Gemini Embedding 2 vs text-embedding-3-large
Which one to choose
Pick Gemini Embedding 2 when you want task instructions written in the prompt, vector sizes adjustable across a wide range, and Google's design for putting text, images, audio, video and PDFs in one space as you grow beyond text. Pick OpenAI's text-embedding-3-large for a proven text embedding with shortenable vectors across search, clustering, recommendations and classification. For text-only indexes, test both on your own data.
Pricing comparison
| Gemini Embedding 2 | text-embedding-3-large | |
|---|---|---|
| Model maker | OpenAI | |
| Delivery availability | Available | Available |
| Context window | 8K | 8.2K |
| Max output | - | - |
| Official price | Input$0.20per 1M tokensOutput$0.00per 1M tokens | Input$0.13per 1M tokensOutput$0.00per 1M tokens |
| TokenLab price | — | Input$0.091per 1M tokensOutput$0.00per 1M tokens |
| Model performance | Collecting data | Collecting data |
| Capabilities |
Choose Gemini Embedding 2 when
- You want to say in plain words what each vector is for
- You may later add image or PDF retrieval to the same index
- You prefer vectors that are normalized for you after truncation
Choose text-embedding-3-large when
- Your pipeline is text only and relies on established OpenAI tooling
- You use the same vectors for clustering, anomaly detection and classification
- You want a widely documented model with a clear sibling for smaller indexes
How they differ
| Aspect | Gemini Embedding 2 | text-embedding-3-large |
|---|---|---|
| Task handling | Instructions go in the prompt, replacing the older task type parameter. | The task is not declared; one vector serves search, clustering and classification. |
| Input types | Google designs it to embed text, images, audio, video and PDFs in one space. | Text only, so each request carries plain text inputs and no images or audio. |
| Vector size | Adjustable across a wide range, with truncated vectors normalized automatically. | Shortenable dimensions, with normalization left to you where needed. |
| Batching | Several inputs in one request combine into a single embedding unless wrapped separately. | Each input gets its own vector in a batch. |
| Index migration | Not comparable with gemini-embedding-001 vectors. | Not comparable with other OpenAI or third-party vectors. |
Summary
- Gemini Embedding 2: Input $0.10 / Output $0.00 per 1M tokens; text-embedding-3-large: Input $0.091 / Output $0.00 per 1M tokens. Rates depend on the billing unit, specification, and usage. Compare matching conditions in the model's detailed pricing; a single rate does not determine the total cost.
FAQ
Which is better for semantic search, Gemini Embedding 2 or text-embedding-3-large?
For text-only search both are viable and your own query set should decide. Gemini Embedding 2 is the better pick if you expect to search images, audio or PDFs later; text-embedding-3-large is the simpler pick for plain text.
Can I use them in the same vector store?
No. Their vectors live in different spaces, so keep separate indexes or re-embed everything with one model, and merge results only at the ranking step.
Do I need to change my code to switch?
Yes, a little. Gemini Embedding 2 expects task instructions in the prompt text and handles multi-input requests differently, while text-embedding-3-large takes plain inputs and an optional dimension value.
Which is cheaper, Gemini Embedding 2 or text-embedding-3-large?
Gemini Embedding 2: Input $0.10 / Output $0.00 per 1M tokens; text-embedding-3-large: Input $0.091 / Output $0.00 per 1M tokens. Rates depend on the billing unit, specification, and usage. Compare matching conditions in the model's detailed pricing; a single rate does not determine the total cost.
What are the key differences between Gemini Embedding 2 and text-embedding-3-large?
Both models share similar capabilities.
Sources
Reviewed Oct 2, 2026