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 | 128k |
| 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 | — |
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 | — | |
| 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 | OpenAI |
| Release date | Apr 5 2025 | Feb 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 4 ScoutvsClaude Haiku 5.5GPT-5.3-Codex-SparkvsClaude Haiku 5.5LLaMA 4 ScoutvsGemini 4 ArgonGPT-5.3-Codex-SparkvsGemini 4 ArgonLLaMA 4 ScoutvsGrok 4.7GPT-5.3-Codex-SparkvsGrok 4.7LLaMA 4 ScoutvsDeepSeek-V4.1-Flash-1017GPT-5.3-Codex-SparkvsDeepSeek-V4.1-Flash-1017LLaMA 4 ScoutvsMistral Large 4GPT-5.3-Codex-SparkvsMistral Large 4LLaMA 4 ScoutvsKimi K3GPT-5.3-Codex-SparkvsKimi K3Frequently asked questions
LLaMA 4 Scout and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 4 Scout shipped 313 days before GPT-5.3-Codex-Spark, so benchmark comparisons should account for the intervening progress.
Context windows are 10M (LLaMA 4 Scout) vs 128k (GPT-5.3-Codex-Spark). LLaMA 4 Scout is open weight, while GPT-5.3-Codex-Spark is closed.
Direct benchmark comparisons are unavailable — LLaMA 4 Scout and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks.