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.
284B109B
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.
$0.05Relace$0.08DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
$0.1652StreamLake$0.24DeepInfra
$24.78StreamLake · fp8$35.00DeepInfra · fp8
Benchmarks
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better.
18%20%
Overview
CompanyDeepSeekMeta
Release dateApr 24 2026Apr 5 2025
AccessOpen WeightOpen Weight
Model detailsView modelView model

Frequently asked questions

LLaMA 4 Scout leads DeepSeek-V4-Flash on 1 of the 1 benchmark they both report (BullshitBench v2). LLaMA 4 Scout shipped 384 days before DeepSeek-V4-Flash, so benchmark comparisons should account for the intervening progress.

DeepSeek-V4-Flash has 284B parameters, while LLaMA 4 Scout has 109B. Context windows are 1M (DeepSeek-V4-Flash) vs 10M (LLaMA 4 Scout).

On BullshitBench v2, LLaMA 4 Scout leads at 20% vs DeepSeek-V4-Flash at 18%.