Knowledge · Hugging Face

SmolLM3 on MMLU-Pro: score, rank and what it means

SmolLM3 scores 69.2 on MMLU-Pro, placing it #405 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
69.2
0–100 scale
Rank
#405
of 600
Percentile
33th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
22.4
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with SmolLM3 highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, SmolLM3 sits at #405 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 22.4 points. The median model in this index scores 76.1, which puts SmolLM3 below the midpoint.

Hugging Face models average 60.1 on this benchmark. SmolLM3 is 9.1 points above that provider line, so it is a stronger result than the average Hugging Face model.

Among open-weight models, the average is 72.1. SmolLM3 is 2.9 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 69.2 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 MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Arctic Base Snowflake 32768 $2.27 67.5
DeepSeek-R1 7B DeepSeek 32768 $1.79 63.0
Firefunction Mini Fireworks AI 32768 $4.63 63.2
Embed 4 Pro Cohere 4096 $5.05 63.5
SmolLM3 Hugging Face 8192 $1.71 63.1
GLM-4.6 8B Zhipu AI 8192 $1.69 63.1
Auto Mini OpenRouter 4096 $4.24 61.6

Other benchmarks for SmolLM3

BenchmarkScoreRankPercentileRelative
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
HellaSwag 77.7 405 33th
MMMU 58.3 454 24th
Arena Elo 1294.1 427 29th

FAQ

What score does SmolLM3 get on MMLU-Pro?

SmolLM3 scores 69.2 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does SmolLM3 rank on MMLU-Pro?

It ranks #405 of 600 models, in the 33rd percentile. The leader is GPT-5.2 Pro at 91.6.

Is SmolLM3 good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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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