Hyperbolic: model lineup and pricing overview
Hyperbolic model lineup with prices, context windows and average benchmark scores. Open-access AI cloud and inference network.
What the data shows
Hyperbolic has 7 models in the index. The average input price is $2.37 and the average output price is $10.45 per million tokens.
The strongest model by average score is llama-3-hyperbolic-instruct. The cheapest by input price is Llama 3 Hyperbolic 1.5B.
How to read the lineup
- Start with the best model for quality-sensitive work.
- Use the cheapest model for high-volume tasks.
- Check open-weight availability if self-hosting matters.
- Keep a second provider for fallback.
Hyperbolic models
| # | Model | Input / 1M | Output / 1M | Context | Avg score |
|---|---|---|---|---|---|
| 1 | Llama 3 Hyperbolic Instruct | $3.79 | $16.16 | 200,000 | 76.2 |
| 2 | Llama 3 Hyperbolic Chat | $3.64 | $16.55 | 200,000 | 75.1 |
| 3 | Llama 3 Hyperbolic | $3.86 | $19.72 | 65,536 | 74.5 |
| 4 | Llama 3 Hyperbolic Base | $3.02 | $10.87 | 32,768 | 71.1 |
| 5 | Llama 3 Hyperbolic 8B | $0.92 | $4.50 | 16,384 | 56.0 |
| 6 | Llama 3 Hyperbolic 7B | $0.94 | $3.67 | 4,096 | 55.6 |
| 7 | Llama 3 Hyperbolic 1.5B | $0.45 | $1.70 | 32,768 | 48.4 |
Methodology
This story uses the AI & LLM Index dataset: 600 models, 40 providers and 12 benchmarks. Prices are listed per million tokens. Scores are modelled estimates on a 0–100 scale and are not official benchmark results. Figures are rounded for readability.
Related
FAQ
What does the Hyperbolic: model lineup and pricing overview data show?
Hyperbolic model lineup with prices, context windows and average benchmark scores. Open-access AI cloud and inference network.
Are the numbers official?
No. Prices and scores are compiled for this demo index. Benchmark values are modelled estimates, not official results. Verify figures with the provider or benchmark owner before making decisions.
How should I use this ranking?
Use it to form a shortlist. Check the specific model pages, compare price and context, and test the shortlist on your own examples before committing.