AI Image Token Calculator

Calculate vision API costs for GPT-4, Claude, and Gemini

Select Vision Model

Latest multimodal model, fast and affordable

Image Settings

Include your prompt and expected response tokens

Token & Cost Estimate

Tokens per Image

1,105

0.92 MP

Total Image Tokens

1,105

1 image

Total Tokens

1,605

+ 500 text tokens

Total Cost

$0.004013

$0.002763/image

Cost Breakdown

Image Processing

$0.002763

1,105 tokens

Text (Prompt + Response)

$0.001250

500 tokens

Model Rate

$2.50/1M

GPT-4o

Compare All Models (1280x720, 1 image)

ModelTokens/ImagePrice/1MTotal Cost
GPT-4o(OpenAI)1,105$2.50$0.004013
GPT-4o mini(OpenAI)1,105$0.15$0.000241
GPT-4 Turbo(OpenAI)1,105$10.00$0.016050
Claude 3.5 Sonnet(Anthropic)1,272$3.00$0.005316
Claude 3 Opus(Anthropic)1,272$15.00$0.026580
Claude 3 Haiku(Anthropic)1,272$0.25$0.000443
Gemini 2.0 Flash(Google)258$0.10$0.000076
Gemini 1.5 Pro(Google)258$1.25$0.000947

OpenAI Vision: Uses a tiling system where images are scaled and divided into 512x512 pixel tiles. Each tile costs ~170 tokens plus a base cost of 85 tokens. Larger images = more tiles = more cost.

Claude Vision: Token count is based on image resolution/megapixels. Smaller images use fewer tokens. Maximum resolution supported is 8192x8192.

Gemini Vision: Currently charges a fixed token count per image (~258 tokens) regardless of resolution, making it predictable for budgeting.

Note: Token calculations are estimates based on documented algorithms as of January 2025. Actual token counts may vary slightly. Always verify with the provider's official documentation for production use.

How to Use the AI Image Token Calculator

Select Your Vision Model

Choose from GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, Claude 3 Haiku, or Gemini 2.0 Flash. Each model has different token calculation methods and pricing for image processing.

Enter Image Dimensions

Input your image resolution (width x height in pixels). OpenAI uses a tile-based system where larger images cost more, while Gemini charges a flat rate regardless of size.

Set Number of Images

Specify how many images you plan to process. The calculator will multiply the per-image token cost by your quantity to show total token usage and estimated API cost.

Compare Costs Across Models

Review the cost comparison showing token counts and prices for each vision model. Gemini is often cheapest for high-resolution images, while GPT-4o mini excels for simple tasks.

Pro tip: Your data is processed entirely in your browser. Nothing is sent to any server, ensuring complete privacy.

Understanding AI Vision API Pricing

AI vision models like GPT-4o, Claude 3, and Gemini can analyze images and answer questions about visual content. However, processing images adds to your API costs through additional token usage. Understanding how each provider calculates image tokens helps you budget effectively.

How Vision Models Calculate Tokens

OpenAI (GPT-4o, GPT-4 Turbo): Uses a tiling algorithm. Images are first scaled down if larger than 2048px on any side, then the shortest side is scaled to 768px. The result is divided into 512x512 tiles. Each tile costs ~170 tokens plus a base cost of 85 tokens.

Anthropic (Claude 3 family): Token count is based on image resolution in megapixels. Roughly 1,380 tokens per megapixel with a minimum of ~300 tokens. Maximum supported resolution is 8192x8192.

Google (Gemini): Currently uses a fixed token count of ~258 tokens per image regardless of resolution. This makes cost predictable but means small images cost the same as large ones.

Typical Image Token Costs

ResolutionGPT-4oClaude 3.5Gemini 2.0
512×512255 tokens~360 tokens258 tokens
1024×1024765 tokens~1,400 tokens258 tokens
1920×1080765 tokens~1,590 tokens258 tokens
4K (3840×2160)765 tokens~1,590 tokens258 tokens

Cost Optimization Tips

  • Resize images: For OpenAI, images larger than 1024px often don't add value but do add cost. Resize to the minimum needed.
  • Choose the right model: GPT-4o mini and Claude 3 Haiku are significantly cheaper for simple vision tasks.
  • Batch processing: If analyzing many similar images, consider if a cheaper model can handle the task.
  • Use Gemini for volume: Fixed per-image pricing makes Gemini cost-effective for high-resolution images.
  • Compress when possible: JPEG quality doesn't affect token count, so compression reduces bandwidth without increasing cost.

When to Use Each Model

  • GPT-4o: Best for complex visual reasoning, document analysis, and detailed image understanding.
  • GPT-4o mini: Great for simple image classification, basic OCR, and high-volume simple tasks.
  • Claude 3.5 Sonnet: Excellent for nuanced visual analysis, charts, and technical diagrams.
  • Claude 3 Haiku: Fast and cheap for simple image tasks, good for real-time applications.
  • Gemini 2.0 Flash: Best value for simple vision tasks, especially with high-resolution images.

Frequently Asked Questions

How does GPT-4 Vision calculate image tokens?

GPT-4 Vision uses a tiling system where images are scaled down and divided into 512×512 pixel tiles. Each tile costs approximately 170 tokens plus a base cost of 85 tokens. Larger images require more tiles and thus more tokens, though OpenAI caps the scaling to prevent excessive costs.

How much does it cost to process an image with AI?

Costs vary significantly by model. GPT-4o typically costs $0.0008-0.003 per image, GPT-4o mini costs $0.00005-0.0002, Claude 3 Haiku costs $0.0001-0.0004, and Gemini 2.0 Flash costs around $0.00003 per image. Higher resolution images cost more on OpenAI and Claude but not on Gemini.

Which AI vision model is cheapest?

Gemini 2.0 Flash and GPT-4o mini are currently the cheapest vision models. Gemini charges a flat ~258 tokens per image at $0.10/1M tokens, making it extremely cost-effective. GPT-4o mini charges variable tokens based on resolution at $0.15/1M tokens.

Does image resolution affect token count?

Yes, for OpenAI and Anthropic models. OpenAI uses a tile-based system where larger images require more tiles. Anthropic charges based on megapixels. Gemini currently uses a fixed token count regardless of resolution, which can be advantageous for high-resolution images.

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