Semantic Search Infrastructure: 1B Token Embedding Cost

text-embedding-3-large vs text-embedding-3-small
Complete Comparison: 1,000,000,000 input tokens × 1,000 output tokens
Comparison Mode (Custom field comparison)

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

Comparison Criteria text-embedding-3-large
OpenAI
text-embedding-3-small
OpenAI
Input Parameters Applied
Input Tokens 1,000,000,000 1,000,000,000
Output Tokens 1,000 1,000
Calculation Results
Input Cost $2000.000000 $2000.000000
Output Cost $0.012000 (rounded ~ 0.01) $0.012000 (rounded ~ 0.01)
Unit Cost (Audio/OCR) $0.000000 $0.000000
Service Fees $0.000000 $0.000000
Total Cost $2000.012000 (rounded ~ 2,000.01) $2000.012000 (rounded ~ 2,000.01)
Processing Time 555 hours, 33 minutes, 22.00 seconds 555 hours, 33 minutes, 22.00 seconds
Tokens per Second 500 500
Time to First Token 200ms 200ms
Cost per 1K tokens $0.002000 (rounded ~ 0.00) $0.002000 (rounded ~ 0.00)
Tokens per Dollar 499,998 499,998
Cost per 1 Million Tokens
Input Cost / 1M (Base) $2.000000 $2.000000
Output Cost / 1M (Base) $12.000000 $12.000000
Input Cost / 1M (Optimized) $2.000000
Optimizations: No optimizations applied
$2.000000
Optimizations: No optimizations applied
Output Cost / 1M (Optimized) $12.000000
Optimizations: No optimizations applied
$12.000000
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

1

First Model

2

Second Model

Select providers and models above, then click "Compare Models" to update the comparison.
All other parameters will be preserved from the current comparison.

ℹ️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 32,768 tokens.

Select AI Model

Text Embedding 3 Large
OtherMax Context: 32,768 tokens
Input: $2.000000 / 1M
Output: $12.000000 / 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
$2000.012000Total Cost
$2000.000000Input Cost
$0.012000Output Cost
$0.000000Unit Cost
$0.000000Search Cost
$0.000000Request Fee
$0.000000Tool Fee
$0.000000Code Execution
1,000,001,000Total Tokens
$0.002000Cost per 1K
499,998Tokens per $

Click Recalculate to update after making changes

Calculate Processing Speed

▼
33333m 22sProcessing Time
500Tokens/Second
200msTime to First Token
500Effective 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.

text-embedding-3-large OpenAI

$2000.012000 (rounded ~ 2,000.01)
Total Cost
⚠️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 32,768 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) $2000.012000 (rounded ~ 2,000.01) Input: $2000.000000
Output: $0.012000 (rounded ~ 0.01)
Optimized Cost $2000.012000 (rounded ~ 2,000.01) Input: $2000.000000
Output: $0.012000 (rounded ~ 0.01)
Unit: $0.000000
Fees: $0.000000

Detailed Cost Analysis

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

  • Input Cost: $2000.000000
  • Output Cost: $0.012000 (rounded ~ 0.01)
  • Unit Cost: $0.000000
  • Service Fees: $0.000000
  • Total Cost: $2000.012000 (rounded ~ 2,000.01)
  • Cost per 1K tokens: $0.002000 (rounded ~ 0.00)
  • Tokens per dollar: 499,998 tokens
  • Context Window: 32768 tokens

Speed & Performance Analysis

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

  • Processing Time: 555 hours, 33 minutes, 22.00 seconds
  • Latency: 200 milliseconds to first token
  • Base Throughput: 500 tokens/second

Best Use Cases

RAGVector SearchEnterprise KnowledgeData Science

text-embedding-3-small OpenAI

$2000.012000 (rounded ~ 2,000.01)
Total Cost
⚠️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 32,768 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) $2000.012000 (rounded ~ 2,000.01) Input: $2000.000000
Output: $0.012000 (rounded ~ 0.01)
Optimized Cost $2000.012000 (rounded ~ 2,000.01) Input: $2000.000000
Output: $0.012000 (rounded ~ 0.01)
Unit: $0.000000
Fees: $0.000000

Detailed Cost Analysis

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

  • Input Cost: $2000.000000
  • Output Cost: $0.012000 (rounded ~ 0.01)
  • Unit Cost: $0.000000
  • Service Fees: $0.000000
  • Total Cost: $2000.012000 (rounded ~ 2,000.01)
  • Cost per 1K tokens: $0.002000 (rounded ~ 0.00)
  • Tokens per dollar: 499,998 tokens
  • Context Window: 32768 tokens

Speed & Performance Analysis

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

  • Processing Time: 555 hours, 33 minutes, 22.00 seconds
  • Latency: 200 milliseconds to first token
  • Base Throughput: 500 tokens/second

Best Use Cases

RAGVector SearchEnterprise KnowledgeData Science

Vector Database & RAG Infrastructure Economics

Calculating the total cost to vectorize and embed a 1-billion token enterprise knowledge base into a semantic search infrastructure using the latest high-dimension embedding models.

Embedding Setup

  • Corpus Size: 1,000,000,000 tokens (Enterprise-wide docs/data)
  • Model Tier: Large-scale high-dimension embeddings (3072 dims)
  • Refresh Cycle: Monthly full-repo re-indexing
  • Output: Vector embeddings for Pinecone/Weaviate integration
  • Total API Cost: Unit cost per 1M tokens vs proprietary hosting

Enterprise Knowledge Management ROI

Enabling internal RAG systems, sub-second semantic retrieval across all departments, and automated knowledge discovery. Benchmarks Text Embedding 3 Large against specialized open-source embeddings.

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.