Same answers as ChatGPT Pro. Receipts that prove ~70% less.
Every prompt is routed to the cheapest model that can actually answer it — across OpenAI, Anthropic, Google, and open-source. You get a receipt for every reply showing the model picked, what you paid, and what you saved.
- Stripe — secure payments
- 30-day money-back
- No training on your data
- 18 models, one bill
Cheaper for you. Lighter on everything else.
Smart routing isn't only about your bill. It's about using the smallest model that can do the job — and being honest about which one ran.
- 01SustainabilitySmallest capable model, always.
Routing to the right-sized model means fewer GPU cycles, lower carbon, and watts that don't get burned on a one-line summary.
- 02Privacy & data ownershipNever used to train foundation models.
Prompts and responses are not contributed to OpenAI, Anthropic, or Google training sets. You own the conversation, and you can export or delete it at any time.
- 03TransparencyEvery receipt shows which model and why.
See the model, the cost, and the savings on every reply. Read our transparency report and privacy policy.
How your savings are calculated.
No magic, no marketing math. Every receipt compares what we actually paid to route your prompt against what the same tokens would have cost on a frontier model.
- 01Step 01We classify your prompt
A lightweight classifier reads the task — drafting, summarizing, reasoning, code — before any frontier model is touched.
- 02Step 02We route to the cheapest capable model
Each task has a shortlist of models that pass our quality bar. We pick the cheapest one — across OpenAI, Anthropic, Google, and open-source.
- 03Step 03We price the answer two ways
Routed cost = (input + output tokens) × the routed model's published per-token rate. Baseline cost = the same tokens at GPT-5 / Claude Opus rates — what a naïve 'always-frontier' setup would have charged.
- 04Step 04Your savings = baseline − routed
Every reply ships with a receipt: model picked, tokens in/out, what you paid, and what you saved vs. the frontier baseline. Numbers are token-accurate, not estimates.