Model comparison
Compare text-embedding-3-large and Voyage Context 4 using the same provider-sourced embeddings & vector search rubric. No mystery score and no invented benchmark ranking.
Set your usage. Your estimate updates as you type.
Assumes 600 tokens per page, processed separately. Actual token counts vary. This covers embedding only, not storage, search, or generated answers.
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Estimated total (USD) |
|---|---|---|
| Voyage Context 4Voyage AI | Voyage AI | $0.0007 |
| text-embedding-3-largeOpenAI | OpenAI | $0.0008 |
Quick take
OpenAI's highest-capability text embedding model for semantic search, recommendations, clustering, and retrieval pipelines.
The model embeds text only and does not chunk long documents for you. Your ingestion pipeline still needs a deliberate chunking, metadata, evaluation, and re-indexing strategy.
Voyage AI's document-aware embedding model that automatically creates vectors for chunks while preserving information from the surrounding document.
Automatic chunking is convenient but less application-controlled than a custom parser. Test headings, tables, citations, metadata, and update behavior before adopting it across a document estate.
Compare the published facts
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Embeddings & vector search | text-embedding-3-large | Voyage Context 4 |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | $0.13 / 1M tokens | $0.12 / 1M tokens after 200M free |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 8,191 input tokens | 32K per internal pass; longer documents split automatically |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 3,072 default; shorter vectors via dimensions | 256, 512, 1,024, or 2,048 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text | Whole text documents |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Optional dimensions parameter; chunking handled by the application | Automatic contextual chunking, overlap, and long-document splitting |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | OpenAI hosted Embeddings API | Voyage hosted API and MongoDB-integrated routes |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
OpenAI says business and API data is not used to train its models by default. Abuse-monitoring retention and eligible zero-data-retention controls depend on the endpoint and organization approval.
Provider and API links
Voyage's public terms allow customer content to improve services unless the customer opts out. Eligible paid organizations can configure an opt-out and zero-day retention; separately negotiated enterprise terms may differ.
Provider and API links
Frequently asked questions
text-embedding-3-large: OpenAI's highest-capability text embedding model for semantic search, recommendations, clustering, and retrieval pipelines. Voyage Context 4: Voyage AI's document-aware embedding model that automatically creates vectors for chunks while preserving information from the surrounding document.
Consider text-embedding-3-large when your priority is High-quality text search and RAG. Consider Voyage Context 4 when your priority is Long-document RAG. Test both with your own data and provider route before committing.
No. This comparison aligns provider-published facts for the Embeddings & vector search category. It does not claim a universal winner or combine incompatible third-party benchmark scores.
