Llama 4 vs GPT-5 Mini: Equal I/O Pricing Strategy 2026

llama-4-scout-17b vs gpt-5-mini
Complete Comparison: 200,000 input tokens × 800,000 output tokens
Comparison Mode (Custom field comparison)

Complete comparison of pricing, performance, and capabilities for 2 leading AI models and caching optimization .

Comparison Criteria llama-4-scout-17b
Meta AI
gpt-5-mini
OpenAI
Input Parameters Applied (20% cached)
Input Tokens 200,000 200,000
Output Tokens 800,000 800,000
Calculation Results
Input Cost $0.200000 Best $1.000000 Worst
Output Cost $4.000000 Best $20.000000 Worst
Unit Cost (Audio/OCR) $0.000000 $0.000000
Service Fees $0.000000 $0.000000
Total Cost $4.200000 Best Value $21.000000 Most Expensive
Processing Time 29 minutes, 43.00 seconds Fastest 35 minutes, 40.00 seconds Slowest
Tokens per Second 600 Fastest 500 Slowest
Time to First Token 120ms Best 200ms Worst
Cost per 1K tokens $0.004200 (rounded ~ 0.00) Best $0.021000 Worst
Tokens per Dollar 238,095 Best Value 47,619 Worst Value
Cost per 1 Million Tokens
Input Cost / 1M (Base) $1.000000 Best $5.000000 Worst
Output Cost / 1M (Base) $5.000000 Best $25.000000 Worst
Input Cost / 1M (Optimized) $1.000000 Best
Optimizations: No optimizations applied
$5.000000 Worst
Optimizations: No optimizations applied
Output Cost / 1M (Optimized) $5.000000 Best
Optimizations: No optimizations applied
$25.000000 Worst
Optimizations: No optimizations applied
Capabilities
Caching Support ✗ Not Supported ✗ Not Supported
Batch API Support ✗ Not Supported ✗ Not Supported
Fine-Tuning Mode Standard Standard
Research Mode Not Enabled Not Enabled
Thinking Enabled Not Enabled Not Enabled
Scroll horizontally to see all data

🔄 Compare Different AI Models

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First Model

2

Second Model

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All other parameters will be preserved from the current comparison.

Select AI Model

Llama 4 Scout 17b
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
$4.200000Total Cost
$0.200000Input Cost
$4.000000Output Cost
$0.000000Unit Cost
$0.000000Search Cost
$0.000000Request Fee
$0.000000Tool Fee
$0.000000Code Execution
1,000,000Total Tokens
$0.004200Cost per 1K
238,095Tokens per $

Click Recalculate to update after making changes

Calculate Processing Speed

▼
29m 43sProcessing Time
600Tokens/Second
120msTime to First Token
561Effective Speed

Model Comparison

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Model Information

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🔄 Advanced Options

⚡ Optimization
20%
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
📊 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-17b Meta AI 10000000

$4.200000
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) $4.200000 Input: $0.200000
Output: $4.000000
Optimized Cost $4.200000 Input: $0.200000
Output: $4.000000
Unit: $0.000000
Fees: $0.000000

Detailed Cost Analysis

For 200,000 input tokens and 800,000 output tokens:

  • Input Cost: $0.200000
  • Output Cost: $4.000000
  • Unit Cost: $0.000000
  • Service Fees: $0.000000
  • Total Cost: $4.200000
  • Cost per 1K tokens: $0.004200 (rounded ~ 0.00)
  • Tokens per dollar: 238,095 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, 43.00 seconds
  • Latency: 120 milliseconds to first token
  • Base Throughput: 600 tokens/second
  • Effective Throughput: 561 tokens/second

Best Use Cases

Content generationHigh output workflows

gpt-5-mini OpenAI

$21.000000
Total Cost
⚠️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 400,000 tokens.
👁️
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) $21.000000 Input: $1.000000
Output: $20.000000
Optimized Cost $21.000000 Input: $1.000000
Output: $20.000000
Unit: $0.000000
Fees: $0.000000

Detailed Cost Analysis

For 200,000 input tokens and 800,000 output tokens:

  • Input Cost: $1.000000
  • Output Cost: $20.000000
  • Unit Cost: $0.000000
  • Service Fees: $0.000000
  • Total Cost: $21.000000
  • Cost per 1K tokens: $0.021000
  • Tokens per dollar: 47,619 tokens
  • Context Window: 400000 tokens

Speed & Performance Analysis

With a processing speed of 500 tokens per second and 200ms time to first token:

  • Processing Time: 35 minutes, 40.00 seconds
  • Latency: 200 milliseconds to first token
  • Base Throughput: 500 tokens/second
  • Effective Throughput: 467 tokens/second

Best Use Cases

Content generationHigh output workflows

Symmetric Pricing Models

Comparing models with equal input/output pricing vs traditional asymmetric models.

Pricing Strategy

  • Llama 4 Scout: $0.08/1M both input & output
  • GPT-5 Mini: $0.25/1M input, $2/1M output
  • Use case dependent: High output favors Llama
  • Mixed workflows require careful calculation

Output-Intensive Workflows

Content generation, report writing, code generation, creative writing, translation.

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.
What is the YemHub AI Calculator Tool?
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.