Mathematics · Meta AI

Llama 4 Scout 7B on MATH-500: score, rank and what it means

Llama 4 Scout 7B scores 61.4 on MATH-500, placing it #449 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
61.4
0–100 scale
Rank
#449
of 600
Percentile
25th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
35.3
points

Score distribution

The chart shows the top 10 models on MATH-500, with Llama 4 Scout 7B 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, Llama 4 Scout 7B sits at #449 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 35.3 points. The median model in this index scores 76.0, which puts Llama 4 Scout 7B below the midpoint.

Meta AI models average 71.4 on this benchmark. Llama 4 Scout 7B is 10.0 points below that provider line, so it is a weaker result than the average Meta AI model.

Among small open-weight models, the average is 53.7. Llama 4 Scout 7B is 7.7 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 61.4 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 $1.30 per million input tokens, Llama 4 Scout 7B 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
Llama 3.3 7B Meta AI 8192 $1.18 57.8
SmolLM3 Chat Hugging Face 16384 $1.61 62.1
Qwen3-Coder 7B Alibaba Cloud 32768 $1.24 59.6
Reka Flash Nano Reka AI 16384 $3.93 61.0
Llama 4 Scout 7B Meta AI 32768 $1.30 60.0
Firefunction Nano Fireworks AI 16384 $3.07 58.1
Zephyr Chat Hugging Face 4096 $1.49 61.3

Other benchmarks for Llama 4 Scout 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 63.3 484 19th
GPQA Diamond 53.9 483 20th
SWE-bench Verified 45.9 454 24th
HumanEval 61.2 483 20th
AIME 2025 53.1 433 28th
LiveCodeBench 51.3 435 28th
IFEval 70.5 449 25th
TruthfulQA 64.9 460 23th
HellaSwag 74.6 447 26th
MMMU 60.0 429 29th
Arena Elo 1265.6 468 22th

FAQ

What score does Llama 4 Scout 7B get on MATH-500?

Llama 4 Scout 7B scores 61.4 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Llama 4 Scout 7B rank on MATH-500?

It ranks #449 of 600 models, in the 25th percentile. The leader is GPT-5.2 Max at 96.7.

Is Llama 4 Scout 7B 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 Llama 4 Scout 7B cost?

Llama 4 Scout 7B costs $1.30 per million input tokens and $5.58 per million output tokens.

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