Compute the cost to embed 1B tokens with text-embedding-3-large via batch API

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You're embedding 1 billion tokens of corpus with OpenAI text-embedding-3-large (sync price: $0.13 per 1M tokens) via the batch API, which gives a 50% discount. Compute the total cost in USD.

Quick reference

TL;DR

$65. 1B tokens at $0.13 per million is $130 sync; the batch API halves it to $65. Still cheap relative to storage and the cost of repeat re-embeddings.

Memory aid

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Easy to grasp

Imagine buying concert tickets in bulk. At the box office window they cost thirteen cents each, but if you mail in your order and let them ship by tomorrow, the price drops to six and a half cents each. You need one thousand tickets. At the cheaper rate that comes to sixty-five dollars total. That is exactly the cost of converting a billion pieces of text into the premium catalog cards using the overnight lane. The bill looks tiny, almost laughable next to a real engineering budget. The expensive part shows up later, when the catalog system gets updated and every single card has to be reprinted from scratch in the new format. That reprint, repeated across years, is where the real money goes, not the original sixty-five dollars.

Key concepts

Embeddings

Detailed answer & concept explanation

~4 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

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Quick scan · 2–4 min

Everything important, quickly.

2 min: arithmetic walkthrough + sync vs batch pricing + 6.5x cost ratio + Matryoshka truncation + re-embedding cycle multiplier.

Real-world examples

Real products, models, and research that use this idea.

OpenAI's 2026 pricing page lists text-embedding-3-large at $0.13 per million tokens sync and $0.065 batch.
Production RAG teams routinely migrate from text-embedding-3-small to text-embedding-3-large via parallel-index migration, paying the one-time batch bill in exchange for the recall lift.
Matryoshka truncation of text-embedding-3-large to 1536 dim is the default 2026 pattern for teams that want most of the quality lift without doubling storage.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QWhy is text-embedding-3-large priced 6.5x higher than text-embedding-3-small?

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QWhen does Matryoshka truncation outperform the smaller model?

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QHow would the cost change if you migrated re-embedded the corpus twice?

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Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Forgetting to apply the batch discount, or applying it to the wrong base price; the sync number is $130, batch is $65.

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.