Model comparison
Compare Gemini Embedding 2 and text-embedding-3-large using the same provider-sourced embeddings & vector search rubric. No mystery score and no invented benchmark ranking.
Facts checked September 4, 2026
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) |
|---|---|---|
| text-embedding-3-largeOpenAI | OpenAI | $0.0008 |
| Gemini Embedding 2Google | $0.0012 |
Quick take
Google's multimodal embedding model for placing text, images, video, audio, and PDFs in one searchable vector space.
Media inputs have separate limits and prices, so text-only cost estimates do not describe a multimodal index. Free-tier and paid Gemini API data-use terms also differ.
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.
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 | Gemini Embedding 2 | text-embedding-3-large |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | Text $0.20 / 1M tokens; multimodal rates vary | $0.13 / 1M tokens |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 8,192 text tokens; media has separate limits | 8,191 input tokens |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 128–3,072; 768, 1,536, or 3,072 recommended | 3,072 default; shorter vectors via dimensions |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text, image, video, audio, PDF | Text |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Task types, output dimension, title for retrieval documents | Optional dimensions parameter; chunking handled by the application |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | Gemini Developer API and Google AI Studio | OpenAI hosted Embeddings API |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
Google's Gemini API terms distinguish unpaid and paid services: content from unpaid services may be used to improve products, while paid-service prompts and responses are not used to improve products.
Provider and API links
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
Frequently asked questions
Gemini Embedding 2: Google's multimodal embedding model for placing text, images, video, audio, and PDFs in one searchable vector space. text-embedding-3-large: OpenAI's highest-capability text embedding model for semantic search, recommendations, clustering, and retrieval pipelines.
Consider Gemini Embedding 2 when your priority is Multimodal search across text and media. Consider text-embedding-3-large when your priority is High-quality text search and 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.
