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.
OpenAI
OpenAI
Specifications
Context window, ranked against other embedding models
Insufficient data
No published context window for this model or its peers.
—
—
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
Frequently asked questions
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 Whisper 1 (nrouter.ai/models/whisper-1), o1 (nrouter.ai/models/o1), GPT-5.5 Pro (nrouter.ai/models/gpt-5.5-pro), GPT-4o Mini TTS (nrouter.ai/models/gpt-4o-mini-tts-2025-12-15).
Pricing is live — see above for current rates.