Safety · Hyperbolic

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

Llama 3 Hyperbolic Instruct scores 79.4 on TruthfulQA, placing it #203 of 600 models in the index. The benchmark average is 73.6 and the leader is GPT-5.1 Max at 91.1.

Score
79.4
0–100 scale
Rank
#203
of 600
Percentile
66th
within the index
Benchmark avg
73.6
all models
Leader
91.1
GPT-5.1 Max
Gap to leader
11.7
points

Score distribution

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

GPT-5.1 Max 91.1
Gemini 3 Pro Reasoning 90.3
GPT-5.1 Reasoning 90.0
GPT-5.2 Pro 90.0
GPT-5.2 89.7
Gemini 3 Pro Max 89.6
Grok 4 Reasoning 89.4
Qwen3-Max Max 89.3
GPT-5.1 89.2
GPT-5.1 Turbo 89.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
2 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
3 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
4 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
5 GPT-5.2 OpenAI 2000000 $19.71 88.0
6 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
7 Grok 4 Reasoning xAI 2000000 $19.67 87.8
8 Qwen3-Max Max Alibaba Cloud 400000 $18.53 85.9
9 GPT-5.1 OpenAI 2000000 $19.01 86.0
10 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1

What this score means

On TruthfulQA, Llama 3 Hyperbolic Instruct sits at #203 of 600. The leader, GPT-5.1 Max, scores 91.1, so the gap to the top is 11.7 points. The median model in this index scores 76.3, which puts Llama 3 Hyperbolic Instruct above the midpoint.

Hyperbolic models average 69.3 on this benchmark. Llama 3 Hyperbolic Instruct is 10.1 points above that provider line, so it is a stronger result than the average Hyperbolic model.

Among multimodal models, the average is 80.1. Llama 3 Hyperbolic 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 79.4 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 TruthfulQA. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
o3 Mini OpenAI 128000 $10.68 76.2
Command R+ Chat Cohere 32768 $4.02 76.0
Llama 3 Anyscale Instruct Anyscale 200000 $3.91 75.7
Llama 3 Modal Modal 32768 $3.82 74.9
Llama 3 Hyperbolic Instruct Hyperbolic 200000 $3.79 76.2
Hunyuan-Turbo Turbo Tencent 65536 $11.60 77.4
Llama 3 Baseten Instruct Baseten 200000 $3.91 76.2

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
AIME 2025 70.4 251 58th
LiveCodeBench 69.0 208 65th
IFEval 83.6 223 63th
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 TruthfulQA?

Llama 3 Hyperbolic Instruct scores 79.4 on TruthfulQA in this index. The benchmark average is 73.6 and the top score is 91.1.

How does Llama 3 Hyperbolic Instruct rank on TruthfulQA?

It ranks #203 of 600 models, in the 66th percentile. The leader is GPT-5.1 Max at 91.1.

Is Llama 3 Hyperbolic Instruct good at TruthfulQA?

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 TruthfulQA score calculated?

TruthfulQA is a safety 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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