Text Embedding 3 Large
Embedding model by OpenAI.
OpenAI text embedding model producing 3,072-dimensional vector representations across an 8,192-token context window. It scores 64.6 on MTEB and 54.9 on MIRACL for search, clustering, and retrieval systems.
Specifications
Context window, ranked against other embedding models
Proportional scale: 0 to 8.2K tokens. Not ranked, no published context window: text-embedding-3-small.
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embedding
Pricing
Rates are read live
Pricing for text-embedding-3-large is fetched from the live catalogue on load, so it is never served from a cache that could outlive a repricing.
Availability
Not enough data yet
We have not collected enough health probes for this model to publish an uptime or latency figure. A percentage from a handful of samples is fabricated precision, so none is shown until the sample count supports one.
How Text Embedding 3 Large is billed
Billed at the provider's list price, with no per-token markup.
- Exact list price
- Tokens settle at the published rate of the model that served your call.
- Cache discounts passed through
- Provider prompt-caching discounts reach your balance with no added fee.
- Cost on every response
- Each response carries the
x-nr-request-costheader, so you can reconcile spend per request.
More from OpenAI
FAQs
What is Text Embedding 3 Large?
OpenAI text embedding model producing 3,072-dimensional vector representations across an 8,192-token context window. It scores 64.6 on MTEB and 54.9 on MIRACL for search, clustering, and retrieval systems.
How much does Text Embedding 3 Large cost?
Text Embedding 3 Large is billed at the provider's list price on nRouter with no per-token markup — tokens settle at the exact upstream rate. Input and output rates are read live, so see the pricing section above for the current figures.
Does Text Embedding 3 Large support tool calling and structured outputs?
No. Text Embedding 3 Large does not support tool calling, so it cannot invoke external tools or functions.
What inputs and outputs does Text Embedding 3 Large support?
Text Embedding 3 Large is an embedding model: it takes text as input and returns vector embeddings as output.
How do I use Text Embedding 3 Large via nRouter?
Use any OpenAI-compatible client: point it at https://api.nrouter.ai/v1 with your nRouter API key and set the model to "openai/text-embedding-3-large".
When was Text Embedding 3 Large released?
Text Embedding 3 Large was released on January 25, 2024.
Where is the Text Embedding 3 Large documentation?
Start at nrouter.ai/docs. The quick-start guide (nrouter.ai/docs/getting-started/quick-start) covers API keys and your first request, and the API reference (nrouter.ai/docs/api-reference) lists every parameter.
What other embedding models does OpenAI have?
Other OpenAI embedding models on nRouter include GPT-OSS 120B 1 (nrouter.ai/models/gpt-oss-120b-1), GPT-6.1 Sol (nrouter.ai/models/gpt-6.1-sol), GPT-5.2 Pro (nrouter.ai/models/gpt-5.2-pro-2025-12-11), GPT Audio 1.5 (nrouter.ai/models/gpt-audio-1.5).