Visible planning range
See a likely count and lower-to-upper range instead of one falsely precise number.
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Estimate AI prompt tokens with a transparent English, code, or multilingual heuristic, including a planning range, characters, UTF-8 bytes, words, and detected content profile.
AI Token Counter gives a fast planning estimate for prompt text when an exact model tokenizer is not available. It reports a likely value plus a wider range, then shows the character, UTF-8 byte, word, line, and detected-profile data behind the estimate.
The result is deliberately labeled as an estimate. Different models can use different tokenizers and encodings. Message roles, system instructions, tool definitions, images, files, and provider-specific request wrappers can add tokens that are not present in the pasted text. Use the official counting method for the exact target model before enforcing context limits or reconciling a bill.
Focused controls, predictable output, and a workflow designed around this exact transformation.
See a likely count and lower-to-upper range instead of one falsely precise number.
Separate English prose, code or JSON, and multilingual text assumptions.
Compare Unicode characters, UTF-8 bytes, words, and lines behind the estimate.
Keep heuristic planning separate from exact tokenizer and billing results.
Practical details about input, output, privacy, limits, and the best way to use this tool.
No. It is a transparent heuristic for planning. Exact counts depend on the target model tokenizer and the complete serialized request.
Text-to-token ratios vary with language, vocabulary, punctuation, code, and tokenizer. The range makes that uncertainty visible.
It compares code-like punctuation and non-ASCII character ratios, then selects an English, code, or multilingual heuristic.
Only if the system prompt text is pasted. Hidden instructions, role markers, request wrappers, tool schemas, files, and images are not added automatically.
The provider may tokenize structured message metadata and other request content that is absent from the text box. Output tokens are also separate.
Use the upper planning estimate only for early design. Before sending the request, use the official tokenizer or counting endpoint for the selected model.
Yes for rough planning. Send the result to AI Token Cost Calculator, enter current rates, and replace estimates with provider-reported usage when available.
No AI completion is requested. The server applies deterministic length and profile heuristics to the submitted text.
The English profile uses approximately four Unicode characters per token.
Summarize the following release notes in five bullets.
Likely: 14 tokens; planning range: 10–19; profile: english; 54 characters; 54 UTF-8 bytes.
Code-like punctuation triggers the more conservative code heuristic in Auto mode.
{"user":{"id":42,"active":true},"tags":["api","json"]}
Likely: 18 tokens; planning range: 13–25; profile: code; 54 characters.
The multilingual profile uses UTF-8 bytes so non-Latin text is not treated like ASCII English.
Поясни різницю між JSON і JSONL простими словами.
Likely: 21 tokens; planning range: 15–29; profile: multilingual; 49 characters; 81 UTF-8 bytes.
A tokenizer divides text into units used by a model. A common word can be one token while an uncommon word, code identifier, emoji sequence, or non-Latin phrase may be split differently. Therefore character count is useful for a heuristic but cannot reproduce a model vocabulary.
OpenAI documents model-aware request counting in its token counting guide. Anthropic provides a token counting API for Claude requests. Those methods can evaluate structured request content that a plain text estimate cannot see.
English prose uses an approximate four-character ratio. Code and JSON use a more conservative character ratio because punctuation and short identifiers often create additional boundaries. Multilingual mode uses UTF-8 bytes so multi-byte text is not estimated like ASCII. Auto detection chooses one of these profiles from the visible input.
The lower and upper values are planning bounds around the same heuristic, not statistical confidence intervals. Measure representative production prompts before selecting safety margins.
A real model request can include message roles, previous conversation turns, system instructions, tool or function schemas, image and file inputs, and provider serialization. Paste visible components when comparing drafts, but use provider counting for the final request.
Inspect large tool definitions with JSON to OpenAI Function Schema or MCP Tool Schema Generator. Use JSON Formatter when unnecessary formatting makes a schema hard to review.
The estimate is not tied to a named model or tokenizer. It does not count images, audio, files, cached-token categories, hidden provider metadata, or future output. Emoji with joiners, unusual Unicode normalization, minified code, and compressed structured data can fall outside the displayed range.
For financial planning, continue with AI Token Cost Calculator. Enter current provider rates and keep input and output prices separate.