Training data preparation with image embedding and text

Complete Analysis: 421,000 tokens for gpt-40-mini
🖼️ 1000 Images 🔧 Training Mode 🔍 50,000 Embedding Tokens

Complete analysis of pricing, performance, and use cases for OpenAI's gpt-40-mini model with 1000 Images, Training Mode, 50,000 Embedding Tokens. (Fine-tuning mode: training)

🖼️ Multimodal Input 🔧 Training Mode
$2.125000 (rounded ~ 2.13) Total Cost
421,000 Total Tokens
14 minutes, 27.00 seconds Processing Time
485 Effective Tokens/Sec
ℹ️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 400,000 tokens.

Select AI Model

Gpt 40 Mini
OpenAIMax Context: 400,000 tokens
Input: $5.000000 / 1M
Output: $25.000000 / 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
$2.125000Total Cost
$2.100000Input Cost
$0.025000Output Cost
$0.000000Unit Cost
$0.000000Search Cost
$0.000000Request Fee
$0.000000Tool Fee
$0.000000Code Execution
421,000Total Tokens
$0.005048Cost per 1K
198,118Tokens per $

Click Recalculate to update after making changes

Calculate Processing Speed

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14m 27sProcessing Time
500Tokens/Second
200msTime to First Token
485Effective Speed

Model Comparison

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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

gpt-40-mini OpenAI

$2.125000 (rounded ~ 2.13)
Total Cost
🔧 Fine-Tuning Mode Active: Model is in Training mode. Output cost is $0.00 for model training.
⚠️ Note: Calculation represents bulk volume across multiple requests; single-request limit is 400,000 tokens.
🖼️ 1000 Image (Medium) 📊 Batch API
👁️
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 Training Mode

Base Cost (No Optimizations) $2.125000 (rounded ~ 2.13) Input: $2.100000
Output: $0.025000 (rounded ~ 0.03)
Training Cost $2.125000 (rounded ~ 2.13) Input: $2.100000
Output: $0.025000 (rounded ~ 0.03)
Unit: $0.000000
Fees: $0.000000

Detailed Cost Analysis

For 420,000 input tokens and 1,000 output tokens:

  • Input Cost: $2.100000
  • Output Cost: $0.025000 (rounded ~ 0.03) (Training mode: $0)
  • Total Cost: $2.125000 (rounded ~ 2.13)
  • Cost per 1K tokens: $0.005048 (rounded ~ 0.01)
  • Tokens per dollar: 198,118 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: 14 minutes, 27.00 seconds
  • Latency: 200 milliseconds to first token
  • Base Throughput: 500 tokens/second
  • Effective Throughput: 485 tokens/second (temperature-adjusted)

Best Use Cases

Training dataML pipelinesDataset preparation

Training Data Processing Cost

Preparing training datasets with image embeddings and text annotations. Combined multimodal processing for AI training pipelines.

Training Data Setup

  • Training Images: 1000 sample images
  • Resolution: Medium (170 tokens each)
  • Image Tokens: 170,000 total
  • Text Annotations: 200,000 tokens
  • Embedding Tokens: 50,000 for vectorization
  • Batch API: Enabled for large datasets
  • Fine-Tuning Mode: Training (2× cost)

ML Pipeline Applications

Training data preparation, dataset annotation, model fine-tuning, AI pipeline development.

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 fine-tuning mode affect AI model costs?
Fine-tuning has two modes: Training (output costs are $0, only input costs apply for model creation) and Fine-Tuned Inference (1.5× base cost for specialized model usage).
How are image tokens calculated?
Images are tokenized based on resolution: Low: 85 tokens, Medium: 170 tokens, High: 255 tokens, Full: 765 tokens per image. Some models (like Llama 4 Maverick) use tile-based encoding with 1,610 tokens/image (standard) or 8,050 tokens/image (high-res).
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.