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. | 7B/13B/34B | 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. | 16k | 32k |
| Overview | ||
| Company | Meta | Mistral |
| Release date | Aug 24 2023 | May 29 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Code LlamavsClaude Haiku 5.5Codestral 22BvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolCodestral 22BvsGPT-6.1 SolCode LlamavsGemini 4 ArgonCodestral 22BvsGemini 4 ArgonCode LlamavsGrok 4.7Codestral 22BvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017Codestral 22BvsDeepSeek-V4.1-Flash-1017Code LlamavsKimi K3Codestral 22BvsKimi K3Frequently asked questions
Code Llama and Codestral 22B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 279 days before Codestral 22B, so benchmark comparisons should account for the intervening progress.
Code Llama has 7B/13B/34B parameters, while Codestral 22B has 22B. Context windows are 16k (Code Llama) vs 32k (Codestral 22B). Code Llama is open weight, while Codestral 22B is closed.
Direct benchmark comparisons are unavailable — Code Llama and Codestral 22B don't publish scores on any of the same benchmarks.