Llama 4 Maverick vs Llama 4 Scout
Llama 4's two personalities, Maverick capability against Scout efficiency.
What the numbers say
- Llama 4 Scout is about 2.0x cheaper on output tokens (3.3 vs 6.6 credits per 1M).
- Llama 4 Scout takes noticeably more context: 10M vs 1.0M tokens.
- Llama 4 Maverick is the newer release (April 2025 vs April 2025).
Derived from the live registry Zeplik routes and bills against. Credits: 1 credit = $0.10, raw provider rate with a 1.10x margin.
Side by side
Llama 4 Maverick
Meta
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
- Provider
- Meta
- Context window
- 1.0M tokens
- Input price
- 1.7 credits / 1M tokens ($0.15 raw)
- Output price
- 6.6 credits / 1M tokens ($0.60 raw)
- Vision (image input)
- Yes
- Tool calling
- Yes
- Extended reasoning
- No
Best for
- Everyday chat and drafting on an open-weight model with transparent lineage
- Very long documents and codebases: a 1.0M-token window fits entire books or repositories in one conversation
- Working with images: screenshots, charts, photos and scanned documents alongside text
- Tool use and agents: reliably calls functions, so it can search, run skills and drive workflows
Llama 4 Scout
Meta
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
- Provider
- Meta
- Context window
- 10M tokens
- Input price
- 1.1 credits / 1M tokens ($0.10 raw)
- Output price
- 3.3 credits / 1M tokens ($0.30 raw)
- Vision (image input)
- Yes
- Tool calling
- Yes
- Extended reasoning
- No
Best for
- Everyday chat and drafting on an open-weight model with transparent lineage
- Very long documents and codebases: a 10M-token window fits entire books or repositories in one conversation
- Working with images: screenshots, charts, photos and scanned documents alongside text
- Tool use and agents: reliably calls functions, so it can search, run skills and drive workflows
Try both on Zeplik
The honest answer to most model debates is to run your own prompt on both. Zeplik puts Llama 4 Maverick and Llama 4 Scout in the same chat, so you can switch mid-conversation and compare answers on the work you actually do.