Estimate the monthly bill of any AI feature before you ship it. Plug in token counts, request volume, and traffic curves — get a side-by-side cost across GPT-5, Claude 4.6, Gemini 3, DeepSeek, Llama, Mistral, and 25+ more models.
Most "AI pricing pages" show you a number per million tokens. That's useful for finance, useless for engineering. As a web developer, the question you actually have to answer is: "If I ship this feature behind a button on a page that gets 40,000 visits a month, what does my AWS bill look like next quarter?"
Token math compounds in surprising ways. A 1,200-token system prompt that gets prepended to every chat turn costs nothing in dev. At 10 requests per active user, 5,000 weekly active users, and a 6-turn average conversation, that same prompt is now 1.4 billion input tokens per month before a single response is generated. Multiply by the per-million rate and a "cheap" feature can outrun your hosting budget within a week.
This calculator is built to short-circuit that surprise. You enter the request shape — system prompt size, average user input, expected output, and monthly volume — and it cross-multiplies against current published pricing for every major provider. Output: a sortable table you can paste into a Notion doc, an RFC, or a client estimate.
Before you wire up the model behind your /api/chat route, run the worst-case numbers. If 95th-percentile cost per user-month exceeds your subscription tier price, you have a unit-economics problem and need to redesign — not a scaling problem.
Anthropic raised input pricing on a tier? OpenAI shipped a cheaper mini variant? Drop the same workload into the comparison view and see whether the migration is worth the engineering days. Most teams discover that 3-5× of their cost is in one model call that has no business being on the frontier tier.
The calculator separates input from output tokens, so you can model what happens when you trim a system prompt by 40%, switch to prompt caching, or move boilerplate out of every turn. The savings are usually larger than swapping providers.
Freelancers and agency devs ship AI features into client codebases all the time, then get blindsided when the client's CFO asks for a 12-month run-rate. Run the projection here, paste the table into your proposal, and price the integration with margins that survive scale.
If you're shipping AI inside an existing SaaS, you need to know the per-seat marginal cost to set the right plan boundaries. The calculator's "cost per active user" view answers that directly so PMs stop free-tiering away the margin.
Three failure modes show up over and over in production AI features built by web devs:
The calculator surfaces all three by design — it asks for context size, output length, and volume separately, then recomputes the bill against every supported provider. See OpenAI's pricing page for the canonical input/output split, or Google's Gemini pricing for context-window tier breakpoints.
To make the numbers concrete, here's how a typical "support chatbot embedded in a marketing site" lands:
| Model | Monthly cost | Cost / chat |
|---|---|---|
| GPT-5 | $1,142 | $0.357 |
| Claude Sonnet 4.6 | $884 | $0.276 |
| Gemini 3 Flash | $118 | $0.037 |
| DeepSeek V3.1 | $71 | $0.022 |
Numbers above are illustrative. Plug your real shape into the live tool to get a current comparison with the latest published rates.
Yes — the comparison table is plain HTML, so you can copy it into a Notion page, a Linear issue, or a markdown RFC. We're adding a one-click CSV export soon.
Partially. We include the major hosted endpoints (Together, Fireworks, Groq, Bedrock, Vertex) for Llama, Mistral, Qwen, and DeepSeek. Pure GPU self-host pricing is too workload-dependent to model accurately, but the hosted serverless rates are a reasonable upper bound.
The calculator reads from a price table that we update whenever a major provider publishes a change. Expect 1-3 day lag on smaller providers, near-real-time on the top five.