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.
109B1T
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.
10M256k
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$0.60Novita
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
$0.24DeepInfra$2.50Novita
$35.00DeepInfra · fp8$245Novita · fp8
These models have no shared benchmark scores.
Overview
CompanyMetaMoonshot AI
Release dateApr 5 2025Sep 5 2025
AccessOpen WeightOpen Weight
Model detailsView modelView model

Frequently asked questions

LLaMA 4 Scout and Kimi K2 (0905) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 4 Scout shipped 153 days before Kimi K2 (0905), so benchmark comparisons should account for the intervening progress.

LLaMA 4 Scout has 109B parameters, while Kimi K2 (0905) has 1T. Context windows are 10M (LLaMA 4 Scout) vs 256k (Kimi K2 (0905)).

Direct benchmark comparisons are unavailable — LLaMA 4 Scout and Kimi K2 (0905) don't publish scores on any of the same benchmarks.