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. | 1B/3B/11B/90B | — |
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 | 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. | $0.027Cloudflare | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.201Cloudflare | — |
| $15.45Cloudflare | — | |
| 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. | — | 32% |
MATHReasoning — accuracy on the original competition mathematics test set | 68% | — |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | 86% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 86.9% | — |
MathVistaMultimodal reasoning — mathematical reasoning about visual information | 57.3% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 60.3% | — |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Sep 25 2024 | Nov 13 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.2vsClaude Fable 5.2GPT-5.1-CodexvsClaude Fable 5.2LLaMA 3.2vsGemini 4 CarbonGPT-5.1-CodexvsGemini 4 CarbonLLaMA 3.2vsGrok 4.7GPT-5.1-CodexvsGrok 4.7LLaMA 3.2vsDeepSeek-V4.1-Flash-1017GPT-5.1-CodexvsDeepSeek-V4.1-Flash-1017LLaMA 3.2vsMistral Large 4GPT-5.1-CodexvsMistral Large 4LLaMA 3.2vsKimi K3.1 ProGPT-5.1-CodexvsKimi K3.1 ProFrequently asked questions
LLaMA 3.2 and GPT-5.1-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.2 shipped 414 days before GPT-5.1-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.2) vs 400k (GPT-5.1-Codex). LLaMA 3.2 is open weight, while GPT-5.1-Codex is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.2 and GPT-5.1-Codex don't publish scores on any of the same benchmarks.