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.
109B8B/49B
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.
10M128k
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.
$0.08DeepInfra—
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
$0.24DeepInfra—
$35.00DeepInfra · fp8—
These models have no shared benchmark scores.
Overview
CompanyMetaNVIDIA
Release dateApr 5 2025Mar 18 2025
AccessOpen WeightOpen Weight
Model detailsView modelView model

Frequently asked questions

LLaMA 4 Scout and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Llama Nemotron (Nano/Super) shipped 18 days before LLaMA 4 Scout, so benchmark comparisons should account for the intervening progress.

LLaMA 4 Scout has 109B parameters, while Llama Nemotron (Nano/Super) has 8B/49B. Context windows are 10M (LLaMA 4 Scout) vs 128k (Llama Nemotron (Nano/Super)).

Direct benchmark comparisons are unavailable — LLaMA 4 Scout and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks.