Are token counts exact?

No. Counts are estimates unless exact model-specific tokenizers are implemented.

TokenTrim Docs

What TokenTrim does

TokenTrim is a free AI prompt optimization platform that helps developers, prompt engineers, AI agents, founders, RAG builders, and automation teams analyze prompts, detect wasted tokens, estimate possible savings, and clean prompts before using ChatGPT, Claude, Gemini, or other AI APIs.

How to use the App

Open the App.
Paste a prompt, agent instruction, RAG context block, or AI API payload.
Select OpenAI, Claude, Gemini, or Generic LLM.
Review estimated original tokens, optimized tokens, waste percentage, compression ratio, possible savings, and waste categories.
Copy the optimized prompt and review it before using it with any AI system.

How token estimates work

The current build uses browser-side word and character heuristics. Estimates are useful for planning, but exact counts may differ from provider usage records because exact model-specific tokenizers are not implemented.

How waste detection works

TokenTrim checks for repeated context, verbose instruction wording, stale background, and unclear task objectives. These categories are practical signals, not formal guarantees.

How savings estimates work

Estimated possible savings compare estimated original tokens with estimated optimized tokens against a simple provider-family input-cost assumption. Actual savings depend on model, provider cost tables, output length, caching, and API behavior.

Prompt optimization examples

Before: long background, repeated notes, soft wording, and no clear output. After: task first, relevant facts only, concise instructions, and a bounded output request.

Privacy model

Prompt analysis currently runs in the browser. TokenTrim does not send prompt content to a TokenTrim prompt-analysis server in this build. The app may use browser localStorage for local settings and result state.

Limitations

TokenTrim does not guarantee exact token counts, exact savings, model quality, compliance outcomes, or provider-specific cost results.

Best practices

Put the task first, remove repeated context, summarize long history, keep only relevant RAG passages, and never paste secrets, passwords, API keys, private customer data, or sensitive personal data.