GPT GPT-4.1
0 tokens
0% of 1,000,000-token window
GPT-4.1 publishes a 1M-token context window; GPT-4o is 128K — verify which variant you're targeting.
VIBE CODING / AI-ASSISTED DEVELOPMENT
AI Token & Context Window Counter
Paste text or code and see an approximate token count for GPT, Claude, and Gemini side by side, plus what share of each model's real published context window that usage consumes.
- Updates live — no submit button
- Family-specific approximation, not one constant divided three ways
- Real published context window sizes, named per model
0 characters
Claude Claude Sonnet 4.5
0 tokens
0% of 200,000-token window
200K tokens standard; a 1M-token context window is available in beta for some accounts.
Gemini Gemini 2.5 Pro
0 tokens
0% of 1,000,000-token window
1M tokens standard, with a 2M-token window previewed for some Gemini 1.5/2.x tiers.
These are heuristic approximations of each vendor's real tokenizer, not exact BPE/SentencePiece output — expect single-digit-percent variance from the vendor's own counting tool. Context window sizes change as vendors ship new models; verify current figures on the vendor's docs before relying on them for a production limit.
How it works
Paste your text or code
Drop in a prompt, a file, or a chat log. The counter updates live as you type — there is no submit button.
Three family-specific approximations run at once
GPT and Claude estimates blend a chars-per-token baseline with a word-count adjustment (BPE tends to split longer or rarer words into more than one token); Gemini uses a similar but separately tuned ratio. None divide the same character count by the same constant.
See usage against real published context windows
Each column names the specific model its window size is drawn from (e.g. GPT-4.1 at 1M tokens, Claude Sonnet 4.5 at 200K, Gemini 2.5 Pro at 1M) so the percentage is checkable, not a guess.
Continue learning: Prompt and context management for AI-assisted coding · Should your AI MVP use RAG or a longer prompt?
Frequently asked questions
Is this an exact token count?
No. It is a heuristic approximation of each vendor's real BPE or SentencePiece tokenizer, which requires the vendor's own vocabulary file to compute exactly and cannot run client-side. Expect single-digit-percent variance from the vendor's own counting tool.
Why do GPT, Claude, and Gemini show different counts for the same text?
Each vendor uses a different tokenizer trained on a different vocabulary, so the same text splits into a different number of tokens per family. This tool approximates that difference rather than applying one identical ratio to all three.
Are the context window sizes current?
They reflect each model's publicly stated context window as of this tool's last review, with the specific model named in each column. Vendors expand context windows with new releases — verify the current figure on the vendor's own documentation before relying on it as a hard limit.
Does this count image, audio, or file attachments?
No. It only counts the plain text or code you paste into the box. Multimodal inputs consume additional tokens that this tool does not estimate.
Why does the percentage matter?
Getting close to a model's context window risks truncated input, dropped earlier context, or a hard error. Checking usage before you send a large prompt or long conversation history helps you catch that early.
How We Compare
| Feature | MVPHub | OpenAI Tokenizer | Tiktoken (Python library) |
|---|---|---|---|
| Compares GPT, Claude, and Gemini side by side | Included | Not included | Not included |
| No install or code required | Included | Included | Not included |
| Shows % of real context window used | Included | Not included | Not included |
| Runs entirely client-side, nothing uploaded | Included | Included | Limited |
OpenAI's Tokenizer and the tiktoken library give an exact GPT token count but cover one vendor at a time. MVPHub approximates all three major families together with published context window sizes, for a fast side-by-side check.
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