Llama 4 Scout vs GPT-5.5 for 1 Million Token Content Generation

Llama 4 Scout vs GPT-5.5
Complete Comparison: 1,000,000 input tokens × 500 output tokens
Comparison Mode
⚡ 15% Cached

Complete comparison of pricing, performance, and capabilities for 2 leading AI models with 15% Cached.

⚡ Caching Optimized (up to 90% savings)
Comparison Criteria Llama 4 Scout
Meta AI
GPT-5.5
OpenAI
Calculation Results (Current Inputs) (15% cached)
Input Tokens 1,000,000 1,000,000
Output Tokens 500 500
Cost Breakdown
Input Cost $0.080000Best $10.000000Worst
Output Cost $0.000150Best $0.022500 (rounded ~ $0.02)Worst
Unit Cost (Audio/OCR) $0.000000 $0.000000
Service Fees $0.000000 $0.000000
Total Cost $0.080150 Best Value $8.672500 (rounded ~ $8.67) Most Expensive
Processing Time 29 minutes, 44.41 seconds Fastest 42 minutes, 29.07 seconds Slowest
Tokens per Second 600Fastest 420Slowest
Time to First Token 120ms Best 210ms Worst
Cost per 1K tokens $0.000080Best $0.008668 (rounded ~ $0.01)Worst
Tokens per Dollar 12,482,845Best Value 115,365Worst Value
Cost per 1 Million Tokens (Informational)
Input Cost / 1M (Base) $0.080000Best $10.000000Worst
Output Cost / 1M (Base) $0.300000Best $45.000000Worst
Input Cost / 1M (Optimized) $0.080000Best
Optimizations: No optimizations applied
$10.000000Worst
Optimizations: No optimizations applied
Output Cost / 1M (Optimized) $0.300000Best
Optimizations: No optimizations applied
$45.000000Worst
Optimizations: No optimizations applied
Capabilities & Advanced Features
Images Support ✗ Not Supported ✓ Supported
Caching Support
15
✗ Not Supported requested ✓ Supported
Batch API Support ✓ Supported ✓ Supported
Tool Usage Support ✗ Not Supported ✓ Supported
Scroll horizontally to see all data

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Select AI Model

Llama 4 Scout
Meta AIMax Context: 10,000,000 tokens
$0.08 / $0.3 per 1M tokens
▼
▼
Use Batch API (50% discount)
15%
Provider-specific multipliers applied after all calculations
Enable for cache discounts
Select platform to enforce context limits
Number of requests (max 1M). Summary view auto-enabled >10k.

Calculate Token Costs

$0.080150 Total Cost
$0.080000 Input Cost
$0.000150 Output Cost
$0.000000 Unit Cost
$0.000000 Search Cost
$0.000000 Request Fee
$0.000000 Tool Fee
$0.000000 Code Execution
1,000,500Total Tokens
$0.000080Cost per 1K
12,482,845Tokens per $
📊 Advanced Cost Breakdown

Processing Speed

29m 44s Processing Time
600 Tokens/Second
120ms Time to First Token
561 Effective Speed

Model Comparison

Select a model to see comparisons with competitors.

Model Information

Select a model to see detailed information.

🔄 Advanced Options

⚡ Optimization
Flat fee per session (e.g., $0.03 for Code Interpreter)
Hourly storage fee for cached data
First 50 hours free, $0.05/hour after
🧠 Reasoning & Thinking
Manual thinking tokens (billed at output rate)
🔧 Special Modes
Enable 6.0x Fast Mode multiplier
📚 Research & Citations
Enable $1.00/$4.00 rates + $10.00/1k search
Enable research tier pricing
Fee per source cited
🎤 Realtime Audio & Video
Session length for billing
📊 Multiple Models Detected: This page contains data for 2 models. See the detailed comparison table above, and switch between models using tabs below.

Llama 4 Scout Meta AI 10000000

$0.080150
Total Cost
⚡ 15% Cached
👁️
Vision/Images
✗ Not Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✗ Not Available
📄
OCR Support
✗ Not Available
📊
Batch API
✓ Available
⚡
Caching
✗ Not Available

💰 Total Cost Calculation (from Plugin)

Base Cost (No Optimizations) $0.080150 Input: $0.080000
Output: $0.000150
Optimized Cost $0.080150 Input: $0.080000
Output: $0.000150
Unit: $0.000000
Fees: $0.000000

Detailed Cost Analysis (from Plugin)

For 1,000,000 input tokens and 500 output tokens:

  • Input Cost: $0.080000
  • Output Cost: $0.000150
  • Total Cost: $0.080150
  • Cost per 1K tokens: $0.000080
  • Tokens per dollar: 12,482,845 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: 29 minutes, 44.41 seconds
  • Latency: 120 milliseconds to first token
  • Base Throughput: 600 tokens/second
  • Effective Throughput: 561 tokens/second (temperature-adjusted)

Best Use Cases

Applications requiring massive context windows for RAGdocument analysisor extensive data processingalongside models focused on agentic taskscomplex reasoningand code generation.

Want this applied to YOUR actual stack?

This calculator shows the math for Llama 4 Scout. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.

Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.

Get my instant AI audit — $39 →

GPT-5.5 OpenAI 1000000 🏔️ Context Cliff

$8.672500 (rounded ~ $8.67)
Total Cost
⚠️ Bulk Calculation: Total volume exceeds single-request limit of 1,000,000 tokens. Budgeting mode active.
⚡ 15% Cached
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✓ Available
📄
OCR Support
✗ Not Available
📊
Batch API
✓ Available
⚡
Caching
✓ Available
90% savings

💰 Total Cost Calculation (from Plugin)

Base Cost (No Optimizations) $10.022500 (rounded ~ $10.02) Input: $10.000000
Output: $0.022500 (rounded ~ $0.02)
Optimized Cost $8.672500 (rounded ~ $8.67) Input: $10.000000
Output: $0.022500 (rounded ~ $0.02)
Unit: $0.000000
Fees: $0.000000
Total Savings $1.350000 13.5% discount

Advanced Cost Breakdown (from Plugin)

🏔️ Context Cliff
Premium Tier
>272,000 tokens triggered premium pricing
📊 Cliff Pricing
Premium
premium pricing (threshold: 272,000)

Detailed Cost Analysis (from Plugin)

For 1,000,000 input tokens and 500 output tokens:

  • Input Cost: $10.000000
  • Output Cost: $0.022500 (rounded ~ $0.02)
  • Total Cost: $8.672500 (rounded ~ $8.67)
  • Cost per 1K tokens: $0.008668 (rounded ~ $0.01)
  • Tokens per dollar: 115,365 tokens
  • Context Window: 1000000 tokens

Speed & Performance Analysis

With a processing speed of 420 tokens per second and 210ms time to first token:

  • Processing Time: 42 minutes, 29.07 seconds
  • Latency: 210 milliseconds to first token
  • Base Throughput: 420 tokens/second
  • Effective Throughput: 393 tokens/second (temperature-adjusted)

Best Use Cases

Applications requiring massive context windows for RAGdocument analysisor extensive data processingalongside models focused on agentic taskscomplex reasoningand code generation.

Want this applied to YOUR actual stack?

This calculator shows the math for GPT-5.5. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.

Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.

Get my instant AI audit — $39 →

✨ Market Recommendations AI Model Registry

← Back to Llama 4 Scout
📋 Active Input Parameters
Input Tokens: 1,000,000
Output Tokens: 500
Cached Tokens: 15%
Rank AI Model & Provider Total Cost vs Llama 4 Scout vs GPT-5.5
🏆 Grok 4.20 Beta
xAI
 $1.733000 (rounded ~ $1.73) Best Value ↑ 2062.2% more ↓ 80% cheaper
🥈 Gemini 2.5 Pro
Google
 $2.170000 ↑ 2607.4% more ↓ 75% cheaper
🥉 Gemini 3.1 Pro
Google
 $3.469000 (rounded ~ $3.47) ↑ 4228.1% more ↓ 60% cheaper
#4 GPT-5.4
OpenAI
 $4.336250 (rounded ~ $4.34) ↑ 5310.2% more ↓ 50% cheaper
#5 GPT-5.4 Thinking
OpenAI
 $4.336250 (rounded ~ $4.34) ↑ 5310.2% more ↓ 50% cheaper
#6 GPT-5.4 Thinking
OpenAI
 $4.336250 (rounded ~ $4.34) ↑ 5310.2% more ↓ 50% cheaper
🏆

Grok 4.20 Beta
xAI

$1.733000 (rounded ~ $1.73)
vs Llama 4 Scout: ↑ 2062.2%
vs GPT-5.5: ↓ 80%
🥈

Gemini 2.5 Pro
Google

$2.170000
vs Llama 4 Scout: ↑ 2607.4%
vs GPT-5.5: ↓ 75%
🥉

Gemini 3.1 Pro
Google

$3.469000 (rounded ~ $3.47)
vs Llama 4 Scout: ↑ 4228.1%
vs GPT-5.5: ↓ 60%
#4

GPT-5.4
OpenAI

$4.336250 (rounded ~ $4.34)
vs Llama 4 Scout: ↑ 5310.2%
vs GPT-5.5: ↓ 50%
#5

GPT-5.4 Thinking
OpenAI

$4.336250 (rounded ~ $4.34)
vs Llama 4 Scout: ↑ 5310.2%
vs GPT-5.5: ↓ 50%
#6

GPT-5.4 Thinking
OpenAI

$4.336250 (rounded ~ $4.34)
vs Llama 4 Scout: ↑ 5310.2%
vs GPT-5.5: ↓ 50%
✨ How recommendations work (v8.6.0): We scan all active models in the registry and only include those that support ALL your current inputs. For token-based models, we check if they can handle your token counts. For special pricing models (OCR, video, audio), we verify they have the correct pricing structure. Features marked requested were in your inputs but not supported by that model. Now using official provider pricing without reseller markups.

For academic researchers exploring the frontiers of AI in content generation, comparing Llama 4 Scout and GPT-5.5 provides a look at cutting-edge models with extensive context capabilities. Both models are designed to handle large volumes of information, making them suitable for generating personalized newsletter content at scale. Llama 4 Scout, known for its vast context window, can process significant amounts of subscriber data or contextual information, while GPT-5.5 offers strong reasoning and agentic task performance, potentially improving the quality and uniqueness of generated intros.

Strategic Choices in Model Selection

When considering Llama 4 Scout against GPT-5.5 for newsletter personalization research, academic evaluators should note:

  • Context Window vs. Reasoning: Llama 4 Scout excels in handling extremely large context windows (10M tokens), which can be beneficial if subscriber data is vast. GPT-5.5, while also supporting large contexts (1M+ tokens), is particularly lauded for its reasoning and agentic capabilities, which might lead to more sophisticated personalization.
  • Performance Metrics: Benchmarks show GPT-5.5 leading in areas like agentic tasks and coding, while Llama 4 Scout is noted for its retrieval and summarization strengths, especially with its massive context.
  • Open vs. Proprietary: Llama 4 Scout is an open-weight model, offering greater flexibility for research and fine-tuning, whereas GPT-5.5 is proprietary.
  • Cost-Benefit Analysis: The per-token pricing and overall efficiency of each model will influence budget allocation for large-scale generation tasks.

This comparison highlights the trade-offs between extensive context handling and advanced reasoning, offering researchers distinct pathways to optimize AI-driven content personalization.

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. Note: Reseller markups and dedicated instance multipliers have been removed to reflect official provider pricing.
How does prompt caching work?
Caching discounts vary by provider: Google and OpenAI offer 90% discounts on cached input tokens. Anthropic uses write (1.25x) and read (0.10x) multipliers. Savings are applied to the token portion only, not unit-based fees.
How do Market Recommendations work (v8.6.0)?
Our recommendation engine scans the entire model registry and only includes models that support ALL your current input parameters (tokens, images, video, audio, OCR, tools, batch API, etc.). It calculates exact costs with your settings and sorts by price, showing you the best value options that can handle your complete workflow. Special pricing models (OCR, video, audio, image generation) are properly handled and only appear when their specific input types are requested. v8.6.0 removes reseller markups (20% buffer) and dedicated instance multipliers to reflect official provider pricing.
What is the YemHub AI Calculator Tool?
The YemHub AI Calculator is the most comprehensive tool for estimating costs and comparing performance metrics across 50+ AI models. It calculates token-based pricing, analyzes multimodal processing, accounts for state-dependent pricing (context cliffs, tiered tunnels), provides optimization recommendations, and now offers intelligent market matching to find the best alternatives for your specific needs.