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.
1.8B/3.25B109B
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.
32k10M
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 dateDec 6 2023Apr 5 2025
AccessClosedOpen Weight
Model detailsView modelView model

Frequently asked questions

Gemini 1.0 Nano and LLaMA 4 Scout don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.0 Nano shipped 486 days before LLaMA 4 Scout, so benchmark comparisons should account for the intervening progress.

Gemini 1.0 Nano has 1.8B/3.25B parameters, while LLaMA 4 Scout has 109B. Context windows are 32k (Gemini 1.0 Nano) vs 10M (LLaMA 4 Scout). Gemini 1.0 Nano is closed, while LLaMA 4 Scout is open weight.

Direct benchmark comparisons are unavailable — Gemini 1.0 Nano and LLaMA 4 Scout don't publish scores on any of the same benchmarks.