Customer Support Automation: 1M Tickets Monthly

Complete Analysis: 800,000,000 tokens for gemini-2-5-flash
⚡ 70% Cached

Complete analysis of pricing, performance, and use cases for Google's gemini-2-5-flash model with 70% Cached.

⚡ Caching Optimized (up to 90% savings) 📊 Batch API
$2000.000000 Total Cost
800,000,000 Total Tokens
381 hours, 28 minutes, 53.00 seconds Processing Time
583 Effective Tokens/Sec
ℹ️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 1,000,000 tokens.

Select AI Model

Gemini 2.5 Flash
GoogleMax Context: 1,000,000 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
$842.500000Total Cost
$500.000000Input Cost
$1500.000000Output Cost
$0.000000Unit Cost
$0.000000Search Cost
$0.000000Request Fee
$0.000000Tool Fee
$0.000000Code Execution
800,000,000Total Tokens
$0.001053Cost per 1K
949,555Tokens per $

Click Recalculate to update after making changes

Calculate Processing Speed

▼
22888m 53sProcessing Time
600Tokens/Second
120msTime to First Token
583Effective Speed

Model Comparison

Select a model to see comparisons with competitors.

Model Information

Select a model to see detailed information.

🔄 Advanced Options

⚡ Optimization
70%
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

gemini-2-5-flash Google 1000000

$2000.000000
Total Cost
⚠️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 1,000,000 tokens.
⚡ 70% Cached 📊 Batch API
👁️
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) $2000.000000 Input: $500.000000
Output: $1500.000000
Optimized Cost $2000.000000 Input: $500.000000
Output: $1500.000000
Unit: $0.000000
Fees: $0.000000
Total Savings $1157.500000 57.9% discount

Advanced Cost Breakdown

📊 Batch API
50.0% off
Asynchronous processing

Detailed Cost Analysis

For 500,000,000 input tokens and 300,000,000 output tokens:

  • Input Cost: $500.000000
  • Output Cost: $1500.000000
  • Total Cost: $2000.000000
  • Cost per 1K tokens: $0.001053 (rounded ~ 0.00)
  • Tokens per dollar: 949,555 tokens
  • Context Window: 1000000 tokens

Speed & Performance Analysis

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

  • Processing Time: 381 hours, 28 minutes, 53.00 seconds
  • Latency: 120 milliseconds to first token
  • Base Throughput: 600 tokens/second
  • Effective Throughput: 583 tokens/second (temperature-adjusted)

Best Use Cases

Enterprise supportSaaS companiesTelecom

Enterprise Customer Service Automation

Calculates costs for automating 1,000,000 support tickets monthly. Includes triage, response generation, and escalation handling.

Support Scale

  • Tickets: 1,000,000 monthly
  • Tokens per ticket: 500 in, 300 out
  • Caching: 70% for common issues
  • Model: Gemini 2.5 Flash (cost-effective)
  • Batch: For ticket backlog processing

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.
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.
What is the YemHub AI Calculator Tool?
The YemHub AI Calculator is the most comprehensive tool for estimating costs and comparing performance metrics across 44+ AI models. It calculates token-based pricing, analyzes multimodal processing, accounts for state-dependent pricing (context cliffs, tiered tunnels), and provides optimization recommendations.