What does your prompt cost?
Paste anything. See token counts and price across every major model — computed in your browser, not on a server.
Cost by model
| Model | In /1M | Out /1M | Context | This input |
|---|
Batch & repo estimator
Drop many files at once for a combined token and cost total. Files are read locally and never uploaded.
| File | Chars | OpenAI | Claude | Gemini |
|---|---|---|---|---|
| No files yet | ||||
Open-model tokenizers
Exact counts for Llama, Mistral and Qwen. The real tokenizer downloads once from Hugging Face, then runs offline.
Prompt compressor
Strips filler, hedge words and citations to cut tokens. Rule-based, so dense technical prose may compress very little.
Multimodal calculator
Image, video and audio token counts using Google's published Gemini formulas.
Caching savings
Model the cost of sending the same prompt repeatedly with prompt caching enabled. Uses the text from the main calculator.
Browser extension
See cost while you type — not after.
Live cost inside ChatGPT, Claude and Gemini
A small overlay shows your running token count and cost as you write, so you catch an expensive prompt before you send it. Counting happens locally — no text leaves the page.
Add to Chrome — freeAPI
Token counts and cost for any text, across every supported model. Free to use while in beta.
curl -X POST https://YOUR-DOMAIN/api/v1/tokenize \
-H 'content-type: application/json' \
-d '{"text":"Hello world","models":["gpt-5.4"]}'
/api/v1/modelsStatus
betaThe API is free and open while in beta. There are no accounts or keys yet, and no published request limits — there is basic abuse protection, but we won't quote a number we can't guarantee. Build against it, but don't depend on it for anything critical until it's out of beta.
Paid tiers with committed limits and an SLA are planned. If you want to be told when they land, or you need higher volume sooner, get in touch.
Response fields
| Field | Meaning |
|---|---|
| tokens | Token count for that model |
| method | exact — counted with the real tokenizer · estimate — character ratio, no public tokenizer exists |
| input_cost_usd | Cost to send this text as input |
| fits_context | Whether it fits that model's context window |
Case study: team cost dashboard
A concept for what per-team LLM cost tracking could look like. Built as a design exercise, not a live product.
Every number in this dashboard is randomly generated sample data. Nothing is connected to a backend, no accounts exist, and no real usage is being tracked. It exists to show the shape of the idea.
The problem it addresses
A calculator answers "what will this cost?" before you send a prompt. It doesn't answer "what did we spend last month, which team spent it, and why is it climbing?" — which is the question that actually reaches a finance or engineering lead once a company has several LLM features in production.
Two audiences, one surface
| View | Answers |
|---|---|
| Business | Spend over time, budget vs. cap, cost by team and project, which provider dominates |
| IT / developer | Request volume, error rate, latency, per-key rate-limit headroom, request log |
What making it real would require
Authentication so each organisation sees only its own data, a database logging every API call, an SDK that wraps a customer's LLM calls to feed that log, and a scheduled job for budget alerts. That's a different product from a calculator — which is why it's shown here as a concept rather than shipped as a feature.
FAQ
POST /api/v1/tokenize endpoint returns counts and cost for any text across all supported models. The free tier covers 10,000 requests a month with no card required.