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 | 22B |
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 | 32k |
| 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 | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.04DeepInfra | — |
| $6.00DeepInfra · 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. | 14% | — |
| Overview | ||
| Company | Meta | Mistral |
| Release date | Jul 23 2024 | May 29 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.1vsClaude Haiku 5.5Codestral 22BvsClaude Haiku 5.5LLaMA 3.1vsGPT-6.1 SolCodestral 22BvsGPT-6.1 SolLLaMA 3.1vsGemini 4 ArgonCodestral 22BvsGemini 4 ArgonLLaMA 3.1vsGrok 4.7Codestral 22BvsGrok 4.7LLaMA 3.1vsDeepSeek-V4.1-Flash-1017Codestral 22BvsDeepSeek-V4.1-Flash-1017LLaMA 3.1vsKimi K3Codestral 22BvsKimi K3Frequently asked questions
LLaMA 3.1 and Codestral 22B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Codestral 22B shipped 55 days before LLaMA 3.1, so benchmark comparisons should account for the intervening progress.
LLaMA 3.1 has 8B/70B/405B parameters, while Codestral 22B has 22B. Context windows are 128k (LLaMA 3.1) vs 32k (Codestral 22B). LLaMA 3.1 is open weight, while Codestral 22B is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.1 and Codestral 22B don't publish scores on any of the same benchmarks.