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 | — |
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 |
| 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. | — | $2.00Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $6.00Mistral |
| — | $700Mistral | |
| Overview | ||
| Company | Meta | Mistral |
| Release date | Aug 24 2023 | Feb 26 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
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Code Llama and Mistral Large don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 186 days before Mistral Large, so benchmark comparisons should account for the intervening progress.
Context windows are 16k (Code Llama) vs 32k (Mistral Large). Code Llama is open weight, while Mistral Large is closed.
Direct benchmark comparisons are unavailable — Code Llama and Mistral Large don't publish scores on any of the same benchmarks.