5B is 16.6 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 35.8 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 $0.040 per million input tokens, SmolLM3 1.5B is below the median input price of $4.00 across the 600 models scored on MMMU. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
StableLM 2 1.5B Stability AI 16384 $0.13 38.3
Zephyr 1.5B Hugging Face 32768 $0.10 38.0
Pythia 7B EleutherAI 8192 $0.10 40.7
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
HellaSwag 58.1 599 1th
Arena Elo 1103.0 599 1th

FAQ

What score does SmolLM3 1.5B get on MMMU?

SmolLM3 1.5B scores 35.8 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does SmolLM3 1.5B rank on MMMU?

It ranks #599 of 600 models, in the 1st percentile. The leader is GPT-5.2 Max at 86.9.

Is SmolLM3 1.5B good at MMMU?

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 MMMU score calculated?

MMMU is a multimodal 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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