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/13B/34B | 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. | 16k | 32k |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Aug 24 2023 | Nov 30 2023 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Code LlamavsClaude Haiku 5.5Qwen-72BvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolQwen-72BvsGPT-6.1 SolCode LlamavsGemini 4 ArgonQwen-72BvsGemini 4 ArgonCode LlamavsGrok 4.7Qwen-72BvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017Qwen-72BvsDeepSeek-V4.1-Flash-1017Code LlamavsMistral Large 4Qwen-72BvsMistral Large 4Frequently asked questions
Code Llama and Qwen-72B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 98 days before Qwen-72B, so benchmark comparisons should account for the intervening progress.
Code Llama has 7B/13B/34B parameters, while Qwen-72B has 72B. Context windows are 16k (Code Llama) vs 32k (Qwen-72B).
Direct benchmark comparisons are unavailable — Code Llama and Qwen-72B don't publish scores on any of the same benchmarks.