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. | — | 39% |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Sep 25 2024 | Sep 15 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.2vsClaude Haiku 5.5GPT-5-CodexvsClaude Haiku 5.5LLaMA 3.2vsGemini 4 ArgonGPT-5-CodexvsGemini 4 ArgonLLaMA 3.2vsGrok 4.7GPT-5-CodexvsGrok 4.7LLaMA 3.2vsDeepSeek-V4.1-Flash-1017GPT-5-CodexvsDeepSeek-V4.1-Flash-1017LLaMA 3.2vsMistral Large 4GPT-5-CodexvsMistral Large 4LLaMA 3.2vsKimi K3GPT-5-CodexvsKimi K3Frequently asked questions
LLaMA 3.2 and GPT-5-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.2 shipped 355 days before GPT-5-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.2) vs 400k (GPT-5-Codex). LLaMA 3.2 is open weight, while GPT-5-Codex is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.2 and GPT-5-Codex don't publish scores on any of the same benchmarks.