Commonsense · DeepInfra

Llama 3 DeepInfra Instruct on HellaSwag: score, rank and what it means

Llama 3 DeepInfra Instruct scores 86.1 on HellaSwag, placing it #212 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
86.1
0–100 scale
Rank
#212
of 600
Percentile
65th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
10.0
points

Score distribution

The chart shows the top 10 models on HellaSwag, with Llama 3 DeepInfra 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 DeepInfra Instruct sits at #212 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 10.0 points. The median model in this index scores 83.6, which puts Llama 3 DeepInfra Instruct above the midpoint.

DeepInfra models average 78.4 on this benchmark. Llama 3 DeepInfra Instruct is 7.7 points above that provider line, so it is a stronger result than the average DeepInfra model.

Among multimodal models, the average is 86.8. Llama 3 DeepInfra Instruct is 0.7 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 86.1 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 $3.84 per million input tokens, Llama 3 DeepInfra Instruct 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
Llama 3 Lambda Chat Lambda 32768 $3.73 74.7
MiniMax-Text Base MiniMax 128000 $3.66 74.9
Sonar Reasoning Perplexity 200000 $11.48 76.2
Hunyuan-Turbo Max Tencent 128000 $12.40 78.3
Llama 3 DeepInfra Instruct DeepInfra 65536 $3.84 75.5
Magistral Chat Mistral AI 128000 $4.33 76.8
Nemotron 4 NVIDIA 65536 $4.18 76.6

Other benchmarks for Llama 3 DeepInfra Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 75.5 317 47th
GPQA Diamond 71.7 226 62th
SWE-bench Verified 66.3 186 69th
HumanEval 81.4 201 67th
MATH-500 77.6 263 56th
AIME 2025 70.0 256 57th
LiveCodeBench 69.2 204 66th
IFEval 82.3 265 56th
TruthfulQA 76.3 299 50th
MMMU 74.2 179 70th
Arena Elo 1374.9 292 51th

FAQ

What score does Llama 3 DeepInfra Instruct get on HellaSwag?

Llama 3 DeepInfra Instruct scores 86.1 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Llama 3 DeepInfra Instruct rank on HellaSwag?

It ranks #212 of 600 models, in the 65th percentile. The leader is GPT-5.2 Max at 96.1.

Is Llama 3 DeepInfra 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 DeepInfra Instruct cost?

Llama 3 DeepInfra Instruct costs $3.84 per million input tokens and $20.22 per million output tokens.

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