Compare AI models

Specifications
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality.
—109B
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider.
1M10M
API pricingUSD per 1M tokens · lower wins
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page.
—$0.10DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$0.30DeepInfra
—$35.00DeepInfra · fp8
These models have no shared benchmark scores.
Overview
CompanyGoogleMeta
Release dateFeb 15 2024Apr 5 2025
AccessClosedOpen Weight
Model detailsView modelView model

Frequently asked questions

Gemini 1.5 Pro and LLaMA 4 Scout don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.5 Pro shipped 415 days before LLaMA 4 Scout, so benchmark comparisons should account for the intervening progress.

Context windows are 1M (Gemini 1.5 Pro) vs 10M (LLaMA 4 Scout). Gemini 1.5 Pro is closed, while LLaMA 4 Scout is open weight.

Direct benchmark comparisons are unavailable — Gemini 1.5 Pro and LLaMA 4 Scout don't publish scores on any of the same benchmarks.