Multimodal · Meta AI

Llama 3.3 1.5B on MMMU: score, rank and what it means

Llama 3.3 1.5B scores 51.4 on MMMU, placing it #540 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
51.4
0–100 scale
Rank
#540
of 600
Percentile
10th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
35.5
points

Score distribution

The chart shows the top 10 models on MMMU, with Llama 3.3 1.5B 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, Llama 3.3 1.5B sits at #540 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 35.5 points. The median model in this index scores 70.0, which puts Llama 3.3 1.5B below the midpoint.

Meta AI models average 66.5 on this benchmark. Llama 3.3 1.5B is 15.1 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 52.4. Llama 3.3 1.5B is 1.0 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 51.4 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 $0.61 per million input tokens, Llama 3.3 1.5B 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
QwQ 1.5B Alibaba Cloud 32768 $0.61 51.6
Yi-Large 7B 01.AI 4096 $0.83 55.0
Llama 3.3 Groq 1.5B Groq 4096 $0.46 50.0
Hunyuan-Large 1.5B Tencent 8192 $0.54 50.5
Llama 3.3 1.5B Meta AI 4096 $0.61 52.2
Llama 3 Lambda 7B Lambda 8192 $0.84 55.4
Mixtral Groq 8B Groq 32768 $0.77 54.8

Other benchmarks for Llama 3.3 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 59.0 537 11th
GPQA Diamond 46.1 557 7th
SWE-bench Verified 37.6 539 10th
HumanEval 57.3 529 12th
MATH-500 51.8 543 10th
AIME 2025 40.7 540 10th
LiveCodeBench 40.1 538 10th
IFEval 64.6 535 11th
TruthfulQA 56.7 556 7th
HellaSwag 68.7 550 8th
Arena Elo 1203.1 549 9th

FAQ

What score does Llama 3.3 1.5B get on MMMU?

Llama 3.3 1.5B scores 51.4 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does Llama 3.3 1.5B rank on MMMU?

It ranks #540 of 600 models, in the 10th percentile. The leader is GPT-5.2 Max at 86.9.

Is Llama 3.3 1.5B 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 Llama 3.3 1.5B cost?

Llama 3.3 1.5B costs $0.61 per million input tokens and $2.59 per million output tokens.

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