Cafiyn Pulse
Cost Comparator · Multi-modal app

What does a multi-modal AI product cost when the categories stack?

Text, voice, and image each have their own pricing unit. A multi-modal product pays all of them per user, per day.

Model my bill

Multi-modal products (an assistant that can talk, see, and generate images in the same session) are the hardest workload to estimate because three separate pricing models stack on top of each other: per-token text, per-minute audio, and per-image generation. A single active user touching all three in one session pays three different meters simultaneously.

The practical mistake is modeling each modality's cost in isolation and adding the totals, without accounting for the fact that a real multi-modal session uses them together, not as three separate users. Session-level cost, not modality-level cost, is what should drive the budget conversation.

Who runs this workload
  • AI companions and assistants with voice, vision, and generation in one product
  • Creative tools combining a chat interface with generated media output
  • Next-generation support products that can see a screenshot and respond by voice
Cost drivers

What actually moves the bill.

Which modalities are default vs opt-in

Making voice or image generation default-on for every session multiplies your baseline cost by however many users touch that path.

Session composition

Model a realistic session (say, 60% text-only, 30% text+voice, 10% all three) instead of assuming every user hits every modality.

Cross-modal model choice

You do not need the same quality tier across all three. A premium text model paired with a budget image model is common and defensible.

FAQ

Common questions.

How do I estimate cost for a product with multiple AI modalities?

Model a realistic session mix first (what percent of sessions touch each modality), then price each modality's per-unit cost against that mix, rather than assuming every user hits every capability every session. The calculator above lets you set daily volume per modality separately for exactly this reason.

Should I use one provider for everything or mix providers?

Mixing is standard and often cheaper. Most production multi-modal products use one provider's LLM for text and reasoning, a specialist provider for voice (ElevenLabs, Deepgram), and a specialist for image generation (Flux, DALL-E), rather than one vendor for all three.

Other workloads

See a different shape of AI product.

Chatbot / support assistant
Support bots and product assistants are the most common AI workload, and the easiest to under-budget. Here is the real math.
Voice agent
Voice is three billed legs per turn: transcription, the model, and speech synthesis. Most estimates only price one of them.
Image generation
Per-image pricing looks trivial until you multiply it by users who generate more than once.
RAG / knowledge assistant
Retrieved context is the hidden cost driver in RAG. It is paid as input tokens on every single query.
Code assistant
Coding workloads send more tokens per call than almost any other use case. Context window, not call count, drives the bill.
Per-model pricing

What each model costs for this workload.

Gemini 3.1 Pro pricing
Google's current Pro tier at $2 and $12 per million tokens, replacing the Gemini 2.5 line that is now marked legacy.
GPT-6 Astra pricing
OpenAI's flagship at $10 and $50 per million tokens. Two and a half times GPT-5.6 Sol, and eight times GPT-5.
Claude Sonnet 5 pricing
Anthropic's mid-tier flagship. Here is what it actually costs per token, and what that means for a real monthly bill.
All model pricing
One table, every provider, per million tokens.
Price change log
Every change we verify, including increases already scheduled.

Open the tool.

Live math against your own usage numbers, verified monthly against provider pricing pages.

Model my multi-modal app bill