What does “token efficiency” mean?

Language models process text in units called tokens. A token may be a whole word, part of a word, punctuation or another text unit. Token counts are not the same as word counts, and the same text can produce different counts across models, encodings and languages. For that reason, a token-efficiency assessment should state which content and tokenizer it measures. OpenAI’s token guide explains these differences and cautions that estimates vary by model and language.

In this article, web page token efficiency means how much task-relevant, usable information a page communicates in relation to its token footprint. This is a working concept for evaluation—not a universal industry standard or a claim that the shortest page is the best one.

Why measure it now?

People are adding AI tools to how they search, compare and explore information, while continuing to use traditional search. In a 2025 UK survey of 4,189 adults, 51% said they had used AI search tools to look for products, services or advice; 24% used them frequently, compared with 82% who frequently used traditional search engines. The figures describe that survey’s UK respondents, not a universal or permanent adoption rate. Read the Which? survey.

This makes it increasingly valuable to present information so that people can understand it and automated systems can identify its subject, evidence and context. It does not mean that a concise page is guaranteed to be cited, recommended or included in an AI-generated answer.

A practical way to evaluate a page

Start with the reader’s task, not the token counter.

  1. Define the page’s purpose. What should a reader be able to understand, compare or do after reading?
  2. List the essential information. Identify the main answer, definitions, evidence, limitations and next steps required to fulfil that purpose.
  3. Measure the relevant content consistently. State whether you count the main article, the whole rendered page or another defined portion. Use the intended model’s tokenizer where possible; don’t assume one tokenizer’s count applies to another.
  4. Check whether the information survives compression. If repeated wording is removed, are the meaning, important qualifications, evidence and practical usefulness still intact?
  5. Test extraction with representative questions. Ask a fixed set of questions that a reader or AI-assisted search system might ask. Check whether the page supports a correct, sufficiently complete answer—and whether the cited material actually backs it up.
  6. Compare versions under the same conditions. Keep the tokenizer, content scope, questions and evaluation criteria consistent. Record changes in token count alongside clarity, completeness and answer quality.

A useful conceptual question is: How much verified, task-relevant information can a reader or system use from this page, relative to its token footprint? The answer should not be reduced to a raw token count. A page that is short but vague, incomplete or hard to interpret is not necessarily efficient.

Does reducing tokens make AI faster or greener?

Sometimes, in specific workflows. Research on prompt compression has reported substantial reductions in prompt size and faster model inference in selected benchmark settings. For example, the LLMLingua study reported compression of up to 20× on its tested tasks; these are results for a particular prompt-compression method—not a measurement of web-page efficiency or a promised speed increase for every AI system. Jiang et al., EMNLP 2023.

Likewise, a 2025 study of inference optimizations reported energy reductions of up to 73% against its unoptimized baselines. That finding applies to the optimizations and workloads studied; it does not show that reducing a page’s tokens by a given percentage produces the same energy saving. Fernandez et al., ACL 2025.

Model, hardware, serving software, batching, caching, input length, output length and task complexity can all affect total processing time and energy. Token reduction can also discard context that matters. A recent arXiv preprint reports provider-dependent energy effects from prompt compression, illustrating why input-token reduction alone should not be treated as a guaranteed energy optimization. Johnson, 2026 preprint.

So, token efficiency is relevant to sustainability as a design question, but a web-page score should not be presented as a measure of energy, carbon or time saved unless those outcomes have been directly measured. Any reliable percentage would require controlled tests that specify the model, hardware, task, content, quality threshold and measurement method.

Efficiency should preserve meaning

Good token efficiency is not “write as little as possible.” It is closer to “make each section earn its place.” Descriptive headings, direct explanations, useful examples, clear sourcing and explicit limitations can help people navigate a page and make its content easier to identify. Removing necessary context or turning prose into cryptic fragments may reduce a count while making the page worse.

This is consistent with Google’s published guidance: the same SEO fundamentals apply to its AI search features, and there is no special markup that guarantees inclusion. Google emphasizes helpful, reliable, people-first content, technical accessibility and content that is available in text. See Google Search guidance on AI features and helpful content.

A responsible measurement principle

A credible evaluation should report more than “tokens saved.” It should say what part of the page was measured, which tokenizer was used, what information had to be preserved and how usefulness was checked. It should also make limitations visible.

Token efficiency can help teams ask a practical question: Can this page deliver the same useful, reliable information more clearly, without unnecessary repetition? That is a testable editorial goal. Whether a particular change improves AI retrieval, response time or environmental impact is a further question—and should be measured separately.

References

  1. OpenAI. Understanding and counting tokens.
  2. Gibson, A. & Leader, M. (2025). Consumer use and attitudes towards AI search tools. Which?
  3. Jiang, H. et al. (2023). LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models. EMNLP 2023.
  4. Fernandez, J. et al. (2025). Energy Considerations of Large Language Model Inference and Efficiency Optimizations. ACL 2025.
  5. Johnson, W. (2026). The Compression Paradox in LLM Inference: Provider-Dependent Energy Effects of Prompt Compression. arXiv preprint.
  6. Google Search Central. AI Features and Your Website.
  7. Google Search Central. Creating helpful, reliable, people-first content.