Head-to-Head Model Comparison

Llama 4 Scout vs Llama 4 Maverick

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Llama 4 ScoutProfile

Llama 4 MaverickProfile

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Llama 4 Scout

Modelo Base de Pesos Abertos

O Llama 4 Scout da Meta é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 3.5M, velocidade de 95 tokens/s, índice de inteligência de 76/100 e tarif

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

3.5M tokens

SWE-bench Verified:

N/A

Llama 4 Maverick

IA Multimodal Nativa

O Llama 4 Maverick da Meta é um modelo de IA projetado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, oferecendo janela de contexto de 1M, velocidade de 85 tokens/s, índice de inteligência de 84/100 e t

Intelligence Index84 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

62%

Principais Diferenças em Resumo

Both models cost the same under this workload — choose on capability, not price.
Llama 4 Scout has a larger context window (4M vs 1M tokens).
Llama 4 Scout is faster at 95 tok/s vs 85 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Llama 4 Scout	Llama 4 Maverick
Intelligence Index Composite reasoning & knowledge score (0-100)	76	84
Provider	Meta	Meta
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	3.5M tokens~5,250 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	33K	33K
Throughput Speed Tokens generated per second	95 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	N/A	62%
GPQA Diamond Graduate-level reasoning	N/A	72.5%

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Llama 4 Scout

+12% faster generation

Context

Llama 4 Scout

3.5M tokens window

Pontuações de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Llama 4 ScoutLlama 4 Maverick

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs62%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs72.5%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

78%vs80.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1370ptsvs1415pts

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

92.5%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs85 tok/s

Latência e Tempo de Resposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Llama 4 Scout0.65s

Llama 4 Maverick0.55s

Est. End-to-End Response (500 tokens)TTFT + generation time

Llama 4 Scout5.91s

Llama 4 Maverick6.43s

Simulador Interativo de Custo de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Llama 4 Scout Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 4 Maverick Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Llama 4 Scout · Strengths & Key Advantages

Janela de contexto de 3.5M com limite máximo de saída de 33K tokens (MMLU-Pro 78% · Arena Elo 1370)
Taxa de transferência rápida de 95 tokens/segundo com latência inicial (TTFT) de 650 ms
Pesos abertos (17B (109B total MoE)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Llama 4 Maverick · Strengths & Key Advantages

Janela de contexto de 1M com limite máximo de saída de 33K tokens (SWE-bench Verified 62% · GPQA Diamond 72.5%)
Taxa de transferência rápida de 85 tokens/segundo com latência inicial (TTFT) de 550 ms
Pesos abertos (17B (400B total MoE)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Llama 4 Scout	Llama 4 Maverick
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	10K	15K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Llama 4 Scout vs Llama 4 Maverick: Respostas Rápidas (FAQ)

Which is more intelligent, Llama 4 Scout or Llama 4 Maverick?

Llama 4 Maverick is more intelligent. It scores 84 compared with 76 on the composite Intelligence Index.

Which model is faster?

Llama 4 Scout is faster, generating 95 tokens per second compared with 85 tokens per second.

Which model has lower latency?

Llama 4 Maverick has lower latency, with a time to first token of 0.55s compared with 0.65s.

Which model has a larger context window?

Llama 4 Scout has a larger context window, supporting 3.5M tokens compared with 1M tokens.

Veredito

Tanto Llama 4 Scout (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Llama 4 Maverick (Indicado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Llama 4 Maverick lidera em inteligência geral e programação, Llama 4 Scout oferece geração mais rápida e Llama 4 Scout apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Llama 4 Scout ou Llama 4 Maverick?+

Nas avaliações de inteligência e código, Llama 4 Maverick leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Llama 4 Scout ou Llama 4 Maverick?+

O Llama 4 Scout custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Llama 4 Maverick custa $0.00 / $0.00. No custo combinado, Llama 4 Scout é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Llama 4 Scout suporta 3.5M tokens de contexto contra 1M tokens do Llama 4 Maverick, enquanto Llama 4 Scout entrega maior velocidade de geração.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Feature	Llama 4 Scout	Llama 4 Maverick
Official API Model ID (SDK)	meta-llama/Llama-4-Scout	meta-llama/Llama-4-Maverick
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2025-04
Knowledge Cutoff	January 2025	January 2025
Architecture	Extreme-context sparse MoE multimodal transformer (17B active / 109B total)	High-capacity sparse MoE multimodal transformer (17B active / 400B total)
Parameters	17B (109B total MoE)	17B (400B total MoE)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text, vision
Output Modalities	text	text

Llama 4 Scout vs Llama 4 Maverick

Troque e Compare Qualquer Modelo

Llama 4 Scout

Llama 4 Maverick

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Llama 4 Scout · Strengths & Key Advantages

Considerations & Trade-offs

Llama 4 Maverick · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Llama 4 Scout vs Llama 4 Maverick: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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