Commonsense · Meta AI

Llama 3.3 Instruct on HellaSwag: score, rank and what it means

Llama 3.3 Instruct scores 86.9 on HellaSwag, placing it #182 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
86.9
0–100 scale
Rank
#182
of 600
Percentile
70th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
9.2
points

Score distribution

The chart shows the top 10 models on HellaSwag, with Llama 3.3 Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.1
GPT-5.2 Turbo 95.5
GPT-5.2 Pro 95.3
Claude Opus 4.5 Pro 95.2
Claude Sonnet 4.5 Reasoning 95.2
Grok 4 Reasoning 95.2
GPT-5.2 Vision 94.6
Gemini 3 Pro Turbo 94.6
GPT-5.1 Reasoning 94.4
o4 Pro 94.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
8 Gemini 3 Pro Turbo Google DeepMind 400000 $19.62 86.4
9 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
10 o4 Pro OpenAI 1000000 $19.37 87.3

What this score means

On HellaSwag, Llama 3.3 Instruct sits at #182 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 9.2 points. The median model in this index scores 83.6, which puts Llama 3.3 Instruct above the midpoint.

Meta AI models average 80.6 on this benchmark. Llama 3.3 Instruct is 6.3 points above that provider line, so it is a stronger result than the average Meta AI model.

Among multimodal models, the average is 86.8. Llama 3.3 Instruct is 0.1 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 86.9 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 $4.33 per million input tokens, Llama 3.3 Instruct is above the median input price of $4.00 across the 600 models scored on HellaSwag. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3 Lambda Instruct Lambda 32768 $3.92 75.8
Qwen3 Instruct Alibaba Cloud 1000000 $5.06 79.8
Command A Turbo Cohere 32768 $13.53 79.8
Hunyuan-Large Chat Tencent 200000 $4.35 78.1
Llama 3.3 Instruct Meta AI 128000 $4.33 76.6
DeepSeek-Coder Instruct DeepSeek 32768 $4.34 77.7
Llama 3 Baseten Instruct Baseten 200000 $3.91 76.2

Other benchmarks for Llama 3.3 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.7 254 58th
GPQA Diamond 70.1 257 57th
SWE-bench Verified 63.7 239 60th
HumanEval 82.1 185 69th
MATH-500 81.9 168 72th
AIME 2025 76.9 131 78th
LiveCodeBench 67.1 247 59th
IFEval 85.4 176 71th
TruthfulQA 79.0 219 64th
MMMU 72.0 245 59th
Arena Elo 1418.0 157 74th

FAQ

What score does Llama 3.3 Instruct get on HellaSwag?

Llama 3.3 Instruct scores 86.9 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Llama 3.3 Instruct rank on HellaSwag?

It ranks #182 of 600 models, in the 70th percentile. The leader is GPT-5.2 Max at 96.1.

Is Llama 3.3 Instruct good at HellaSwag?

Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.

How is the HellaSwag score calculated?

HellaSwag is a commonsense 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 Instruct cost?

Llama 3.3 Instruct costs $4.33 per million input tokens and $19.59 per million output tokens.

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