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.
Changez et Comparez les Modèles
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Llama 4 Scout
Roi du contexte 10Mmonde-record à long contexte ouvert modèle supporting ~3.5 millions tokens pour complet codebase et vidéo library ingestion.
$0.00 / 1M
$0.00 / 1M
3.5M tokens
N/A
Llama 4 Maverick
Multimodal open sourcephare ouvert multimodal MoE modèle offrant de pointe intelligence à élevé hébergement efficacité.
$0.00 / 1M
$0.00 / 1M
1M tokens
62%
Différences Clés en un Coup d’Œil
- 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.
| Caractéristique | 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% |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Scout · Strengths & Key Advantages
- Colossal 3.5 millions token fenêtre de contexte
- Sparse MoE architecture keeps actif compute à seulement 17B
- à poids ouverts available pour local déploiement
Considerations & Trade-offs
- nécessite élevé unified mémoire à store le complet 109B weights
- Secondary raisonnement profondeur par rapport à dense raisonnement modèles
Llama 4 Maverick · Strengths & Key Advantages
- Rivals leading commercial modèles à travers codage et STEM
- 1M fenêtre de contexte
- à poids ouverts avec accessible 17B actif paramètre routage
Considerations & Trade-offs
- Total weight empreinte de 400B nécessite multi-GPU cluster
- Does pas feature étendu natif chaîne de pensée
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (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.
Verdict
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.
Questions fréquentes
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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs