Head-to-Head Model Comparison

Llama 4 Scout vs Llama 4 Maverick

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

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Llama 4 ScoutProfile

Llama 4 MaverickProfile

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Llama 4 Scout

Açık Ağırlıklı Temel Model

Meta tarafından geliştirilen Llama 4 Scout; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 3.5M bağlam penceresi, 95 token/sn hız, 76/100 zeka indeksi ve ücretsiz açık ağırlık e

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

Yerel Çok Modlu Yapay Zeka

Meta tarafından geliştirilen Llama 4 Maverick; yerel çok modlu metin ve görsel anlama ile karmaşık belge analizi için tasarlanmış olup 1M bağlam penceresi, 85 token/sn hız, 84/100 zeka indeksi ve ücretsiz açık ağırlık erişimi sunar.

Intelligence Index84 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

62%

Bir Bakışta Temel Farklar

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%

Hangi Alanda Kim Kazanıyor?

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

Doğrulanmış Benchmark Puanları

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

Gecikme Süresi ve Yanıt Hızı

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

Gerçek Zamanlı Token Maliyet Simülatörü

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

3.5M bağlam penceresi ve yanıt başına maksimum 33K çıktı tokenı kapasitesi (MMLU-Pro 78% · Arena Elo 1370)
Sadece 650 ms ilk token gecikmesi (TTFT) ile saniyede 95 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (17B (109B total MoE))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Llama 4 Maverick · Strengths & Key Advantages

1M bağlam penceresi ve yanıt başına maksimum 33K çıktı tokenı kapasitesi (SWE-bench Verified 62% · GPQA Diamond 72.5%)
Sadece 550 ms ilk token gecikmesi (TTFT) ile saniyede 85 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (17B (400B total MoE))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

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

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Llama 4 Scout vs Llama 4 Maverick: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Llama 4 Scout (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Llama 4 Maverick (yerel çok modlu metin ve görsel anlama ile karmaşık belge analizi, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Llama 4 Maverick karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Llama 4 Scout daha yüksek üretim hızı sağlar; Llama 4 Scout ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Llama 4 Scout mi yoksa Llama 4 Maverick mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Llama 4 Maverick önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Llama 4 Scout ve Llama 4 Maverick API fiyatları nasıl karşılaştırılır?+

Llama 4 Scout 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, Llama 4 Maverick $0.00 / $0.00 seviyesindedir. Karma maliyette Llama 4 Scout daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Llama 4 Scout 3.5M token bağlam penceresi sunarken Llama 4 Maverick 1M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Llama 4 Scout öndedir.

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

İstediğiniz Modelleri Seçin ve Karşılaştırın

Llama 4 Scout

Llama 4 Maverick

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Llama 4 Scout · Strengths & Key Advantages

Considerations & Trade-offs

Llama 4 Maverick · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Llama 4 Scout vs Llama 4 Maverick: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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