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. | 8B/70B/405B | — |
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. | $0.02DeepInfra | $1.25Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.04DeepInfra | $10.00Azure |
| $6.00DeepInfra · fp8 | $750Azure | |
| 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. | 14% | — |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Jul 23 2024 | Nov 19 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.1vsClaude Haiku 5.5GPT-5.1-Codex-MaxvsClaude Haiku 5.5LLaMA 3.1vsGemini 4 ArgonGPT-5.1-Codex-MaxvsGemini 4 ArgonLLaMA 3.1vsGrok 4.7GPT-5.1-Codex-MaxvsGrok 4.7LLaMA 3.1vsDeepSeek-V4.1-Flash-1017GPT-5.1-Codex-MaxvsDeepSeek-V4.1-Flash-1017LLaMA 3.1vsMistral Large 4GPT-5.1-Codex-MaxvsMistral Large 4LLaMA 3.1vsKimi K3GPT-5.1-Codex-MaxvsKimi K3Frequently asked questions
LLaMA 3.1 and GPT-5.1-Codex-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.1 shipped 484 days before GPT-5.1-Codex-Max, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.1) vs 400k (GPT-5.1-Codex-Max). LLaMA 3.1 is open weight, while GPT-5.1-Codex-Max is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.1 and GPT-5.1-Codex-Max don't publish scores on any of the same benchmarks.