Match the Model to the Task: A Genealogist’s Guide to Saving Time and Tokens

I tokenmaxxed today. I was working with frontier model ChatGPT's Astra on Max level to create a map. The third generation of the map was what I wanted. I asked Astra two more questions and hit my token limit. The questions were simple ones so I should have dropped to a lower model and strength to preserve tokens. Live and learn. The point is that waste comes from paying frontier prices for simple work; or from redoing cheap work that failed. So Claude's Fable 5.1 Medium compiled this article with this human's input and review to help genealogists figure out how to conserve tokens.

Every major AI company now sells a ladder of models. At the top are frontier models such as Claude Fable 5.1 and GPT-6 Astra. At the bottom are small, fast models such as Claude Haiku 5.5 and GPT-6 Luna. The top of the ladder costs up to 100 times more per token than the bottom. The bottom makes more mistakes. Knowing which rung fits which genealogical task saves both money and hours of rework.

The current ladder

Prices are per million tokens, input and output, as of October 12, 2026.

Tier	Model	Input	Output
Frontier	Claude Fable 5.1	$10.00	$50.00
Frontier	GPT-6 Astra	$10.00	$50.00
Upper middle	Claude Opus 5.5	$4.00	$20.00
Middle	Claude Sonnet 5.5	$2.00	$10.00
Middle	GPT-6.1 Sol	$2.00	$10.00
Middle	Gemini 3.1 Pro	$2.00	$12.00
Middle	Gemini 3.8 Flash	$0.75	$3.75
Economy	Claude Haiku 5.5	$0.10	$0.50
Economy	GPT-6 Luna	$0.10	$0.50

Sources for the table are the three companies' price lists. [1][2][3] The Gemini 3.8 Flash price is promotional and doubles on January 1, 2027. [3] Google announced Gemini 4 Argon on September 30 at an introductory $2 and $10, but it was limited to testers and top-tier subscribers at announcement and is not yet on Google's public price list. [3][4]

Where the tokens go

Most genealogists pay a flat subscription, so tokens show up as usage limits instead of a bill. Either way, three things drain them.

The first is price. A frontier model burns through an allowance far faster than a middle model doing the same job.

The second is thinking. Reasoning tokens are billed but never shown. First Page Sage found they made up 22.4% of billed tokens across its 14-task study and were the largest hidden cost. [7] Effort settings matter here: at maximum effort, Claude Opus 5.5 used about 119,000 output tokens per benchmark task, Fable 5.1 about 78,000, and GPT-6 Astra about 27,000. [5]

The third is failure. Cheap models are not always frugal. GPT-6 Luna used about 51,000 output tokens per task against 31,000 for the larger GPT-6 Sol. [6] In the First Page Sage content test, the two cheapest models had 45% and 48% of their drafts thrown away, against 9% for the best model. [7] A discarded answer costs tokens and your time.

Transcribing English handwriting: middle tier, thinking turned down

This is the best-tested genealogy task, and the evidence says not to pay for the top rung. Mark Humphries measured Gemini 3 Pro at a 1.67% character error rate on 18th and 19th century English documents, with the best results at the lowest thinking setting and a cost of about one cent per page. [8] Nicole Elder Dyer found Gemini 3 Flash slightly better than Pro, and on one North Carolina deed it scored 1.22% against 5.61% for Transkribus. She advises turning extended thinking off, because reasoning modes tend to "correct" old spellings. [10] A Dutch test found that limiting the Flash model's reasoning budget raised its score from 47% to 67%. [11]

For transcription, then, extra thinking costs more and can make the result worse. Using Humphries's token counts, my rough estimate is a third of a cent per page at Gemini 3.8 Flash prices and about four cents at frontier prices, before any reasoning tokens. One caution: these tests used earlier Gemini versions, and I found no published handwriting test of Gemini 3.8 Flash. Claude Opus 5.5 can no longer run with thinking switched off, so set its effort to low instead. [19]

Checking a transcription: a second middle model

Spend the savings on verification. Humphries had two other models compare their readings with the first. They flagged 374 disagreements, 106 of them real errors, and a human had to review only about 4% of the text. [9] Names, dates, and places remain the weakest spots, so check each one against the image. [10][14]

Difficult hands and other languages: step up, and still verify

On the University of Basel's benchmark, updated October 6, the top scores for 15th century German manuscripts were Claude Fable 5 at 88.4 and GPT-6 Astra at 87.1. Claude Sonnet 5.5, at one fifth the price, scored 85.9. [12] A 29-language benchmark found the best models still had handwritten character error rates near 30%. [13] Start in the middle, move to a frontier model for pages that defeat it, and use a specialized tool such as Transkribus alongside. [10]

Tables, registers, and index cards: middle tier

Extraction from structured records does not reward the most expensive models. On the Basel benchmark, Gemini 3.8 Flash led on meeting minutes at 95.5, and Claude Sonnet 5.5 led on library cards at 91.4. [12] In the Dutch test, Gemini read a 23-column register in one pass. [11] Dyer notes that censuses and tax lists are still harder than prose. [10]

Economy tier: text in, text out

Use the cheapest models for reformatting a clean transcription, sorting a list, or summarizing typed text. Do not use them to read images. Claude Haiku 4.5 scored 19% in the Dutch handwriting test, against 75% for the best model. [11] Haiku 5.5 and GPT-6 Luna have no published handwriting results.

Writing: middle to upper middle

The First Page Sage data covers the previous generation, but the pattern is useful: Claude Opus 5 and Sonnet 5 needed the fewest revisions, and Sonnet 5 finished cheaper than a model with a lower first-draft price. [7] Denyse Allen reports the same preference for Claude in family narratives. [16] Their successors, Opus 5.5 and Sonnet 5.5, are the reasonable picks.

Evidence analysis and proof arguments: where frontier models earn their price

Correlating conflicting records across a large file is the task where a wrong answer costs the most. Opus 5.5 currently holds the highest Artificial Analysis Intelligence Index score at 58, and Fable 5.1 and Astra sit in the same class. [5] No published benchmark tests genealogical reasoning, so this recommendation rests on general measures. A sensible rule from developer practice applies: start with the cheapest model likely to succeed, and when its answer fails your check, move up a tier instead of retrying. [15]

Finding records: not a chatbot task

Chat models do not search archives. FamilySearch Full-Text Search covers nearly 2 billion records in English, Spanish, and Portuguese. [17]

Quick reference

Task	Tier to use	Effort setting
English handwriting	Middle (Gemini Flash or Pro)	Low or off
Checking a transcription	Second middle model	Low
Hard hands, other languages	Middle, then frontier	Medium
Tables and registers	Middle	Low to medium
Reformatting clean text	Economy	Low
Narrative writing	Middle to upper middle	Medium
Evidence analysis	Frontier	High

Whatever the tier, the Coalition for Responsible AI in Genealogy asks us to verify AI output against the records and to disclose its use. [18]

Sources

1. Anthropic, "Pricing," Claude Platform Docs, accessed October 12, 2026. https://platform.claude.com/docs/en/about-claude/pricing
2. OpenAI, "Pricing," API documentation, accessed October 12, 2026. https://developers.openai.com/api/docs/pricing
3. Google, "Gemini Developer API pricing," accessed October 12, 2026. https://ai.google.dev/gemini-api/docs/pricing
4. "Google announces Gemini 4 Argon as its new frontier model," 9to5Google, September 30, 2026. https://9to5google.com/2026/09/30/gemini-4-argon-announcement/
5. Artificial Analysis, analysis of Claude Opus 5.5, September 22, 2026. https://artificialanalysis.ai/articles/claude-opus-5-5
6. Artificial Analysis, analysis of GPT-6 Sol and Luna, September 22, 2026. https://artificialanalysis.ai/articles/gpt-6-sol-and-luna-push-the-cost-efficiency-frontier
7. Evan Bailyn, "AI Token Usage Cost by Model: OpenAI vs. Anthropic vs. Google," First Page Sage, September 9, 2026. https://firstpagesage.com/seo-blog/ai-token-usage-cost-by-model/
8. Mark Humphries, "Gemini 3 Solves Handwriting Recognition and it's a Bitter Lesson," Generative History, November 25, 2025. https://generativehistory.substack.com/p/gemini-3-solves-handwriting-recognition
9. Mark Humphries, "When Models Disagree...Transcription Accuracy Improves Significantly," Generative History, May 27, 2026. https://generativehistory.substack.com/p/when-models-disagree-transcription
10. Nicole Elder Dyer, "AI-Powered Transcription of Handwritten Documents," RootsTech 2026 syllabus. https://cms-z-assets.familysearch.org/3e/69/1106b1c7ad61708fa9c63d714652/syllabus-for-ai-powered-transcription-of-handwritten-documents.pdf
11. Lieuwe Jongsma, "Can a general LLM really just read handwriting?" Neerlandistiek, June 21, 2026. https://neerlandistiek.nl/2026/06/can-a-general-llm-really-just-read-handwriting/
12. University of Basel, RISE Humanities Data Benchmark, version 0.6.1, updated October 6, 2026. https://rise-services.rise.unibas.ch/benchmarks/p/benchmark-results/
13. Mélodie Boillet, Solène Tarride, and Christopher Kermorvant, "METATR: A Multilingual, Evolving Benchmark for Automatic Text Recognition," arXiv, May 26, 2026. https://arxiv.org/abs/2605.26712
14. Steve Little, "When the Machine Finally Learned to Read: Gemini 3 and the Question of 'Good Enough,'" AI Genealogy Insights, December 16, 2025. https://aigenealogyinsights.com/2025/12/16/when-the-machine-finally-learned-to-read-gemini-3-and-the-question-of-good-enough/
15. Satvik Paramkusam, "AI Model Routing in 2026: When to Use Fable, Astra, Gemini or Muse," Build Fast with AI, September 9, 2026. https://blog.buildfastwithai.com/ai-model-routing-2026-fable-astra-gemini-muse
16. Denyse Allen, "My practical AI toolkit for family history for 2026," Chronicle Makers, December 3, 2025. https://denyseallen.substack.com/p/practical-ai-toolkit-genealogy-2026-chatgpt-claude-gemini-perplexity
17. FamilySearch, "Full-Text Search: A Powerful Tool for Making Genealogy Discoveries," October 18, 2025. https://www.familysearch.org/en/blog/what-is-full-text-search
18. Coalition for Responsible AI in Genealogy, "Guiding Principles for Responsible AI in Genealogy," adopted by the National Genealogical Society, May 2025. https://www.ngsgenealogy.org/wp-content/uploads/2026/01/Responsible-Use-of-AI.pdf
19. Anthropic, "Claude Opus 5.5," September 22, 2026. https://www.anthropic.com/claude-opus-5-5