Knowledge · Hugging Face

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

SmolLM3 Chat scores 68.7 on MMLU-Pro, placing it #409 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
68.7
0–100 scale
Rank
#409
of 600
Percentile
32th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
22.9
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with SmolLM3 Chat 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 Chat sits at #409 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 22.9 points. The median model in this index scores 76.1, which puts SmolLM3 Chat below the midpoint.

Hugging Face models average 60.1 on this benchmark. SmolLM3 Chat is 8.6 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 Chat is 3.4 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 68.7 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 $1.61 per million input tokens, SmolLM3 Chat 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
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
Zephyr Hugging Face 8192 $1.61 62.8
SmolLM3 Chat Hugging Face 16384 $1.61 62.1
Sonar Pro Nano Perplexity 32768 $5.50 66.1
Molmo Base Allen Institute for AI 4096 $2.02 65.0

Other benchmarks for SmolLM3 Chat

BenchmarkScoreRankPercentileRelative
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
MMMU 60.3 426 29th
Arena Elo 1293.9 428 29th

FAQ

What score does SmolLM3 Chat get on MMLU-Pro?

SmolLM3 Chat scores 68.7 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does SmolLM3 Chat rank on MMLU-Pro?

It ranks #409 of 600 models, in the 32nd percentile. The leader is GPT-5.2 Pro at 91.6.

Is SmolLM3 Chat 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 Chat cost?

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

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