How to use Financial Document Token Estimator
Paste a 10-K, 10-Q, 8-K, or earnings transcript. The page reports token count and one-pass extraction cost across eight frontier LLMs, with cache-hit toggle so you can see real-world economics, not list price.
What It Does
Use the calculator with intent
Paste a 10-K, 10-Q, 8-K, or earnings transcript. The page reports token count and one-pass extraction cost across eight frontier LLMs, with cache-hit toggle so you can see real-world economics, not list price.
Engineers building filing-analysis pipelines who need to size the model bill before committing the architecture to one provider.
Interpreting Results
Token count is the underlying cost driver. Toggle cache-hit on for realistic recurring-extraction economics. Per-document cost x docs-per-month gives you the workload bill.
Input Steps
Field by field
- 1
Pick option
Pick document type (10-K, 10-Q, 8-K, proxy, earnings transcript, annual report) or upload a sample.
- 2
Provide
Provide page count if you don't have the document in hand.
- 3
Read outputs
Read the token estimate (within ±10% for the supported types).
- 4
Check assumptions
Check whether the estimate exceeds your model's context window. Documents that don't fit need chunking — see the SEC Filing Chunk Optimizer.
- 5
Multiply
Multiply by per-token pricing for cost estimation. For batch processing across many documents, pair with the Token Cost Optimizer.
Common Scenarios
Use realistic starting points
10-K archive analysis
Doc type
10-K
Volume
500 docs/quarter
Token count typically 50–200k. Sonnet is the typical sweet spot — Opus rarely justifies the cost step on filing analysis.
Earnings-day batch
Doc type
Earnings transcript
Volume
200 docs/day
Cache-hit toggle changes the bill dramatically — recurring extraction of the same boilerplate sections benefits most.
Try These Tools
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Token-Cost Optimizer
Compute the dollar cost of a trading research loop across Claude, GPT, and Gemini. Prompt length × model × retry × call volume → cost per idea and per.
FAQ
Questions people ask next
The short answers readers usually want after the first pass.
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