Model comparison
Compare text-embedding-3-large and Cohere Embed 4 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 |
| Cohere Embed 4Cohere | Cohere | No reviewed rate |
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 · 1.38.43 · eng-Latn · test/default
dimensions: 1536 · similarity: cosine · modelMetadata: https://github.com/embeddings-benchmark/results/blob/main/results/Cohere__Cohere-embed-v4.0/1/model_meta.json
Checked: September 5, 2026
MTEB contributors · FinanceBenchRetrieval0.8833 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.
Cohere's enterprise embedding model for multilingual text, images, and visually rich documents with a 128K context window.
Cohere does not publish one simple hosted token price for Embed 4 on the reviewed pricing page. Ask for the exact SaaS or private-deployment rate before comparing total cost.
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 | Cohere Embed 4 |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | $0.13 / 1M tokens | Hosted unit price not published |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 8,191 input tokens | 128K tokens |
| 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 1,536 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text | Text, images, and mixed-content PDFs |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Optional dimensions parameter; chunking handled by the application | Search query/document, classification, and clustering input types |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | OpenAI hosted Embeddings API | Cohere API, Model Vault, Microsoft Foundry, SageMaker |
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
Cohere enterprise customers can opt out of training; SaaS prompts and generations are generally deleted after 30 days. Approved zero-data-retention accounts and private deployments offer stronger controls.
Frequently asked questions
text-embedding-3-large: OpenAI's highest-capability text embedding model for semantic search, recommendations, clustering, and retrieval pipelines. Cohere Embed 4: Cohere's enterprise embedding model for multilingual text, images, and visually rich documents with a 128K context window.
Consider text-embedding-3-large when your priority is High-quality text search and RAG. Consider Cohere Embed 4 when your priority is Enterprise search over visually rich documents. 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.
