Commonsense · Meta AI

Llama 3.1 7B on HellaSwag: score, rank and what it means

Llama 3.1 7B scores 71.1 on HellaSwag, placing it #518 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
71.1
0–100 scale
Rank
#518
of 600
Percentile
14th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
25.0
points

Score distribution

The chart shows the top 10 models on HellaSwag, with Llama 3.1 7B 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 3.1 7B sits at #518 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 25.0 points. The median model in this index scores 83.6, which puts Llama 3.1 7B below the midpoint.

Meta AI models average 80.6 on this benchmark. Llama 3.1 7B is 9.5 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 70.2. Llama 3.1 7B is 0.9 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 71.1 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 $1.01 per million input tokens, Llama 3.1 7B is below 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
Phi-4 7B Microsoft 8192 $0.91 56.8
OLMo 2 8B Allen Institute for AI 4096 $0.64 52.5
Llama 3 Lambda 8B Lambda 4096 $1.03 57.2
Command R+ 7B Cohere 8192 $0.95 56.2
Llama 3.1 7B Meta AI 8192 $1.01 57.4
Llama 4 Scout 1.5B Meta AI 8192 $0.66 53.0
Llama 3 DeepInfra 7B DeepInfra 16384 $0.95 55.6

Other benchmarks for Llama 3.1 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 64.8 456 24th
GPQA Diamond 54.4 474 21th
SWE-bench Verified 44.1 477 21th
HumanEval 60.4 498 17th
MATH-500 58.8 481 20th
AIME 2025 46.9 480 20th
LiveCodeBench 45.0 499 17th
IFEval 67.5 494 18th
TruthfulQA 63.8 473 21th
MMMU 54.5 503 16th
Arena Elo 1237.4 518 14th

FAQ

What score does Llama 3.1 7B get on HellaSwag?

Llama 3.1 7B scores 71.1 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Llama 3.1 7B rank on HellaSwag?

It ranks #518 of 600 models, in the 14th percentile. The leader is GPT-5.2 Max at 96.1.

Is Llama 3.1 7B good at HellaSwag?

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 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 3.1 7B cost?

Llama 3.1 7B costs $1.01 per million input tokens and $3.79 per million output tokens.

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