Data story · provider

Fireworks AI: model lineup and pricing overview

Fireworks AI model lineup with prices, context windows and average benchmark scores. Inference platform for open and custom models.

What the data shows

Fireworks AI has 14 models in the index. The average input price is $6.84 and the average output price is $31.35 per million tokens.

The strongest model by average score is firefunction-max. The cheapest by input price is Firesearch Nano.

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.

Fireworks AI models

#ModelInput / 1MOutput / 1MContextAvg score
1Firefunction Max$9.12$43.0832,76873.2
2Firefunction Pro$9.05$49.4116,38472.9
3Firefunction Reasoning$9.08$42.168,19272.5
4Firefunction$8.16$41.4016,38472.3
5Firefunction Turbo$8.44$36.8332,76870.7
6Firesearch Pro$7.67$32.8332,76870.4
7Firesearch Max$7.80$31.808,19269.9
8Firesearch Turbo$7.58$33.4416,38469.3
9Firesearch Reasoning$7.74$33.4732,76869.0
10Firesearch$7.03$29.8132,76868.5
11Firefunction Mini$4.63$24.2632,76863.2
12Firesearch Mini$3.94$16.7632,76861.5
13Firefunction Nano$3.07$12.5016,38458.1
14Firesearch Nano$2.49$11.168,19255.6

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 Fireworks AI: model lineup and pricing overview data show?

Fireworks AI model lineup with prices, context windows and average benchmark scores. Inference platform for open and custom models.

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.