Llama 3 Hyperbolic Chat on MATH-500: score, rank and what it means
Llama 3 Hyperbolic Chat scores 79.2 on MATH-500, placing it #228 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.
Score distribution
The chart shows the top 10 models on MATH-500, with Llama 3 Hyperbolic Chat highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg 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 Chat sits at #228 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 17.5 points. The median model in this index scores 76.0, which puts Llama 3 Hyperbolic Chat above the midpoint.
Hyperbolic models average 67.8 on this benchmark. Llama 3 Hyperbolic Chat is 11.4 points above that provider line, so it is a stronger result than the average Hyperbolic model.
Among multimodal models, the average is 81.3. Llama 3 Hyperbolic Chat is 2.1 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 79.2 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 $3.64 per million input tokens, Llama 3 Hyperbolic Chat 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.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Grok 4 Mini | xAI | 65536 | $12.21 | 77.9 |
| Hunyuan-Large Base | Tencent | 200000 | $3.64 | 74.0 |
| Llama 3 Novita | Novita AI | 128000 | $3.70 | 74.4 |
| Llama 3.3 Groq Instruct | Groq | 65536 | $4.08 | 76.7 |
| Llama 3 Hyperbolic Chat | Hyperbolic | 200000 | $3.64 | 75.1 |
| Reka Flash Pro | Reka AI | 200000 | $10.82 | 75.2 |
| Seed-OSS | ByteDance | 32768 | $3.74 | 75.3 |
Other benchmarks for Llama 3 Hyperbolic Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 76.4 | 293 | 51th | |
| GPQA Diamond | 72.7 | 204 | 66th | |
| SWE-bench Verified | 61.8 | 284 | 53th | |
| HumanEval | 79.8 | 239 | 60th | |
| AIME 2025 | 73.4 | 199 | 67th | |
| LiveCodeBench | 69.0 | 207 | 66th | |
| IFEval | 81.2 | 296 | 51th | |
| TruthfulQA | 77.7 | 263 | 56th | |
| HellaSwag | 84.8 | 256 | 57th | |
| MMMU | 70.5 | 281 | 53th | |
| Arena Elo | 1375.5 | 291 | 52th |
FAQ
What score does Llama 3 Hyperbolic Chat get on MATH-500?
Llama 3 Hyperbolic Chat scores 79.2 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.
How does Llama 3 Hyperbolic Chat rank on MATH-500?
It ranks #228 of 600 models, in the 62nd percentile. The leader is GPT-5.2 Max at 96.7.
Is Llama 3 Hyperbolic Chat good at MATH-500?
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 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 Chat cost?
Llama 3 Hyperbolic Chat costs $3.64 per million input tokens and $16.55 per million output tokens.