Llama 4 Maverick and DeepSeek V3.2 are both open-weight, MoE-based and self-hostable, but they emphasise different things. Maverick is Meta's natively multimodal flagship, with 400B total parameters (17B active), a 1M-token context and image understanding, and Meta says it beats GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks. It's the open choice when you need multimodality and a huge context.
DeepSeek V3.2 is the text/code efficiency specialist: sparse attention, strong reasoning-in-tool-use and lower pricing, but no native image input. Maverick wins on multimodality and context size; DeepSeek wins on cost-efficient reasoning and coding value. For multimodal open deployments, Maverick leads; for cheap, high-volume text and code, DeepSeek is the better fit.
Which should you choose?
Llama 4 Maverick
Llama 4 Maverick · 1M context
Pick it if
you need native multimodality and a 1M-token context.
DeepSeek V3.2
DeepSeek V3.2 · 163K context
Pick it if
you want cheap, efficient text and code reasoning.
Llama 4 Maverick wins on multimodality and context length; DeepSeek V3.2 wins on cost-efficient text and code reasoning. Choose by whether you need images or maximum value.
Questions
Is Llama 4 Maverick multimodal?
Yes. It natively handles text and images, while DeepSeek V3.2 focuses on text and code.
Which has the bigger context?
Maverick, with a 1M-token window versus DeepSeek's 128K.