A standard 100 pages document (~50,000 words / 66,650 tokens) costs $0.009331 to ingest and $0.009401 to generate a comprehensive executive summary using Yi-Lightning (01.AI).
Quick answer
A 100 pages document is modeled as 50,000 words or 66,650 tokens. With Yi-Lightning (01.AI), ingestion costs $0.009331; a 500-token executive summary brings the modeled total to $0.009401.
Method & trust
The page uses approximately 500 words per page, estimates tokens with the selected model tokenizer, and prices input plus a 500-token summary at the model's listed rates. OCR, tables, and formatting can increase the actual count.
Feed the entire 100 pages document into context to extract fields, entities, or answer queries.
Ingest 100 pages and generate a structured 500-token executive summary with key takeaways.
Translate or reformat the entire 100 pages document into an equivalent length output.
| Model | Provider | Ingestion (100 pages) | Cached Ingestion | Summary Cost | Context Limit |
|---|---|---|---|---|---|
| Yi-Lightning (01.AI) (Current) | 01ai | $0.009331 | $0.002333 | $0.009401 | 16,384 |
| GPT-5.6 Luna | openai | $0.0128 | $0.00128 | $0.0134 | 1,050,000 |
| GPT-5.4 mini | openai | $0.048 | $0.0048 | $0.0503 | 256,000 |
| GPT-5.4 nano | openai | $0.0128 | $0.00128 | $0.0134 | 128,000 |
| GPT-4o mini | openai | $0.0096 | $0.0048 | $0.0099 | 128,000 |
| Claude Haiku 4.5 | anthropic | $0.066 | $0.0066 | $0.0685 | 1,000,000 |
Assuming a standard single-spaced document with ~500 words per page, a 100 pages document contains approximately 50,000 words, which translates to roughly 66,650 tokens using Yi-Lightning (01.AI)'s tokenizer. Dense PDF tables, legal boilerplate, and OCR scans may increase this by 20–30%.
Yi-Lightning (01.AI) has a context window of 16,384 tokens. A 100 pages document consumes only 406.80% of its total window, easily fitting in a single prompt without requiring chunking or vector search.
Ingesting the document and outputting a concise 500-token executive summary costs $0.009401. Using 24-hour async batch API queues drops this cost to $0.004701.
If your workflow repeatedly queries or chats with this same 100 pages document, prompt caching reduces subsequent turn input costs by up to 90% (to $0.002333 per turn instead of $0.009331).