Llama 4 Scout vs. Maverick

Meta's Llama 4 family splits into Scout (lean) and Maverick (flagship). Here is how to choose between them for self-hosted work.

28 min · Reviewed 2026

Two open-weight siblings

Llama 4 Scout is the compact sibling — cheap to host, fast per token, strong on mainstream tasks. Maverick is the flagship — wider mixture-of-experts, stronger reasoning, bigger GPU bill. Both ship under Meta's community license.

Aspect	Llama 4 Scout	Llama 4 Maverick
Active params	Smaller MoE	Larger MoE
GPU footprint	Fits on 1x H100 inference	Multi-GPU
Quality tier	Sonnet-class	Near-frontier
Cost per M (hosted)	$	$$
Best for	RAG, chat, agents at scale	Complex reasoning, code

Pick Scout when

You serve high QPS on a single-box budget
Latency matters more than peak quality
You are fine-tuning for a narrow domain

Pick Maverick when

Quality matches or beats frontier APIs for your eval
You have multi-GPU capacity or use Together/Fireworks/Bedrock
Data residency rules forbid public APIs

ollama pull llama4:scout ollama run llama4:scout "Summarize this support ticket"Scout runs locally on a decent workstation GPU. Maverick usually does not.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-modelx-llama4-scout-maverick-builders

What is the main idea of "Llama 4 Scout vs. Maverick"?
1. Meta's Llama 4 family splits into Scout (lean) and Maverick (flagship). Here is how to choose between them for self-hosted work.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "Llama 4 Scout vs. Maverick"?
1. Llama 4 Maverick
2. Llama 4 Scout
3. open weights
4. mixture of experts
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. You serve high QPS on a single-box budget
4. Use the first answer without checking it
What should a careful learner remember about "Hosted is fine"?
1. Use AI to draft or organize ideas about Llama 4 Scout, then verify before acting.
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use the AI answer as a draft, then check it against a reliable source.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about Llama 4 Scout be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about Llama 4 Scout.
Which action would help you apply "Llama 4 Scout vs. Maverick" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Use the first answer without checking it
4. Latency matters more than peak quality

← Back to interactive lesson

Tendril · Builders · Model Families

Llama 4 Scout vs. Maverick

Meta's Llama 4 family splits into Scout (lean) and Maverick (flagship). Here is how to choose between them for self-hosted work.

28 min · Reviewed 2026

Two open-weight siblings

Aspect	Llama 4 Scout	Llama 4 Maverick
Active params	Smaller MoE	Larger MoE
GPU footprint	Fits on 1x H100 inference	Multi-GPU
Quality tier	Sonnet-class	Near-frontier
Cost per M (hosted)	$	$$
Best for	RAG, chat, agents at scale	Complex reasoning, code

Pick Scout when

You serve high QPS on a single-box budget
Latency matters more than peak quality
You are fine-tuning for a narrow domain

Pick Maverick when

Quality matches or beats frontier APIs for your eval
You have multi-GPU capacity or use Together/Fireworks/Bedrock
Data residency rules forbid public APIs

ollama pull llama4:scout ollama run llama4:scout "Summarize this support ticket"Scout runs locally on a decent workstation GPU. Maverick usually does not.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-modelx-llama4-scout-maverick-builders

What is the main idea of "Llama 4 Scout vs. Maverick"?
1. Meta's Llama 4 family splits into Scout (lean) and Maverick (flagship). Here is how to choose between them for self-hosted work.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "Llama 4 Scout vs. Maverick"?
1. Llama 4 Maverick
2. Llama 4 Scout
3. open weights
4. mixture of experts
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. You serve high QPS on a single-box budget
4. Use the first answer without checking it
What should a careful learner remember about "Hosted is fine"?
1. Use AI to draft or organize ideas about Llama 4 Scout, then verify before acting.
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use the AI answer as a draft, then check it against a reliable source.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about Llama 4 Scout be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about Llama 4 Scout.
Which action would help you apply "Llama 4 Scout vs. Maverick" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Use the first answer without checking it
4. Latency matters more than peak quality

← Back to interactive lesson