Llama 4 Maverick
Meta's Llama 4 Maverick packs 400B total parameters into a 128-expert MoE architecture with only 17B active per token, beating GPT-4o on Chatbot Arena while matching DeepSeek V3 on reasoning at half the active parameters.
Llama 4
Llama 4 Scout
Meta's Llama 4 Scout is a 109B-total, 17B-active MoE model with 16 experts and a 10M-token context window - the longest of any open-weight model - with native multimodal support for text and images.
Qwen3.5-122B-A10B vs Llama 4 Maverick: The Efficiency Gap Nobody Expected
A data-driven comparison of Alibaba's Qwen3.5-122B-A10B and Meta's Llama 4 Maverick - two open-weight MoE models with radically different approaches to parameter efficiency and benchmark performance.
Qwen3.5-35B-A3B vs Llama 4 Scout: 3B Active Parameters vs 17B - Does 5.7x More Compute Actually Win?
David vs Goliath: Qwen3.5-35B-A3B activates 3B parameters and beats Llama 4 Scout's 17B active on MMLU-Pro, GPQA, and coding benchmarks - but Scout's 10M context window and native multimodal support tell a different story.