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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.
7B/13B/70B0.5B/1.5B/7B/57B/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.
4k128k
Overview
CompanyMetaQwen
Release dateJul 18 2023Jun 6 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

LLaMA 2vsClaude Haiku 5.5Qwen2vsClaude Haiku 5.5LLaMA 2vsGPT-6.1 SolQwen2vsGPT-6.1 SolLLaMA 2vsGemini 4 ArgonQwen2vsGemini 4 ArgonLLaMA 2vsGrok 4.7Qwen2vsGrok 4.7LLaMA 2vsDeepSeek-V4.1-Flash-1017Qwen2vsDeepSeek-V4.1-Flash-1017LLaMA 2vsMistral Large 4Qwen2vsMistral Large 4

Frequently asked questions

LLaMA 2 and Qwen2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 2 shipped 324 days before Qwen2, so benchmark comparisons should account for the intervening progress.

LLaMA 2 has 7B/13B/70B parameters, while Qwen2 has 0.5B/1.5B/7B/57B/72B. Context windows are 4k (LLaMA 2) vs 128k (Qwen2).

Direct benchmark comparisons are unavailable — LLaMA 2 and Qwen2 don't publish scores on any of the same benchmarks.