Mathematics · Hyperbolic

Llama 3 Hyperbolic Instruct on AIME 2025: score, rank and what it means

Llama 3 Hyperbolic Instruct scores 70.4 on AIME 2025, placing it #251 of 600 models in the index. The benchmark average is 63.6 and the leader is Claude Opus 4.5 Pro at 91.4.

Score
70.4
0–100 scale
Rank
#251
of 600
Percentile
58th
within the index
Benchmark avg
63.6
all models
Leader
91.4
Claude Opus 4.5 Pro
Gap to leader
21.0
points

Score distribution

The chart shows the top 10 models on AIME 2025, with Llama 3 Hyperbolic Instruct highlighted. Longer bars mean higher scores.

Claude Opus 4.5 Pro 91.4
GPT-5.2 Pro 91.0
GPT-5.1 Pro 90.6
GPT-5.2 90.6
o4 Reasoning 90.3
o4 Pro 89.9
Claude Opus 4.5 89.8
Gemini 3 Pro Max 89.6
Claude Opus 4.5 Max 89.3
Claude Opus 4.5 Reasoning 88.9

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
2 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 GPT-5.2 OpenAI 2000000 $19.71 88.0
5 o4 Reasoning OpenAI 1000000 $19.69 87.5
6 o4 Pro OpenAI 1000000 $19.37 87.3
7 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
8 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2

What this score means

On AIME 2025, Llama 3 Hyperbolic Instruct sits at #251 of 600. The leader, Claude Opus 4.5 Pro, scores 91.4, so the gap to the top is 21.0 points. The median model in this index scores 67.6, which puts Llama 3 Hyperbolic Instruct above the midpoint.

Hyperbolic models average 57.9 on this benchmark. Llama 3 Hyperbolic Instruct is 12.5 points above that provider line, so it is a stronger result than the average Hyperbolic model.

Among multimodal models, the average is 73.9. Llama 3 Hyperbolic Instruct is 3.5 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 70.4 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 $3.79 per million input tokens, Llama 3 Hyperbolic Instruct is below the median input price of $4.00 across the 600 models scored on AIME 2025. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Yi-Lightning Base 01.AI 16384 $3.31 72.7
Firefunction Max Fireworks AI 32768 $9.12 73.2
Llama 3.1 Turbo Instruct Together AI 32768 $4.21 76.7
Hunyuan-Large Chat Tencent 200000 $4.35 78.1
Llama 3 Hyperbolic Instruct Hyperbolic 200000 $3.79 76.2
Llama 3.3 Chat Meta AI 32768 $4.18 76.9
Hunyuan-Turbo Tencent 128000 $10.55 75.8

Other benchmarks for Llama 3 Hyperbolic Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.9 212 65th
GPQA Diamond 70.1 256 57th
SWE-bench Verified 66.2 189 69th
HumanEval 79.4 251 58th
MATH-500 81.2 186 69th
LiveCodeBench 69.0 208 65th
IFEval 83.6 223 63th
TruthfulQA 79.4 203 66th
HellaSwag 85.9 219 64th
MMMU 74.1 185 69th
Arena Elo 1385.7 264 56th

FAQ

What score does Llama 3 Hyperbolic Instruct get on AIME 2025?

Llama 3 Hyperbolic Instruct scores 70.4 on AIME 2025 in this index. The benchmark average is 63.6 and the top score is 91.4.

How does Llama 3 Hyperbolic Instruct rank on AIME 2025?

It ranks #251 of 600 models, in the 58th percentile. The leader is Claude Opus 4.5 Pro at 91.4.

Is Llama 3 Hyperbolic Instruct good at AIME 2025?

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 AIME 2025 score calculated?

AIME 2025 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 3 Hyperbolic Instruct cost?

Llama 3 Hyperbolic Instruct costs $3.79 per million input tokens and $16.16 per million output tokens.

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