Mathematics · Hyperbolic

Llama 3 Hyperbolic 1.5B on MATH-500: score, rank and what it means

Llama 3 Hyperbolic 1.5B scores 49.6 on MATH-500, placing it #559 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
49.6
0–100 scale
Rank
#559
of 600
Percentile
7th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
47.1
points

Score distribution

The chart shows the top 10 models on MATH-500, with Llama 3 Hyperbolic 1.5B highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, Llama 3 Hyperbolic 1.5B sits at #559 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 47.1 points. The median model in this index scores 76.0, which puts Llama 3 Hyperbolic 1.5B below the midpoint.

Hyperbolic models average 67.8 on this benchmark. Llama 3 Hyperbolic 1.5B is 18.2 points below that provider line, so it is a weaker result than the average Hyperbolic model.

Among small open-weight models, the average is 53.7. Llama 3 Hyperbolic 1.5B is 4.1 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 49.6 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 $0.45 per million input tokens, Llama 3 Hyperbolic 1.5B is below the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Seed-OSS 1.5B ByteDance 4096 $0.45 49.8
Llama 3 Lambda 1.5B Lambda 16384 $0.46 48.4
Molmo 7B Allen Institute for AI 32768 $0.42 49.3
Llama 3.1 Turbo 1.5B Together AI 16384 $0.46 49.8
Llama 3 Hyperbolic 1.5B Hyperbolic 32768 $0.45 48.4
Molmo 8B Allen Institute for AI 32768 $0.44 49.9
Yi-Lightning 1.5B 01.AI 4096 $0.54 50.0

Other benchmarks for Llama 3 Hyperbolic 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 53.5 579 4th
GPQA Diamond 45.3 564 6th
SWE-bench Verified 33.2 569 5th
HumanEval 49.2 579 4th
AIME 2025 34.5 572 5th
LiveCodeBench 38.2 548 9th
IFEval 62.7 547 9th
TruthfulQA 56.0 567 6th
HellaSwag 65.2 581 3th
MMMU 45.5 582 3th
Arena Elo 1193.7 562 6th

FAQ

What score does Llama 3 Hyperbolic 1.5B get on MATH-500?

Llama 3 Hyperbolic 1.5B scores 49.6 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Llama 3 Hyperbolic 1.5B rank on MATH-500?

It ranks #559 of 600 models, in the 7th percentile. The leader is GPT-5.2 Max at 96.7.

Is Llama 3 Hyperbolic 1.5B good at MATH-500?

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 MATH-500 score calculated?

MATH-500 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 1.5B cost?

Llama 3 Hyperbolic 1.5B costs $0.45 per million input tokens and $1.70 per million output tokens.

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