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Search VisibilityMonitor how AI sees your brand — visibility, position, and sentimentPrompt IntelligenceThe exact questions buyers ask AI — and whether you're in the answerCompetitor BenchmarkingSee who AI recommends instead of you — share of voice, position, and gapsSource AnalyticsThe domains AI trusts when it answers — and how to become oneVisitor AnalyticsSee every AI bot and human visitor — the traffic GA4 missesAction CenterKnow what to do next — every action ranked by impact, tracked to donePage OptimizationSix-dimension audits that show why AI cites a page — or skips itContent LabFrom visibility gap to published post — content AI wants to cite
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BrandsSee what AI answers when buyers askAgenciesRun AI visibility across a client portfolioSEO & GEO ProfessionalsAdd GEO to the service you already sell
PricingResources
BlogResearch & playbooks for AI searchResearchPublished papers on AI searchAI Visibility ReportYour AEO/GEO baseline, emailedFree ToolsAudit or generate, no signupCompare GEO/AEO platformsRanqo vs alternatives, head-to-head
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Page Token Inspector

See your page through AI's eyes. Tokenize any URL or pasted text with the GPT-4o tokenizer and find out where your message lands.

Why Page Token Inspector matters

AI models do not read your page like a human. They tokenize it, and long URLs, heavy code blocks, and generic boilerplate eat the budget before the model reaches your substance. This tool shows exactly where your tokens go, which words fall inside the opening slice, and how your page compares against the context windows of the models that would have to hold it.

How it works

  1. 01

    Give it a URL or paste text

    A URL is fetched and reduced to its main content, the way a model would receive it after boilerplate is stripped. Pasting raw text skips the fetch entirely, which is useful for grading a draft before you publish it.

  2. 02

    We tokenize with the real encoder

    o200k_base, the GPT-4o family tokenizer, run locally through js-tiktoken. These are the actual token boundaries, not a characters-divided-by-four estimate.

  3. 03

    You see the split

    Total tokens, a per-section breakdown, the exact text inside the first 512 tokens, and how the whole page measures against four real context windows.

What it checks

Total token count
What your page costs a model to process, computed with the GPT-4o encoder rather than approximated from word count.
The first 512 tokens
The opening slice, rendered token by token so you can see precisely where it ends. If your central claim sits after it, you find out here.
Per-section breakdown
Title, H1, H2, paragraphs, lists, and alt text tokenized separately, so you can see which parts of the page are consuming the budget.
Context-window comparison
Your page measured against GPT-3.5 Turbo at 16k, GPT-4o at 128k, Claude at 200k, and Gemini 2.5 Flash at 1M, with the percentage each would spend on you.
[ FAQ ]

Page Token Inspector,
answered.

The unit a model actually reads. Roughly three quarters of a word in English, but not uniformly: common words are one token, rare words split into several, and a long URL or a hash can burn a dozen on its own. That unevenness is why counting words tells you less than you think.
As a practical framing device, not a published threshold. No vendor documents a fixed weighting for the opening of a page. What is well established is that models are sensitive to position and that content near the start is more reliably attended to, so treating the first 512 as your working budget is a useful discipline. Do not read it as a rule anyone has published.
o200k_base, used by the GPT-4o family. Claude and Gemini use different tokenizers, so their counts will differ by a few percent. For deciding whether your key claim is early enough, that difference does not change the answer.
No. Every model listed here holds far more than that. Length is rarely the problem; ordering is. A 3,000-token page that leads with its answer outperforms an 800-token page that buries it under a preamble.
No. We extract the main content the way a crawler would before tokenizing, so menus, cookie banners, and footers are excluded. That is deliberate: counting your nav would tell you about your template, not your page.
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