Model comparison
Compare text-embedding-3-large and Voyage 4 Large 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 4 LargeVoyage AI | Voyage AI | $0.0007 |
| text-embedding-3-largeOpenAI | OpenAI | $0.0008 |
Results describe a specific test, language and configuration—not overall intelligence. Missing results do not imply worse quality.
Community-submitted result
Retrieval relevance (0–1; higher is better)
MTEB · 2.1.3 · eng-Latn · test/default
dimensions: 1024 · similarity: cosine · modelMetadata: https://github.com/embeddings-benchmark/results/blob/main/results/voyageai__voyage-4-large/1/model_meta.json
Checked: September 5, 2026
MTEB contributors · FinanceBenchRetrieval0.9288 nDCG@10
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 quality-first general embedding model for text and code retrieval, with adjustable dimensions and a shared family vector space.
The large free-token allowance is an account-level commercial detail, not a permanent zero-cost guarantee. Confirm eligibility, retention settings, and the rate that applies after the allowance.
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 4 Large |
|---|---|---|
| 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 tokens |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 3,072 default; shorter vectors via dimensions | 1,024 default; 256, 512, or 2,048 optional |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text | Text and code |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Optional dimensions parameter; chunking handled by the application | Query/document input types, truncation, output dimension |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | OpenAI hosted Embeddings API | Voyage API, MongoDB Atlas, and selected clouds |
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 4 Large: Voyage AI's quality-first general embedding model for text and code retrieval, with adjustable dimensions and a shared family vector space.
Consider text-embedding-3-large when your priority is High-quality text search and RAG. Consider Voyage 4 Large when your priority is Quality-sensitive text retrieval. 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.
