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 | 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. | 256k | 32k |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Jul 16 2024 | Nov 30 2023 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Codestral Mamba 7BvsClaude Haiku 5.5Qwen-72BvsClaude Haiku 5.5Codestral Mamba 7BvsGPT-6.1 SolQwen-72BvsGPT-6.1 SolCodestral Mamba 7BvsGemini 4 ArgonQwen-72BvsGemini 4 ArgonCodestral Mamba 7BvsMuse Spark OpenQwen-72BvsMuse Spark OpenCodestral Mamba 7BvsGrok 4.7Qwen-72BvsGrok 4.7Codestral Mamba 7BvsDeepSeek-V4.1-Flash-1017Qwen-72BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Codestral Mamba 7B and Qwen-72B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen-72B shipped 229 days before Codestral Mamba 7B, so benchmark comparisons should account for the intervening progress.
Codestral Mamba 7B has 7B parameters, while Qwen-72B has 72B. Context windows are 256k (Codestral Mamba 7B) vs 32k (Qwen-72B).
Direct benchmark comparisons are unavailable — Codestral Mamba 7B and Qwen-72B don't publish scores on any of the same benchmarks.