Transparent accuracy
See the tokenizer, confidence, data source, and verification date behind every result.
Preparing your workspace
Connecting MemonaIQ servicesPreparing your workspace
Connecting MemonaIQ servicesCount tokens, protect context, compare models, and forecast API cost—all in a private workspace that keeps your prompt in the browser.
Tokens
1,284
Context
0.12%
Trust every number
The workspace combines local tokenizer output with source-linked model metadata. When exact data is unavailable, it says so instead of silently guessing.
See the tokenizer, confidence, data source, and verification date behind every result.
Your prompt stays in browser memory and is processed by a dedicated local worker.
Reserve output and safety capacity, then see exactly how much usable context remains.
Preview deterministic removals without rewriting or replacing your original prompt.
Compare token efficiency without confusing fewer tokens with better model quality.
Explore cache, batch, output, request-volume, and long-context pricing rules.
A token is a piece of text recognized by a model's tokenizer. It may be a word, part of a word, punctuation, whitespace, or bytes representing Unicode text. Token boundaries are mechanical and do not necessarily carry semantic meaning.
Models can use different vocabularies, normalization rules, and tokenization algorithms. Never use an OpenAI-compatible encoding as an exact Claude, Gemini, or Llama count.
Words and characters are useful writing metrics, but neither maps reliably to tokens. Code, JSON, URLs, emoji, and multilingual text can vary substantially, which is why MemonaIQ runs the selected tokenizer.
A context window is the request budget shared by input, conversation history, tool definitions, provider wrappers, and output. A safe plan subtracts reserved output and a safety buffer before judging whether a prompt fits.
Deterministic truncation removes token-aligned content from the head, tail, or middle. Structured strategies try to preserve valid JSON or Markdown fences. Protected selections are never knowingly removed.
Providers generally price input and output separately per token unit. Cost figures here multiply a dated rate card by your configured workload; they are estimates rather than billing guarantees.
Vocabulary coverage differs by script and language. Fewer tokens can mean more efficient context use, but it does not prove that a model understands or generates that language better.
Default analysis has no provider call. Prompt text stays in browser memory, moves to a local worker for computation, and is excluded from settings, URLs, analytics events, and exports unless a future explicit control says otherwise.
No. Default tokenization, analysis, comparison, truncation, and export run locally in your browser. Prompt content is not intentionally sent to MemonaIQ or analytics.
For the supported o200k_base and cl100k_base encodings, the local result uses a tiktoken-compatible implementation verified against reference vectors. Chat message wrappers and provider-specific request overhead remain estimates.
Tokenizer vocabularies and splitting rules differ. The same text, code, punctuation, emoji, or language can therefore consume different numbers of tokens.
No. Providers can also account for message wrappers, tool definitions, cached content, output, and reasoning categories. Reserve output capacity and a safety buffer.
No. The truncator removes content deterministically. It does not summarize, paraphrase, or silently overwrite the original prompt.
Yes. Supported text and source files up to 5 MB are read with browser APIs and are not uploaded.
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