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 | 400k |
| 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 | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.24DeepInfra | — |
| $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. | 20% | 32% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 32.8% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 57.2% | — |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | 74.3% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 90.6% | — |
MathVistaMultimodal reasoning — mathematical reasoning about visual information | 70.7% | — |
MMMU-Pro · standard and vision meanMultimodal reasoning — average across the standard and vision sets | 52.2% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 69.4% | — |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Apr 5 2025 | Nov 13 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 4 ScoutvsClaude Fable 5.2GPT-5.1-CodexvsClaude Fable 5.2LLaMA 4 ScoutvsGemini 4 CarbonGPT-5.1-CodexvsGemini 4 CarbonLLaMA 4 ScoutvsGrok 4.7GPT-5.1-CodexvsGrok 4.7LLaMA 4 ScoutvsDeepSeek-V4.1-Flash-1017GPT-5.1-CodexvsDeepSeek-V4.1-Flash-1017LLaMA 4 ScoutvsMistral Large 4GPT-5.1-CodexvsMistral Large 4LLaMA 4 ScoutvsKimi K3.1 ProGPT-5.1-CodexvsKimi K3.1 ProFrequently asked questions
GPT-5.1-Codex leads LLaMA 4 Scout on 1 of the 1 benchmark they both report (BullshitBench v2). LLaMA 4 Scout shipped 222 days before GPT-5.1-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 10M (LLaMA 4 Scout) vs 400k (GPT-5.1-Codex). LLaMA 4 Scout is open weight, while GPT-5.1-Codex is closed.
On BullshitBench v2, GPT-5.1-Codex leads at 32% vs LLaMA 4 Scout at 20%.