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Head-to-Head Model Comparison

Llama 4 Scout vs Llama 4 Maverick

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Llama 4 Scout vs Llama 4 Maverick: Benchmarks, Pricing & Speed Comparison

Meta 的 Llama 4 Scout 与 Meta 的 Llama 4 Maverick 是当前备受关注的两款主力 AI 模型。Llama 4 Scout 提供 3.5M Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Llama 4 Maverick 提供 1M Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。

模型直接对比 (VS)

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Llama 4 Scout

开源权重基础大模型

Meta 推出的 Llama 4 Scout 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 3.5M 上下文窗口、95 Token/秒生成速度与 76/100 综合智能指数（免费开源权重）。

Intelligence Index76 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

3.5M tokens

SWE-bench Verified:

N/A

Meta

Llama 4 Maverick

原生多模态视觉模型

Meta 推出的 Llama 4 Maverick 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 1M 上下文窗口、85 Token/秒生成速度与 84/100 综合智能指数（免费开源权重）。

Intelligence Index84 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

62%

核心差异一览

  • 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 →
FeatureLlama 4 ScoutLlama 4 Maverick
Intelligence Index
Composite reasoning & knowledge score (0-100)
7684
Provider
MetaMeta
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 text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K33K
Throughput Speed
Tokens generated per second
95 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
N/A62%
GPQA Diamond
Graduate-level reasoning
N/A72.5%

各项指标谁更胜一筹？

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

权威基准测试得分对比

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

延迟与响应速度

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

实时 Token 成本模拟器

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

100K / day
10K500K1M
30K / day
5K150K300K
50% 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

  • 提供 3.5M 上下文窗口与最高 33K 输出容量，核心基准表现优异（MMLU-Pro 78% · Arena Elo 1370）
  • 高达 95 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 650 毫秒
  • 开放模型权重 (17B (109B total MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Llama 4 Maverick · Strengths & Key Advantages

  • 提供 1M 上下文窗口与最高 33K 输出容量，核心基准表现优异（SWE-bench Verified 62% · GPQA Diamond 72.5%）
  • 高达 85 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 550 毫秒
  • 开放模型权重 (17B (400B total MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Token Use & Task Economics

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

MetricLlama 4 ScoutLlama 4 Maverick
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
10K15K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureLlama 4 ScoutLlama 4 Maverick
Official API Model ID (SDK)meta-llama/Llama-4-Scoutmeta-llama/Llama-4-Maverick
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-042025-04
Knowledge CutoffJanuary 2025January 2025
ArchitectureExtreme-context sparse MoE multimodal transformer (17B active / 109B total)High-capacity sparse MoE multimodal transformer (17B active / 400B total)
Parameters17B (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 CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText 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 VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Llama 4 Scout vs Llama 4 Maverick: 常见问题解答 (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.

综合结论

综合来看，Llama 4 Scout（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Llama 4 Maverick（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Llama 4 Maverick 在综合智能与编程基准测试中表现更强，Llama 4 Scout 拥有更快的 Token 生成速度，而 Llama 4 Scout 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面，Llama 4 Scout 和 Llama 4 Maverick 哪个更强？+

在综合智能指数与编程基准评测中，Llama 4 Maverick 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Llama 4 Scout 与 Llama 4 Maverick 的 API 价格哪个更便宜？+

Llama 4 Scout 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Llama 4 Maverick 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Llama 4 Scout 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度？+

Llama 4 Scout 支持 3.5M Token 上下文窗口，而 Llama 4 Maverick 支持 1M Token。在流式生成速度方面，Llama 4 Scout 的吞吐表现更快。

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

数据来源: Meta 与 Meta 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。