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. | 22B | 7B |
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. | 32k | 2k |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | May 29 2024 | Aug 3 2023 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Codestral 22BvsClaude Haiku 5.5Qwen-7BvsClaude Haiku 5.5Codestral 22BvsGPT-6.1 SolQwen-7BvsGPT-6.1 SolCodestral 22BvsGemini 4 ArgonQwen-7BvsGemini 4 ArgonCodestral 22BvsMuse Spark OpenQwen-7BvsMuse Spark OpenCodestral 22BvsGrok 4.7Qwen-7BvsGrok 4.7Codestral 22BvsDeepSeek-V4.1-Flash-1017Qwen-7BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Codestral 22B and Qwen-7B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen-7B shipped 300 days before Codestral 22B, so benchmark comparisons should account for the intervening progress.
Codestral 22B has 22B parameters, while Qwen-7B has 7B. Context windows are 32k (Codestral 22B) vs 2k (Qwen-7B). Codestral 22B is closed, while Qwen-7B is open weight.
Direct benchmark comparisons are unavailable — Codestral 22B and Qwen-7B don't publish scores on any of the same benchmarks.