Try the ASV interlinear.
AI models are now smart enough to align English Bible translations with the original Hebrew and Greek, so why not make an alignment?
Starting with the English text of the American Standard Version (ASV) Bible from 1901, which is in the public domain, Opus 5, GPT 5.6 Sol, and GPT 6 Astra created an interlinear alignment, matching up the Hebrew and Greek original with the ASV’s modern(ish) English.
Here’s what they produced:
- The interlinear interface, which lets you explore both English-first and original-language-first interfaces. It also lets you see where the ASV departs from the KJV’s underlying text (155 verses).
- An ASV English text tagged to the original languages.
- A Hebrew and Greek text tagged to the ASV English. This text hypothetically reconstructs the eclectic text used by the ASV translators; textual variants that appear to have been chosen by the translators serve as the main text.
- Data and scripts that let you adapt this alignment process to your own text. If nothing else, the hard-won alignment rules, which went through hundreds of revisions over the course of this project and which cover all the tricky situations encountered during it, can serve as the starting point for your own alignment. LLMs love to talk about what they’re doing, and this repo records alignment reasoning down to individual verses.
Maximalist alignment philosophy
Perhaps most controversially, this project adopts a maximalist alignment philosophy, where it tries to align as much as possible, including italicized words that the ASV translators indicated as added. There are 6,250 such italic words in the ASV:
- 3,303 are tagged as “added,” consistent with the ASV translators’ view.
- 2,584 are tagged as part of a phrase with a different headword (the most-important word in a phrase).
- 363 are tagged as headwords. For example, “those lands” in Judges 11:13 is translating the Hebrew pronoun for those. This alignment puts the phrase head on “lands” because it’s the most distinctive part of the phrase.
Process
Each chapter went through a three-step process:
- Align. The AI preps the data and does a first-pass alignment for each verse. It’s provided the alignment rules and the source text.
- Review. The AI looks at each verse and assesses whether the alignment is correct.
- Reconcile. The AI takes a larger view of the chapter, identifying inconsistencies or common themes and deciding whether to create new rules based on what it learned during this chapter.
Each chapter took about 40-60 minutes to run. Psalm 119 (the longest chapter in the Bible) took 90 minutes.
Managing cost
The hardest part of this project was managing token budgets. At first, I was parsing individual verses, which is the best way to achieve isolated alignments and reviews. But this approach exhausted my token quotas too quickly. So I switched instead to handling a full chapter at a time, which seemed to be the best balance of cost and performance.
Using this process, Claude Max or ChatGPT Pro 20x plan can process about 200-300 chapters per week before exhausting quota. ChatGPT is about 50% more efficient than Claude in terms of the number of chapters it can process per week. It took six quota-weeks to process these chapters, which is how I arrived at a $300 cost. (Traditionally, an interlinear would cost $50,000-$100,000 to produce.)
The token list prices are much higher than what I paid:
| Step | List Price |
|---|---|
| Align | $2,300 |
| Review | $1,900 |
| Reconcile | $3,600 |
| Total | $7,800 |
So a 20x plan netted a 96% token discount off list prices.
I spent 100 million input tokens, 120 million output tokens, and 2.1 billion cached input tokens. With cache writes and some additional processing, the total token usage was about 2.5 billion tokens.
I also tried DeepSeek v4 and GLM 5.3, neither of which produced good results. Gemini 3.7 Flash produced fine results on alignment, but its batch API didn’t support structured outputs, which made it useless for this purpose.
Sources
This project worked from several open datasets:
- SBLGNT, which served as the base Greek text. The variant readings in its footnotes supported 340 verses (out of just under 8,000 in the New Testament) where the ASV translators departed from the SBLGNT’s critical text (in part because the critical text is modern, while the ASV dates from 1901).
- MACULA Hebrew and Greek for parsing data.
- OSHB for the WLC text and verse-number differences between the Hebrew and the ASV.
I’m aware of two existing ASV alignments: Logos (2020) and STEP Bible by Wade Masfield (2013). I validated two chapters against the STEP alignment and one chapter against an NASB alignment to confirm that the AI output was sane, but I didn’t consult existing copyrighted alignments beyond these validation chapters.
Surprises
This project used an open human alignment as a gold standard. However, this data only altered two word-level alignments in the entire Bible. It added a lot of overhead to the process, since the AI mostly talked about how it disagreed with the reference because of differences in alignment philosophy. It turned out to be wasted overhead. The English glosses attached to the Hebrew and Greek mitigate this finding somewhat.
Far and away, this finding was the most-surprising part of the whole process for me: models are smart enough to do the alignment on their own, and a gold-standard, existing alignment reference is just a distraction to them, at least for this workflow.