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. | 70B | 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. | 128k | 10M |
| 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 | $0.10DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.32DeepInfra | $0.30DeepInfra |
| $36.00DeepInfra · turbo · fp8 | $35.00DeepInfra · fp8 | |
| These models have no shared benchmark scores. | ||
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. | — | 20% |
| Overview | ||
| Company | Meta | Meta |
| Release date | Dec 6 2024 | Apr 5 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 3.3vsClaude Haiku 5.5LLaMA 4 ScoutvsClaude Haiku 5.5LLaMA 3.3vsGPT-6.1 SolLLaMA 4 ScoutvsGPT-6.1 SolLLaMA 3.3vsGemini 4 ArgonLLaMA 4 ScoutvsGemini 4 ArgonLLaMA 3.3vsGrok 4.7LLaMA 4 ScoutvsGrok 4.7LLaMA 3.3vsDeepSeek-V4.1-Flash-1017LLaMA 4 ScoutvsDeepSeek-V4.1-Flash-1017LLaMA 3.3vsMistral Large 4LLaMA 4 ScoutvsMistral Large 4Frequently asked questions
LLaMA 3.3 and LLaMA 4 Scout don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.3 shipped 120 days before LLaMA 4 Scout, so benchmark comparisons should account for the intervening progress.
LLaMA 3.3 has 70B parameters, while LLaMA 4 Scout has 109B. Context windows are 128k (LLaMA 3.3) vs 10M (LLaMA 4 Scout).
Direct benchmark comparisons are unavailable — LLaMA 3.3 and LLaMA 4 Scout don't publish scores on any of the same benchmarks.