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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.
70B0.5B/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.
16k32k
Overview
CompanyMetaQwen
Release dateJan 29 2024Feb 4 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

Code Llama 70BvsClaude Haiku 5.5Qwen1.5vsClaude Haiku 5.5Code Llama 70BvsGPT-6.1 SolQwen1.5vsGPT-6.1 SolCode Llama 70BvsGemini 4 ArgonQwen1.5vsGemini 4 ArgonCode Llama 70BvsGrok 4.7Qwen1.5vsGrok 4.7Code Llama 70BvsDeepSeek-V4.1-Flash-1017Qwen1.5vsDeepSeek-V4.1-Flash-1017Code Llama 70BvsMistral Large 4Qwen1.5vsMistral Large 4

Frequently asked questions

Code Llama 70B and Qwen1.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama 70B shipped 6 days before Qwen1.5, so benchmark comparisons should account for the intervening progress.

Code Llama 70B has 70B parameters, while Qwen1.5 has 0.5B/1.8B/4B/7B/14B/72B. Context windows are 16k (Code Llama 70B) vs 32k (Qwen1.5).

Direct benchmark comparisons are unavailable — Code Llama 70B and Qwen1.5 don't publish scores on any of the same benchmarks.