Massive Repo Documentation: 1M Token Context Cost

gemini-3-1-pro vs gpt-5
Complete Comparison: 1,000,000 input tokens × 50,000 output tokens
Comparison Mode (Custom field comparison)

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

📊 Batch API
Comparison Criteria gemini-3-1-pro
Google
gpt-5
OpenAI
Input Parameters Applied
Input Tokens 1,000,000 1,000,000
Output Tokens 50,000 50,000
Calculation Results
Input Cost $1.000000 Best $1.750000 Worst
Output Cost $0.250000 Best $0.700000 Worst
Unit Cost (Audio/OCR) $0.000000 $0.000000
Service Fees $0.050000 $0.050000
Total Cost $1.300000 Best Value $2.500000 Most Expensive
Processing Time 45 minutes, 3.00 seconds Slowest 40 minutes, 3.00 seconds Fastest
Tokens per Second 400 Slowest 450 Fastest
Time to First Token 220ms Worst 200ms Best
Cost per 1K tokens $0.000643 Best $0.001214 (rounded ~ 0.00) Worst
Tokens per Dollar 1,555,556 Best Value 823,529 Worst Value
Cost per 1 Million Tokens
Input Cost / 1M (Base) $1.000000 Best $1.750000 Worst
Output Cost / 1M (Base) $5.000000 Best $14.000000 Worst
Input Cost / 1M (Optimized) $0.500000 Best
Optimizations: 50.0% batch
$0.875000 (rounded ~ 0.88) Worst
Optimizations: 50.0% batch
Output Cost / 1M (Optimized) $2.500000 Best
Optimizations: 50.0% batch
$7.000000 Worst
Optimizations: 50.0% batch
Capabilities
Caching Support ✓ Supported ✓ Supported
Batch API Support ✓ Enabled ✓ Enabled
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

1

First Model

2

Second Model

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All other parameters will be preserved from the current comparison.

Select AI Model

Gemini 3.1 Pro
GoogleMax Context: 2,000,000 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
$0.675000Total Cost
$1.000000Input Cost
$0.250000Output Cost
$0.000000Unit Cost
$0.000000Search Cost
$0.000000Request Fee
$0.000000Tool Fee
$0.000000Code Execution
1,050,000Total Tokens
$0.000643Cost per 1K
1,555,556Tokens per $

Click Recalculate to update after making changes

Calculate Processing Speed

▼
45m 3sProcessing Time
400Tokens/Second
220msTime to First Token
388Effective Speed

Model Comparison

Select a model to see comparisons with competitors.

Model Information

Select a model to see detailed information.

🔄 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
📊 Multiple Models Detected: This page contains data for 2 models. See the detailed comparison table above, and switch between models using tabs below.

gemini-3-1-pro Google 2000000

$1.300000
Total Cost
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✓ Available
🎥
Video Analysis
✓ Available
🔧
Tool Usage
✓ Available
📄
OCR Support
✗ Not Available
📊
Batch API
✓ Available
⚡
Caching
✓ Available
90% savings

💰 Total Cost Calculation

Base Cost (No Optimizations) $1.300000 Input: $1.000000
Output: $0.250000
Optimized Cost $1.300000 Input: $1.000000
Output: $0.250000
Unit: $0.000000
Fees: $0.050000
Total Savings $0.625000 (rounded ~ 0.63) 48.1% discount

Advanced Cost Breakdown

📊 Batch API
50.0% off
Asynchronous processing
💻 Code Execution
$0.050000
Flat fee per execution

Detailed Cost Analysis

For 1,000,000 input tokens and 50,000 output tokens:

  • Input Cost: $1.000000
  • Output Cost: $0.250000
  • Unit Cost: $0.000000
  • Service Fees: $0.050000
  • Total Cost: $1.300000
  • Cost per 1K tokens: $0.000643
  • Tokens per dollar: 1,555,556 tokens
  • Context Window: 2000000 tokens

Speed & Performance Analysis

With a processing speed of 400 tokens per second and 220ms time to first token:

  • Processing Time: 45 minutes, 3.00 seconds
  • Latency: 220 milliseconds to first token
  • Base Throughput: 400 tokens/second
  • Effective Throughput: 388 tokens/second

Best Use Cases

Software EngineeringDocumentationOnboardingArchitecture

gpt-5 OpenAI

$2.500000
Total Cost
⚠️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 400,000 tokens.
👁️
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

Base Cost (No Optimizations) $2.500000 Input: $1.750000
Output: $0.700000
Optimized Cost $2.500000 Input: $1.750000
Output: $0.700000
Unit: $0.000000
Fees: $0.050000
Total Savings $1.225000 (rounded ~ 1.23) 49.0% discount

Advanced Cost Breakdown

📊 Batch API
50.0% off
Asynchronous processing
💻 Code Execution
$0.050000
Flat fee per execution

Detailed Cost Analysis

For 1,000,000 input tokens and 50,000 output tokens:

  • Input Cost: $1.750000
  • Output Cost: $0.700000
  • Unit Cost: $0.000000
  • Service Fees: $0.050000
  • Total Cost: $2.500000
  • Cost per 1K tokens: $0.001214 (rounded ~ 0.00)
  • Tokens per dollar: 823,529 tokens
  • Context Window: 400000 tokens

Speed & Performance Analysis

With a processing speed of 450 tokens per second and 200ms time to first token:

  • Processing Time: 40 minutes, 3.00 seconds
  • Latency: 200 milliseconds to first token
  • Base Throughput: 450 tokens/second
  • Effective Throughput: 437 tokens/second

Best Use Cases

Software EngineeringDocumentationOnboardingArchitecture

Large-Scale Codebase Knowledge Mapping

Analyzing the costs associated with generating comprehensive technical documentation for massive 1M+ token repositories using the ultra-long context windows of 2026 models. This tool helps engineering leads budget for automated onboarding and architectural documentation at scale.

Repository Context Setup

  • Repo Size: ~1,000,000 tokens (Full source code + history)
  • Context Window: 2,000,000 token target for holistic mapping
  • Output Type: Technical Wiki, API docs, and onboarding guides (~50K tokens)
  • Reasoning Depth: High-level architectural pattern recognition
  • Throughput: Optimized for large batch processing via Gemini 3 Pro
  • Cache Efficiency: 85% on repeated code structures and library imports

DevOps & Engineering ROI

Accelerating developer onboarding, reducing documentation debt, and enabling ‘Chat-with-your-Repo’ features for distributed teams. Compares the efficiency of Gemini’s context window against chunking strategies in GPT-5.

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.