Multimodal Prompt Cost Estimator

What it does

Multimodal LLM inputs (images, video frames, audio, PDFs) are the fastest-growing source of API cost surprises in production AI workflows. A single image carries roughly the same token cost as 1000-1500 words of text. A 1-minute video sampled at 1fps is 60 frames × 1500 tokens = 90,000 tokens — enough to fill the average chat context on its own. Audio is similar (~1500 tokens per minute via speech-to-text + processing). Builders accustomed to text pricing get shocked when their first vision-heavy or video-analysis production workload comes back 10-50x more expensive than expected. The estimator translates multimodal input into token equivalents and dollar costs across providers.

Provider-specific tokenization (2024-2025 conventions): Gemini and Claude use roughly 1500 tokens per image (varies slightly by resolution; high-res images can hit 3000 tokens). GPT-5 vision uses a patch-based formula (each 512×512 patch ≈ 170 tokens, stitched at 85 tokens overhead) that lands within ±10% of the 1500/image baseline. Video: 1fps sampling = 60 tokens-equivalents per minute via image-conversion math; some providers compress further with temporal encoding. Audio: Whisper-style transcription plus context yields ~1500 tokens per minute of speech. PDF: ~250 tokens per page for text content, plus 1500 per embedded image. The calculator applies these conversions and outputs total token cost per call plus monthly bill at your call volume.

Cost-control strategies the estimator surfaces: (1) Image resolution downscaling — most use cases work at 1024×1024 or smaller. Some Gemini and Claude pricing tiers offer “low resolution” mode at substantial token discount. (2) Video frame-rate reduction — 1fps is standard but 0.25fps (1 frame per 4 seconds) often sufficient for slowly- changing scenes; cuts video cost 4x. (3) Selective frame sampling — extract only keyframes (scene-change detection) instead of regular sampling; can reduce video cost 5-10x with minimal quality loss. (4) Pre- processing — for documents, OCR + text extraction into the prompt is much cheaper than image-feeding the PDF. (5) Audio: use a dedicated STT API (Whisper, Deepgram) instead of feeding raw audio to a multimodal LLM — STT is 10-100x cheaper. (6) Prompt caching dramatically helps with repeated large multimodal contexts (system prompt with example images, etc.).

How to use it

1. Enter text input tokens per call.
2. Enter number of images, video duration, audio duration per call.
3. Set monthly call volume.
4. Read total token equivalent and monthly cost across providers.
5. Use to plan multimodal architecture before deploying production workloads.

When to use this tool

• Pre-deployment cost forecasting for vision / video / audio AI workloads.
• Comparing providers (Gemini vs Claude vs GPT-5) for multimodal-heavy use cases.
• Identifying which input modality is dominating costs.
• Optimization planning — where to invest in pre-processing vs raw multimodal calls.
• Pitch decks for AI features that consume large multimodal context.

When not to use it

• Pure text-only workloads — use standard token cost calculator.
• Specialty multimodal models (Whisper STT, Stable Diffusion image gen) — those have specific pricing not covered.
• Real-time streaming workloads — pricing models differ for streaming endpoints.
• Local / self-hosted multimodal models — no API token cost.

Common use cases

• Pre-decision sanity-check on inputs and outputs
• Educational use — demonstrating the underlying concept
• Onboarding a colleague who needs the same calculation/conversion
• Verifying a number or output before passing it on