LLM Cost per 1,000 Words Calculator

Comparing AI-generated content cost against traditional per-word content pricing requires translating between two genuinely different units — the token-based pricing every LLM provider uses internally, and the per-word pricing convention that freelance writers, content agencies, and most publishing professionals have used for decades — and this conversion, while mathematically simple, is worth doing explicitly rather than leaving as an intuitive guess, since the actual gap between AI and traditional content cost is often larger than people expect once genuinely calculated.

The conversion itself hinges on the tokens-per-word ratio, which represents how many tokens a given model's tokenizer typically needs to represent one word of text. This ratio isn't a universal constant — it depends on the specific tokenizer a model uses, the language being generated, and even the particular style and vocabulary of the content itself. English text with common vocabulary tokenizes relatively efficiently with most modern tokenizers, commonly landing somewhere around 1.3 tokens per word on average, while text in other languages, highly technical or specialized vocabulary, or content with unusual formatting can shift this ratio meaningfully in either direction — worth keeping in mind as a source of estimation uncertainty rather than treating the default ratio as universally precise.

Once converted to a per-word figure, the resulting AI content generation cost is, for the vast majority of current LLM pricing, dramatically lower than traditional human-written content pricing — often by two or three orders of magnitude for straightforward generation tasks. This stark gap is exactly why AI content generation has become such an actively pursued option across content-heavy industries, and why a genuine cost comparison, done explicitly rather than left as a vague impression, tends to make an unambiguously strong case for at least exploring AI generation for suitable content categories.

The honest caveat this pure cost comparison doesn't capture is quality and editing effort, which genuinely differ between AI-generated and professionally human-written content in ways that matter for many real publishing use cases. AI-generated content, even from a strong current model, commonly benefits from human editing and fact-checking before publication — a real, additional cost beyond the raw generation cost this calculator isolates. Professionally commissioned human-written content, by contrast, is typically delivered closer to publication-ready, reflecting a meaningfully different editing-effort profile even at its higher raw per-word price. A genuinely complete cost comparison for a real content strategy decision should account for this full pipeline cost — generation plus necessary editing and review — rather than comparing only the raw generation cost against a fully-finished human-written price, since that's not quite an apples-to-apples comparison on its own.

For content categories where AI-generated output genuinely needs comparatively little editing to be publication-ready — internal documentation, straightforward summaries, first-draft outlines — the pure cost gap this calculator surfaces represents close to the real total savings available. For content categories with a higher quality bar requiring substantial human editorial involvement regardless of the AI-versus-human starting point, the true savings are smaller than the raw per-word gap suggests, though very often still meaningful once the full pipeline cost is honestly accounted for.