Stop paying for prompt tokens you don't need.
Token Optimiser removes unnecessary tokens from the prompt you're about to send, while preserving what you asked for — and shows the estimated saving before you use it.
See the before and after on your own prompt.
No credit card. Free plan available.
Already efficient? We'll tell you — not invent a saving.
Find your optimisation style.
One prompt. Four optimisation styles. Choose the approach that matches your workflow — from maximum readability to maximum token reduction.
Please analyse the following customer reviews and identify the three biggest customer complaints. Then recommend one practical solution for each issue. Present everything as a concise report.
Analyse the following customer reviews and identify the three most common complaints. Recommend one practical solution for each issue and present the results in a concise report.
Keeps prompts natural and easy to read while removing unnecessary wording.
- Preserves context
- Maintains readability
- Moderate token reduction
Start free. Upgrade when the savings pay for it.
Get started with balanced, generic optimisation.
- 10 prompt optimisations / month
- Balanced style
- Generic model target
- Optimisation history
Unlimited optimisations with the full dashboard.
- Unlimited prompt optimisations
- Balanced style
- Claude, ChatGPT, Gemini & Grok model targets
- Full dashboard & analytics
Every optimisation style and every model target.
- Unlimited prompt optimisations
- Balanced, Aggressive, Caveman & Structured styles
- Claude, ChatGPT, Gemini & Grok model targets
- Full dashboard & analytics
Choose the level of optimisation that fits your AI usage — from 10 prompts a month to unlimited optimisation and advanced controls.
Three steps, roughly ten seconds.
Drop in the prompt you were about to send to ChatGPT, Claude, or Gemini.
Token Optimiser scans for repeated context, filler, and ambiguity, and estimates the cost against the model's public pricing.
You get a rewritten version, a token-and-cost delta, and a plain-language note on what changed.
Most prompts leak tokens in three predictable places.
Whole paragraphs from the system prompt get pasted again into each user message — "As mentioned above, remember that…" repeats what the model already sees.
"You are a helpful assistant. Please help me with the following. I would like you to carefully…" — filler that costs money every call and changes nothing about the output.
Vague asks like 'make this better' force the model to hedge across long possible outputs. A specific instruction is shorter and just as clear.
Read our guide to spotting these patterns yourself — no tool required.
Different jobs, same problem: prompts get bloated.
Ship client work faster. Trim the boilerplate you paste into every ChatGPT session and stop billing hours against your own API key.
Your product sends the same prompts again and again. Reducing repeated, padded, or unnecessary prompt tokens can lower input-token usage on frequently executed prompts — copy the optimised version straight into your code.
Standardise how your team writes prompts. Share optimised versions, spot the recurring waste patterns, and put a ceiling on runaway AI spend.
The same request, without the padding.
This is the actual diff view you get inside Token Optimiser. Removed text on the left, the rewritten version on the right.
Reduce. Understand. Verify.
Cut token count on the prompts you already send — savings vary by prompt, with the biggest gains on padded, repetitive, or filler-heavy requests — without changing the model's behaviour.
Every optimisation shows what was removed and why: repeated instructions, filler phrases, ambiguous asks. You learn the patterns.
See exactly what changed, the token difference before and after, and the estimated cost impact — nothing is a guess.
Formatted for the model you target.
Each model reads instructions differently. Pick a target and Token Optimiser applies deterministic, model-specific formatting rules on top of the style you chose — the same token-reduction mechanism, adapted to the model you're sending to.
XML-tagged source content
Numbered lists, explicit format
Section dividers, response framing
Strips meta-phrasing prefixes
Honest numbers, clearly labelled.
Every saving on Token Optimiser is labelled estimated. We calculate it from the token count of your original vs. optimised prompt, multiplied by a representative standard API input rate in USD.
Actual billing depends on the response length, model version, and any negotiated rate on your account, so real invoices vary. We show the workings on every result so you can sanity-check the arithmetic yourself.
See the full V1 benchmark — what we actually measured across 1,400 test executions, including the results that didn't save anything.
Watch your estimated savings compound.
Your dashboard tracks reduction percentage, tokens saved, and estimated spend avoided across every optimisation you run.
Questions people ask before signing up.
What does an AI prompt optimizer actually do?
Token Optimiser removes unnecessary tokens from the prompt you're about to send. Paste a prompt and it analyses repeated context, filler, and ambiguity, then returns a shorter version that keeps your original intent and constraints — with the estimated token and cost savings shown before you use it. In short, it's an AI prompt optimizer focused on one thing: proving what you saved.
How accurate are the estimates?
Token counts use a simple ~4-characters-per-token estimate. Cost estimates multiply that count by a representative standard API input rate in USD. It's a close proxy for input cost — output cost varies with the response length, which we don't predict. We label every number 'estimated' and show the arithmetic on each result.
Does it work with ChatGPT, Claude, Gemini, and Grok?
Yes. You pick the target model in the Optimiser screen and each one gets deterministic, model-specific formatting rules on top of your chosen style — the underlying token-reduction mechanism is the same across targets.
Do you send my prompts anywhere I don't expect?
Optimisation runs on our servers and the result is stored in your private history. Nothing is shared with other users or third parties.
Will optimising change the model's answer?
The goal is to preserve your intent and constraints while removing filler — not to change what the model does with your request. We don't measure or claim any change in answer quality; what you get is a shorter prompt, the token count before and after, and the estimated cost difference.
Can I cancel Pro at any time?
Yes — one click from the Billing screen, and you keep Pro access until the end of the current period.