Mathematics · Hugging Face

SmolLM3 Instruct on MATH-500: score, rank and what it means

SmolLM3 Instruct scores 67.0 on MATH-500, placing it #409 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
67.0
0–100 scale
Rank
#409
of 600
Percentile
32th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
29.7
points

Score distribution

The chart shows the top 10 models on MATH-500, with SmolLM3 Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

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-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, SmolLM3 Instruct sits at #409 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 29.7 points. The median model in this index scores 76.0, which puts SmolLM3 Instruct below the midpoint.

Hugging Face models average 53.8 on this benchmark. SmolLM3 Instruct is 13.2 points above that provider line, so it is a stronger result than the average Hugging Face model.

Among open-weight models, the average is 70.8. SmolLM3 Instruct is 3.8 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 67.0 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 $1.69 per million input tokens, SmolLM3 Instruct is below the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Nova Lite Mini Amazon 16384 $5.43 65.0
Embed 4 Reasoning Cohere 4096 $5.50 65.3
DeepSeek-R1 8B DeepSeek 16384 $1.87 64.3
Reka Flash Mini Reka AI 16384 $5.77 65.5
SmolLM3 Instruct Hugging Face 8192 $1.69 63.6
DeepSeek-V3.2 8B DeepSeek 32768 $2.02 66.2
Sonar Nano Perplexity 4096 $4.37 61.9

Other benchmarks for SmolLM3 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 67.3 428 29th
GPQA Diamond 58.4 423 30th
SWE-bench Verified 50.6 417 31th
HumanEval 69.2 408 32th
AIME 2025 54.4 419 30th
LiveCodeBench 56.1 397 34th
IFEval 74.0 406 32th
TruthfulQA 66.5 438 27th
HellaSwag 75.1 440 27th
MMMU 60.5 425 29th
Arena Elo 1297.8 423 30th

FAQ

What score does SmolLM3 Instruct get on MATH-500?

SmolLM3 Instruct scores 67.0 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does SmolLM3 Instruct rank on MATH-500?

It ranks #409 of 600 models, in the 32nd percentile. The leader is GPT-5.2 Max at 96.7.

Is SmolLM3 Instruct good at MATH-500?

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 MATH-500 score calculated?

MATH-500 is a mathematics 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 Instruct cost?

SmolLM3 Instruct costs $1.69 per million input tokens and $8.67 per million output tokens.

Related