Bildauflösung in AI-Token-Konverter

The Image Resolution to AI Tokens Converter estimates how many tokens a given image will consume when passed to vision-capable AI models (OpenAI GPT-4o and GPT-4 Vision, Anthropic Claude 3 Opus/Sonnet/Haiku and Claude 3.5/4.x, Google Gemini 1.5 Pro/Flash). Each model uses a different tile-based algorithm to convert pixel dimensions into token cost. Token consumption directly maps to API cost — knowing token counts before sending lets developers budget spend, decide whether to resize, and choose between low-detail and high-detail modes. OpenAI GPT-4o high-detail uses 85 base tokens + 170 tokens per 512×512 tile. A 1024×1024 image needs ⌈1024/512⌉ × ⌈1024/512⌉ = 4 tiles = 85 + 4×170 = 765 tokens (~$0.004 per image at GPT-4o input rates). Low-detail mode is a flat 85 tokens regardless of size — ~9× cheaper for thumbnails, OCR of large text, or classification tasks that don't need fine detail. Claude 3 family uses a simpler formula: tokens ≈ width × height / 750 (so 1024×1024 ≈ 1,400 tokens). Gemini 1.5 charges roughly 258 tokens for any image up to 384×384 plus tiles beyond that. Understanding when to downscale: most vision tasks (object classification, scene description, OCR of standard text) don't benefit from resolutions above 1024 pixels on the long edge. Downscaling a 4K screenshot from 3840×2160 to 1024×576 cuts tokens by ~10× with minimal quality loss for these tasks. Fine detail tasks (handwriting OCR, medical imaging, satellite analysis) benefit from full resolution. For thumbnails or low-stakes classification, OpenAI's low-detail mode is dramatically cheaper. This calculator helps developers budget vision API spend before building image-heavy features (content moderation, e-commerce product analysis, accessibility alt-text generation, document parsing). At GPT-4o pricing (~$5/M input tokens as of mid-2024), 1 million high-detail 1024×1024 images cost ~$3,800. Choosing low-detail mode for the 80% of images that don't need fine detail drops the same workload to ~$425 — a 9× cost reduction.

Calkulon makes complex calculations simple — built for students and everyday problem-solvers.

1. 1Step 1 — Enter image width and height in pixels
2. 2Step 2 — Select the target AI model (each uses a different tile algorithm and pricing)
3. 3Step 3 — Select detail level (OpenAI only — low is 85 tokens flat, high is tile-based)
4. 4Step 4 — Calculator applies the model's specific token formula (tiles × per-tile cost + base)
5. 5Step 5 — Output displays estimated tokens, tile count (where applicable), and cost per image
6. 6Step 6 — Cost projections at 1K and 10K image volumes for budget planning
7. 7Step 7 — Compare costs across models to choose the most economical fit for your use case

• !Forgetting that low-detail mode is much cheaper for thumbnails and triage classification
• !Not capping image resolution before upload — sending 4K images when 1024px suffices
• !Assuming all models cost the same per image (they vary 3–10× for the same input)
• !Ignoring input vs output token cost split — vision inputs are expensive but outputs are typically short
• !Encoding to base64 thinking it changes token count (it doesn't — only dimensions matter)