Model detail
text-embedding-3-large
text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.
Model specs
- Context length
- 8.191K
- Max output
- –
- I/O modalities
- Text
- Released
- 2025-10
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API endpoints
One MixRoute gateway, OpenAI-compatible
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OpenAI-compatible
/v1/chat/completionsPOST
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Pricing
Flat rate, unit: /1M Tokens
Input
$0.1300 /1M Tokens
Completion
$0.1300 /1M Tokens
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Selection summary
Quickly judge whether it fits your workload
What is this model good for?
Most capable embedding model for search and similarity
Use text-embedding-3-large for semantic search, clustering, and recommendations across both English and non-English content. It produces high-dimensional embeddings for measuring text relatedness.
What should you check before using it?
Confirm embedding dimensions and pricing
text-embedding-3-large supports configurable embedding dimensions. Choose the dimension that balances accuracy and storage cost for your use case. Check the per-token pricing.
Why use it through MixRoute?
Use the compatible endpoint with stable model ID
Use the confirmed compatible endpoint with model ID text-embedding-3-large through MixRoute.
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Token cost estimator
Live estimate from this page's pricing, not an actual bill
This model has no cache-read price; caching is not counted
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FAQ
text-embedding-3-large is OpenAI’s most capable embedding model for both English and non-English tasks. Embeddings are numerical representations of text used to measure relatedness between pieces of text.
One endpoint, a testable decision
Test this model and alternate routes with the same request format
Start from a real workload, then let quality, total cost, and failure conditions decide whether to send production traffic.