Financial Document Token Estimator

Four archetype presets — 10-K, 10-Q, 8-K, and earnings call transcript — or you can paste raw text (up to about 50,000 characters) to estimate directly. There is no file upload and no page-count input: pick an archetype for a representative estimate, or paste the actual text for a precise character-based count.

The paste-text path is exact for the characters you provide — it counts characters and divides by the model's tokenizer ratio. The archetype presets are representative mid-range samples, and real filings of the same type span an order of magnitude (a large-cap 10-K with full exhibits can exceed 40,000 tokens), so treat a preset number as a starting estimate, not a guarantee.

Two reasons: (1) cost — input tokens drive LLM API cost, so you estimate before deciding to process the doc; (2) feasibility — context windows range from about 200K to 2M tokens across the models shown, and the Fits column flags, per model, whether a document overflows. Documents that don't fit need chunking.

Pick the closest archetype (10-K, 10-Q, 8-K, or earnings call). Each preset carries a representative token count baked in from typical filings — the 10-K preset is around 18,000 tokens. There is no tokens-per-page or page-count path; for a precise number, paste the actual text and the tool counts characters divided by the model's tokenizer ratio.

The character-to-token ratios assume English prose (about 3.5–4 characters per token). Other languages tokenize differently — CJK text is denser, agglutinative languages sparser — so estimates for non-English text are rougher. There is no language selector: the ratio is fixed per model.