Calculator
Financial Document Token Estimator
Financial document token estimator: price 10-K, 10-Q, 8-K and earnings call runs across 10 frontier LLMs. Context-fit + one-pass + peer synthesis cost.
Transparent by design — computed in your browser from a published formula and sourced rates, not a black box. Data verified May 25, 2026. Sources: Anthropic pricing ↗ · OpenAI pricing ↗ · Google AI / Gemini pricing ↗ Full methodology →
- Inputs
- Form inputs / CSV
- Runtime
- Instant
- Privacy
- Client-side · no upload
- API key
- Not required
- Methodology
- Open →
1 · Configure the document
Business + MD&A + risk factors body, excluding exhibits.
One-pass run
18.0K
input tokens · cheapest at $0.0017 on Gemini 2.5 Flash-Lite · 163.6× spread to priciest
Priciest: $0.282 on Claude Opus 4.1 (retired, prior generation) · Output: 1.5K tok
2 · Cost per model
| Model | Input tokens | Output tokens | Cache-read | One-pass cost | Synthesis cost | Context | Fits |
|---|---|---|---|---|---|---|---|
| Gemini 2.5 Flash-Litegoogle | 18.0K | 1.5K | 9.0K | $0.0017 | — | 1M | ✓ |
| Gemini 2.5 Flashgoogle | 18.0K | 1.5K | 9.0K | $0.0071 | — | 1M | ✓ |
| GPT-5.4 miniopenai | 18.0K | 1.5K | 9.0K | $0.017 | — | 256K | ✓ |
| Claude Haiku 4.5anthropic | 20.6K | 1.5K | 10.3K | $0.019 | — | 200K | ✓ |
| Gemini 2.5 Progoogle | 18.0K | 1.5K | 9.0K | $0.029 | — | 2M | ✓ |
| Gemini 3.5 Flashgoogle | 18.0K | 1.5K | 9.0K | $0.030 | — | 1M | ✓ |
| Claude Sonnet 4.6anthropic | 20.6K | 1.5K | 10.3K | $0.056 | — | 1M | ✓ |
| Claude Opus 4.8anthropic | 20.6K | 1.5K | 10.3K | $0.094 | — | 1M | ✓ |
| GPT-5.5openai | 18.0K | 1.5K | 9.0K | $0.113 | — | 400K | ✓ |
| Claude Opus 4.1 (retired, prior generation)anthropic | 20.6K | 1.5K | 10.3K | $0.282 | — | 200K | ✓ |
Sorted cheapest first on one-pass cost. Input-token count differs slightly per provider because each tokenizer has a different char-per-token ratio.
Approximation notes
Tokenization varies per model. Estimates use published char-per-token ratios from vendor docs (Anthropic ~3.5, OpenAI ~4.0, Gemini ~4.0). For precise counts, use tiktoken (OpenAI) or Anthropic’s count_tokens endpoint. Pricing last verified 2026-04-23.
See methodology for the full rate table, formulas, and archetype assumptions.
How to use
Step-by-step
- 1
Pick document type (10-K, 10-Q, 8-K, proxy, earnings transcript, annual report) or upload a sample.
- 2
Provide page count if you don't have the document in hand.
- 3
Read the token estimate (within ±10% for the supported types).
- 4
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 by per-token pricing for cost estimation. For batch processing across many documents, pair with the Token Cost Optimizer.
For agents
Use in an agent
Same math, same result shape as the UI above — as a static ES module. No HTTP request, no auth, no rate limit.
import { compute } from "https://aifinhub.io/engines/financial-document-token-estimator.js";Contract: /contracts/financial-document-token-estimator.json Full agent guide →
Glossary references
Terms used by this tool
Questions people ask next
FAQ
What documents does it support?
10-K, 10-Q, 8-K (US SEC), proxy statement (DEF 14A), annual report (international), earnings call transcript, and quarterly report PDF. Each has a different typical token count and structure — the tool estimates from document type plus actual page count.
How accurate is the estimate?
Within ±10% for the supported document types. Variance comes from how dense the document is — heavy-table documents tokenize differently than narrative-heavy ones. The methodology page shows accuracy benchmarks per document type.
Why does token count matter for finance use?
Two reasons: (1) cost — input tokens drive LLM API cost, so you need to estimate before deciding to process the doc, (2) feasibility — long-context models cap at 100-200K tokens; documents longer than that need chunking. The tool flags documents that won't fit in the chosen model's context.
How do I estimate without uploading?
Provide page count and document type. The tool uses calibrated tokens-per-page averages: 10-K ~700 tokens/page (dense), earnings call ~500 tokens/page (transcript), proxy ~600 tokens/page. Within ±15% accuracy without seeing the document.
Does it work for non-English filings?
Tokenization rates change by language: 1 token ≈ 4 chars in English, ≈ 2-3 chars in CJK, ≈ 5-6 chars in agglutinative languages (Finnish, Hungarian). The tool defaults to English and lets you override. Specific language calibrations are on the methodology page.
Related deep dive
All articles →Read further
Long-form context behind the tool output.
- Pillar · Guide·14 min
Reading Financial Filings With LLMs: 2026 Playbook
A map of eight filing tasks — extraction, summarization, peer comparison, Q&A, classification, sentiment, forecasting input, compliance — with model.
Read - Methodology · Opinion·12 min
Fine-Tuning vs RAG vs Long-Context for Filings
Decision matrix for finance LLMs: when RAG wins, when long-context wins, and when fine-tuning makes sense. Cost math from published 2026-04 vendor rates.
Read - Methodology · Opinion·11 min
Prompt Caching Economics for Finance
How Anthropic, OpenAI, and Gemini prompt caching works on finance workloads — 5-minute TTL, hit-rate patterns, and 50-90% input savings at the right design.
Read
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