Multimodal · Hugging Face

SmolLM3 Chat on MMMU: score, rank and what it means

SmolLM3 Chat scores 60.3 on MMMU, placing it #426 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
60.3
0–100 scale
Rank
#426
of 600
Percentile
29th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
26.6
points

Score distribution

The chart shows the top 10 models on MMMU, with SmolLM3 Chat 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 Chat sits at #426 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 26.6 points. The median model in this index scores 70.0, which puts SmolLM3 Chat below the midpoint.

Hugging Face models average 51.0 on this benchmark. SmolLM3 Chat is 9.3 points above that provider line, so it is a stronger result than the average Hugging Face model.

Among open-weight models, the average is 65.1. SmolLM3 Chat is 4.8 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 60.3 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 $1.61 per million input tokens, SmolLM3 Chat 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
Zephyr Instruct Hugging Face 4096 $1.66 62.4
Embed 4 Pro Cohere 4096 $5.05 63.5
GPT-NeoX EleutherAI 16384 $1.57 61.7
SmolLM3 Instruct Hugging Face 8192 $1.69 63.6
SmolLM3 Chat Hugging Face 16384 $1.61 62.1
Llama 3.3 8B Meta AI 8192 $1.26 60.1
Pythia Chat EleutherAI 4096 $1.31 59.1

Other benchmarks for SmolLM3 Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 68.7 409 32th
GPQA Diamond 57.5 433 28th
SWE-bench Verified 47.4 442 26th
HumanEval 63.6 454 24th
MATH-500 62.0 446 26th
AIME 2025 53.0 435 28th
LiveCodeBench 55.0 408 32th
IFEval 72.6 424 29th
TruthfulQA 66.4 439 27th
HellaSwag 76.1 427 29th
Arena Elo 1293.9 428 29th

FAQ

What score does SmolLM3 Chat get on MMMU?

SmolLM3 Chat scores 60.3 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does SmolLM3 Chat rank on MMMU?

It ranks #426 of 600 models, in the 29th percentile. The leader is GPT-5.2 Max at 86.9.

Is SmolLM3 Chat 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 Chat cost?

SmolLM3 Chat costs $1.61 per million input tokens and $7.69 per million output tokens.

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