Reasoning · Hyperbolic

Llama 3 Hyperbolic Base on GPQA Diamond: score, rank and what it means

Llama 3 Hyperbolic Base scores 64.3 on GPQA Diamond, placing it #375 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
64.3
0–100 scale
Rank
#375
of 600
Percentile
38th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
22.7
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Llama 3 Hyperbolic Base highlighted. Longer bars mean higher scores.

GPT-5.2 Reasoning 87.0
GPT-5.1 Max 86.4
Claude Opus 4.5 Pro 86.0
GPT-5.1 Reasoning 85.6
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.2 84.9
o4 Turbo 84.8
Claude Opus 4.5 Max 84.6
Grok 4 Turbo 84.5
Claude Sonnet 4.5 Max 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 GPT-5.2 OpenAI 2000000 $19.71 88.0
7 o4 Turbo OpenAI 2000000 $19.34 86.6
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 Grok 4 Turbo xAI 1000000 $19.21 86.7
10 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2

What this score means

On GPQA Diamond, Llama 3 Hyperbolic Base sits at #375 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 22.7 points. The median model in this index scores 68.8, which puts Llama 3 Hyperbolic Base below the midpoint.

Hyperbolic models average 60.8 on this benchmark. Llama 3 Hyperbolic Base is 3.5 points above that provider line, so it is a stronger result than the average Hyperbolic model.

Among open-weight models, the average is 63.6. Llama 3 Hyperbolic Base is 0.7 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 64.3 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 $3.02 per million input tokens, Llama 3 Hyperbolic Base is below the median input price of $4.00 across the 600 models scored on GPQA Diamond. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3 DeepInfra Base DeepInfra 16384 $2.95 71.8
Firesearch Turbo Fireworks AI 16384 $7.58 69.3
Fusion OpenRouter 16384 $8.41 71.1
Mixtral Groq Base Groq 32768 $2.66 68.4
Llama 3 Hyperbolic Base Hyperbolic 32768 $3.02 71.1
Mixtral Base Mistral AI 65536 $2.95 71.1
OLMo 2 Chat Allen Institute for AI 16384 $2.94 70.6

Other benchmarks for Llama 3 Hyperbolic Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 73.3 366 39th
SWE-bench Verified 58.2 353 41th
HumanEval 74.3 354 41th
MATH-500 77.1 275 54th
AIME 2025 69.3 272 55th
LiveCodeBench 62.5 341 43th
IFEval 79.0 355 41th
TruthfulQA 72.5 375 38th
HellaSwag 83.3 313 48th
MMMU 68.6 332 45th
Arena Elo 1369.8 317 47th

FAQ

What score does Llama 3 Hyperbolic Base get on GPQA Diamond?

Llama 3 Hyperbolic Base scores 64.3 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Llama 3 Hyperbolic Base rank on GPQA Diamond?

It ranks #375 of 600 models, in the 38th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Llama 3 Hyperbolic Base good at GPQA Diamond?

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 GPQA Diamond score calculated?

GPQA Diamond is a reasoning 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 Base cost?

Llama 3 Hyperbolic Base costs $3.02 per million input tokens and $10.87 per million output tokens.

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