Llama 4 Scout vs Llama 4 Maverick
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama 4 Scout by Meta and Llama 4 Maverick by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Llama 4 Scout provides an expanded memory horizon with 3,500K tokens. In latency-sensitive production environments, Llama 4 Scout leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Llama 4 Scout
Rey del contexto de 10Mmundo-record de contexto largo abierto modelo supporting ~3.5 millones tokens para completo codebase y video library ingesta.
$0.00 / 1M
$0.00 / 1M
3.5M tokens
N/A
Llama 4 Maverick
Multimodal abiertoinsignia abierto multimodal MoE modelo ofreciendo de frontera inteligencia un alto servicio eficiencia.
$0.00 / 1M
$0.00 / 1M
1M tokens
62%
Diferencias Clave de un Vistazo
- 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.
| Característica | 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% |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Scout · Strengths & Key Advantages
- Colossal 3.5 millones token ventana de contexto
- Sparse MoE arquitectura keeps activo compute un solo 17B
- de pesos abiertos available para local implementación
Considerations & Trade-offs
- requiere alto unified memoria un store el completo 109B weights
- Secondary razonamiento profundidad en comparación con denso razonamiento modelos
Llama 4 Maverick · Strengths & Key Advantages
- Rivals leading comercial modelos un través de codificación y STEM
- 1M ventana de contexto
- de pesos abiertos con accessible 17B activo parámetro enrutamiento
Considerations & Trade-offs
- Total weight huella de 400B requiere multi-GPU clúster
- Does no feature extendido nativo cadena de pensamiento
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas 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.
Veredicto
Verdict & Model Selection Recommendation: Choose Llama 4 Scout if your priority is Whole-repository code analysis, multi-hour video understanding, and legal book ingestion. Its performance in demanding tasks justifies its operational token cost. Choose Llama 4 Maverick if your workflow prioritizes Enterprise private cloud deployments, sophisticated coding assistants, and multimodal analysis. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does Llama 4 Scout compare to Llama 4 Maverick in API token pricing and cost per token?+
Both Llama 4 Scout and Llama 4 Maverick are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Llama 4 Scout or Llama 4 Maverick?+
Llama 4 Scout supports a context window of 3,500K tokens (~2,625k words), while Llama 4 Maverick supports 1,000K tokens (~750k words). Therefore, Llama 4 Scout can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 4 Scout or Llama 4 Maverick?+
Llama 4 Scout scores at the frontier of coding benchmarks, while Llama 4 Maverick scores 62%. For complex autonomous debugging and agentic engineering, Llama 4 Maverick provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Llama 4 Scout and Llama 4 Maverick?+
Llama 4 Scout is optimized for low-latency direct generation. In contrast, Llama 4 Maverick focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 4 Scout and Llama 4 Maverick?+
Llama 4 Scout generates approximately 95 tokens per second, while Llama 4 Maverick delivers 85 tokens per second. In latency-sensitive production environments, Llama 4 Scout provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 4 Scout and Llama 4 Maverick to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Llama 4 Scout, and route deep architectural or complex reasoning queries to Llama 4 Scout. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs