Model comparison
Llama 4 Scout vs Llama 4 Maverick
The most significant observable difference is the maximum token context, with Llama 4 Maverick offering over three times the capacity of Llama 4 Scout.
Meta
Llama 4 Scout
Open-weights frontier with a headline 10M-token context.
Meta
Llama 4 Maverick
The bigger Llama 4 — frontier quality you can self-host.
Specs
| Metric | Llama 4 Scout | Llama 4 Maverick |
|---|---|---|
| Context window | 328K tokens | 1.0M tokens↑ |
| Input $/1M tokens | $0.080↑ | $0.150 |
| Output $/1M tokens | $0.300↑ | $0.600 |
| Modalities | Text · Image | Text · Image |
| Open weights | Yes | Yes |
| Released | Apr 2025 | Apr 2025 |
How they differ
Context handling
Llama 4 Scout
Llama 4 Scout supports a context window of up to 327,680 tokens, making it suitable for medium-length interactions.
Llama 4 Maverick
Llama 4 Maverick supports a context window of up to 1,048,576 tokens, enabling it to process longer and more complex inputs.
Cost profile
Llama 4 Scout
Llama 4 Scout is more cost-efficient at $0.08 per 1M input tokens and $0.3 per 1M output tokens.
Llama 4 Maverick
Llama 4 Maverick's higher cost reflects its expansive capabilities, at $0.15 per 1M input tokens and $0.6 per 1M output tokens.
Speed
Llama 4 Scout
Llama 4 Scout provides faster response times for shorter contexts, optimized for lower token limits.
Llama 4 Maverick
Llama 4 Maverick may exhibit slower throughput for smaller inputs due to its design for large-context tasks.
Reasoning approach
Llama 4 Scout
Llama 4 Scout is optimized for shorter reasoning scenarios requiring less extensive context.
Llama 4 Maverick
Llama 4 Maverick excels in extended reasoning tasks by leveraging its large token context for intricate problem-solving.
Vision
Llama 4 Scout
Llama 4 Scout supports multimodal tasks but struggles with larger joint text-image contexts.
Llama 4 Maverick
Llama 4 Maverick supports detailed multimodal reasoning over high-context text-image inputs.
Llama 4 Scout — what sets it apart
- +Llama 4 Scout's smaller context and lower costs make it ideal for lightweight, shorter interactions and budget-focused projects.
- +It demonstrates efficiency in handling modular or segmented workloads within its context limits.
Llama 4 Maverick — what sets it apart
- +Llama 4 Maverick offers the largest publicly documented token context at 1,048,576 tokens.
- +It is particularly well-suited for maintaining continuity across highly complex or long problem spaces.
The most consequential difference is between Llama 4 Maverick's ability to handle extensive context for complex tasks and Llama 4 Scout's efficiency and affordability for shorter, simpler interactions.
Analysis synthesized from gpt-4o, llama-4-maverick, phi-4, etc.