Llama 4 Scout: Managing 10-Million Token Context Windows

Complete Analysis: 2,000,000 tokens for Llama 4 Scout (10M context)

Complete analysis of pricing, performance, and use cases for Meta AI's Llama 4 Scout (10M context) model .

$6.050000 Total Cost
2,000,000 Total Tokens
59 minutes, 26.00 seconds Processing Time
561 Effective Tokens/Sec

Select AI Model

Llama 4 Scout
Meta AIMax Context: 10,000,000 tokens
Input: $1.000000 / 1M
Output: $5.000000 / 1M

Calculate Token Costs

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Provider-specific multipliers applied after all calculations
Enable for Haiku 4.5 hard cap bypass
Select platform to enforce context limits
Number of requests (max 1M). Summary view auto-enabled >10k.
Multiply total cost by quantity for project budgeting
$7.260000Total Cost
$1.000000Input Cost
$5.000000Output Cost
$0.000000Unit Cost
$0.000000Search Cost
$0.000000Request Fee
$0.000000Tool Fee
$0.000000Code Execution
2,000,000Total Tokens
$0.003630Cost per 1K
275,482Tokens per $

Click Recalculate to update after making changes

Calculate Processing Speed

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59m 26sProcessing Time
600Tokens/Second
120msTime to First Token
561Effective Speed

Model Comparison

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Model Information

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🔄 Advanced Options

⚡ Optimization
0%
Flat fee per session (e.g., $0.03 for Code Interpreter)
Hourly storage fee for cached data (Pro: $4.50/1M/hr, Flash: $1.00/1M/hr)
First 50 hours free, $0.05/hour after (reset at 00:00 UTC)
🧠 Specialized Modes
Enable Thinking Mode (Google models)
Manual thinking tokens (billed at output rate, disabled by default)
Adaptive thinking token estimation for DeepSeek models
30% output surcharge (Vertex AI priority)
Billed at output rate × reasoning multiplier
Global 2x multiplier for priority processing
Enable Reasoning/Thinking Mode (DeepSeek R1, Grok Deep Reason)
Enable Agentic Swarm
🔧 Automated Service Fees
Enable for DeepSeek V4 ($0.01 per 1M tokens)
Enable code execution (adds $0.05 flat fee)
$0.01 per query (auto-applied based on Search Queries input)
🤖 xAI Agent Tools (Unified $5.00/1k)
Real-time X data access calls
Standard internet search calls
Python sandbox execution calls (overrides flat fee if set)
📚 xAI RAG Tools (Unified $2.50/1k)
File search tool access
Collections/RAG knowledge base access - aggregated with File Search at $2.50/1k
ℹ️ Updated xAI Tool Pricing: Agent tools (web, X, code) at $5.00/1k calls. RAG tools (collections, file) at $2.50/1k calls. Integer code_execution_calls overrides boolean.
🎤 Realtime Audio & Deep Research
Enable Deep Research ($2.00/$8.00 rates)
Session length for billing ($0.01 per minute, rounded up)
Active speech time within session
📄 Mistral AI - Unit-Based Options
Number of pages to process with OCR (tiered pricing auto-applied)
Enable HTML table reconstruction surcharge
Duration-based audio processing (not token-based)
Enable speaker diarization (Voxtral models only)
Enable context biasing (Voxtral models only)
🔬 Research & Citation
Enable research tier pricing ($2.00/$8.00 + $0.005/query)
Enable reasoning with 1,000 token floor ($0.015 min)
Fee per source cited when research mode is enabled
⚙️ Performance Tuning
Low = Fast
High = Creative
📊 Advanced Cost Breakdown

Llama 4 Scout (10M context) Meta AI 10000000

$6.050000
Total Cost
👁️
Vision/Images
✗ Not Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✗ Not Available
📄
OCR Support
✗ Not Available
📊
Batch API
✗ Not Available
⚡
Caching
✗ Not Available

💰 Total Cost Calculation

Base Cost (No Optimizations) $6.050000 Input: $1.000000
Output: $5.000000
Optimized Cost $6.050000 Input: $1.000000
Output: $5.000000
Unit: $0.000000
Fees: $0.050000

Advanced Cost Breakdown

💻 Code Execution
$0.050000
Flat fee per execution

Detailed Cost Analysis

For 1,000,000 input tokens and 1,000,000 output tokens:

  • Input Cost: $1.000000
  • Output Cost: $5.000000
  • Unit Cost: $0.000000
  • Service Fees: $0.050000
  • Total Cost: $6.050000
  • Cost per 1K tokens: $0.003630 (rounded ~ 0.00)
  • Tokens per dollar: 275,482 tokens
  • Context Window: 10000000 tokens

Speed & Performance Analysis

With a processing speed of 600 tokens per second and 120ms time to first token:

  • Processing Time: 59 minutes, 26.00 seconds
  • Latency: 120 milliseconds to first token
  • Base Throughput: 600 tokens/second
  • Effective Throughput: 561 tokens/second

Best Use Cases

Legal DiscoveryArchive SearchMassive Ingestion

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Industrial-Scale Information Ingestion

In 2026, Llama 4 Scout defines the ‘Ultra-Long Context’ market with its massive 10-million token window. Priced at just $0.38 for 1M/1M tokens, it is the most efficient way to process entire technical libraries or the full history of a Slack workspace. It is the industrial-scale scanner of the AI world, identifying needle-in-a-haystack information across gigabytes of text in seconds.

The Economics of Scale

For legal firms conducting massive discovery, Scout allows for the ingestion of tens of thousands of documents in a single query. While its reasoning is ‘Scout’ level (meant for identification and summarization), it is the essential first step in any large-scale data analysis pipeline. Once Scout identifies the critical information, a more powerful model like Maverick or Opus can be used for final analysis.

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Frequently Asked Questions

How accurate are these AI model cost calculations?
Our calculations are based on official pricing from each provider (Google, OpenAI, Anthropic, Meta, xAI, Perplexity, DeepSeek, Mistral) and are updated regularly. We account for all factors including multimodal inputs, caching discounts, batch API pricing, tool usage multipliers, OCR processing, audio minutes, silence fees, and research mode pricing.
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The YemHub AI Calculator is the most comprehensive tool for estimating costs and comparing performance metrics across 38 AI models. It calculates token-based pricing, analyzes multimodal processing, and provides optimization recommendations.