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.
22B0.5B/1.5B/3B/7B/14B/32B/72B
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.
32k128k
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.
—$0.36DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$0.40DeepInfra
—$92.00DeepInfra · fp8
Overview
CompanyMistralQwen
Release dateMay 29 2024Sep 19 2024
AccessClosedOpen Weight
Model detailsView modelView model

Frequently asked questions

Codestral 22B and Qwen2.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Codestral 22B shipped 113 days before Qwen2.5, so benchmark comparisons should account for the intervening progress.

Codestral 22B has 22B parameters, while Qwen2.5 has 0.5B/1.5B/3B/7B/14B/32B/72B. Context windows are 32k (Codestral 22B) vs 128k (Qwen2.5). Codestral 22B is closed, while Qwen2.5 is open weight.

Direct benchmark comparisons are unavailable — Codestral 22B and Qwen2.5 don't publish scores on any of the same benchmarks.