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. | 12B | 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 | Sep 11 2024 | Feb 4 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Pixtral 24.09vsClaude Haiku 5.5Qwen1.5vsClaude Haiku 5.5Pixtral 24.09vsGPT-6.1 SolQwen1.5vsGPT-6.1 SolPixtral 24.09vsGemini 4 ArgonQwen1.5vsGemini 4 ArgonPixtral 24.09vsMuse Spark OpenQwen1.5vsMuse Spark OpenPixtral 24.09vsGrok 4.7Qwen1.5vsGrok 4.7Pixtral 24.09vsDeepSeek-V4.1-Flash-1017Qwen1.5vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Pixtral 24.09 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 220 days before Pixtral 24.09, so benchmark comparisons should account for the intervening progress.
Pixtral 24.09 has 12B parameters, while Qwen1.5 has 0.5B/1.8B/4B/7B/14B/72B. Context windows are 128k (Pixtral 24.09) vs 32k (Qwen1.5).
Direct benchmark comparisons are unavailable — Pixtral 24.09 and Qwen1.5 don't publish scores on any of the same benchmarks.