Data story · provider

Groq: model lineup and pricing overview

Groq model lineup with prices, context windows and average benchmark scores. Inference company building LPU accelerators.

What the data shows

Groq has 14 models in the index. The average input price is $2.30 and the average output price is $10.23 per million tokens.

The strongest model by average score is llama-3-3-groq. The cheapest by input price is Mixtral Groq 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.

Groq models

#ModelInput / 1MOutput / 1MContextAvg score
1Llama 3.3 Groq$3.98$16.2032,76876.8
2Llama 3.3 Groq Chat$4.06$19.9832,76876.8
3Llama 3.3 Groq Instruct$4.08$22.2765,53676.7
4Llama 3.3 Groq Base$3.24$12.2816,38473.5
5Mixtral Groq$3.37$15.2316,38473.1
6Mixtral Groq Instruct$3.25$16.0116,38472.5
7Mixtral Groq Chat$3.09$10.838,19272.0
8Mixtral Groq Base$2.66$11.3532,76868.4
9Llama 3.3 Groq 8B$1.17$6.1616,38459.5
10Llama 3.3 Groq 7B$1.01$3.6016,38456.7
11Mixtral Groq 8B$0.77$3.7932,76854.8
12Mixtral Groq 7B$0.70$2.474,09653.4
13Llama 3.3 Groq 1.5B$0.46$1.804,09650.0
14Mixtral Groq 1.5B$0.34$1.244,09646.1

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 Groq: model lineup and pricing overview data show?

Groq model lineup with prices, context windows and average benchmark scores. Inference company building LPU accelerators.

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.