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. | 123B | 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. | 128k | 32k |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Nov 18 2024 | Feb 4 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Mistral Large 2 24.11vsClaude Haiku 5.5Qwen1.5vsClaude Haiku 5.5Mistral Large 2 24.11vsGPT-6.1 SolQwen1.5vsGPT-6.1 SolMistral Large 2 24.11vsGemini 4 ArgonQwen1.5vsGemini 4 ArgonMistral Large 2 24.11vsMuse Spark OpenQwen1.5vsMuse Spark OpenMistral Large 2 24.11vsGrok 4.7Qwen1.5vsGrok 4.7Mistral Large 2 24.11vsDeepSeek-V4.1-Flash-1017Qwen1.5vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Mistral Large 2 24.11 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 288 days before Mistral Large 2 24.11, so benchmark comparisons should account for the intervening progress.
Mistral Large 2 24.11 has 123B parameters, while Qwen1.5 has 0.5B/1.8B/4B/7B/14B/72B. Context windows are 128k (Mistral Large 2 24.11) vs 32k (Qwen1.5).
Direct benchmark comparisons are unavailable — Mistral Large 2 24.11 and Qwen1.5 don't publish scores on any of the same benchmarks.