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. | 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. | 10M | 256k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.00 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $2.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.20 |
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 | $300SpaceXSI list price | |
| 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% | — |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 70.8% |
| Overview | ||
| Company | Meta | SpaceXSI |
| Release date | Apr 5 2025 | Aug 28 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 4 ScoutvsClaude Haiku 5.5Grok Code Fast 1vsClaude Haiku 5.5LLaMA 4 ScoutvsGPT-6.1 SolGrok Code Fast 1vsGPT-6.1 SolLLaMA 4 ScoutvsGemini 4 ArgonGrok Code Fast 1vsGemini 4 ArgonLLaMA 4 ScoutvsDeepSeek-V4.1-Flash-1017Grok Code Fast 1vsDeepSeek-V4.1-Flash-1017LLaMA 4 ScoutvsMistral Large 4Grok Code Fast 1vsMistral Large 4LLaMA 4 ScoutvsKimi K3Grok Code Fast 1vsKimi K3Frequently asked questions
LLaMA 4 Scout and Grok Code Fast 1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Grok Code Fast 1 has a verified first-party API price: $1.00 per million input tokens and $2.00 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 4 Scout. LLaMA 4 Scout shipped 145 days before Grok Code Fast 1, so benchmark comparisons should account for the intervening progress.
Context windows are 10M (LLaMA 4 Scout) vs 256k (Grok Code Fast 1). LLaMA 4 Scout is open weight, while Grok Code Fast 1 is closed.
Direct benchmark comparisons are unavailable — LLaMA 4 Scout and Grok Code Fast 1 don't publish scores on any of the same benchmarks.