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 | 0.5B/1.8B/4B/7B/14B/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. | 32k | 32k |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | May 29 2024 | Feb 4 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Codestral 22BvsClaude Haiku 5.5Qwen1.5vsClaude Haiku 5.5Codestral 22BvsGPT-6.1 SolQwen1.5vsGPT-6.1 SolCodestral 22BvsGemini 4 ArgonQwen1.5vsGemini 4 ArgonCodestral 22BvsMuse Spark OpenQwen1.5vsMuse Spark OpenCodestral 22BvsGrok 4.7Qwen1.5vsGrok 4.7Codestral 22BvsDeepSeek-V4.1-Flash-1017Qwen1.5vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Codestral 22B and Qwen1.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen1.5 shipped 115 days before Codestral 22B, so benchmark comparisons should account for the intervening progress.
Codestral 22B has 22B parameters, while Qwen1.5 has 0.5B/1.8B/4B/7B/14B/72B. Context windows are 32k (Codestral 22B) vs 32k (Qwen1.5). Codestral 22B is closed, while Qwen1.5 is open weight.
Direct benchmark comparisons are unavailable — Codestral 22B and Qwen1.5 don't publish scores on any of the same benchmarks.