E5 Base V2
Embedding model by Intfloat.
intfloat dense vector embedding model. Transforms natural language text into rich semantic embeddings for search and retrieval.
Context window
Maximum input context length.
512
Maker
Intfloat
Specifications
Context window, ranked against other embedding models
Proportional scale: 0 to 8.2K tokens. Not ranked, no published context window: text-embedding-3-large, text-embedding-3-small.
512
tokens—
embedding
Pricing
Rates are read live
Pricing for intfloat/e5-base-v2 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 E5 Base V2 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.
FAQs
What is E5 Base V2?
intfloat dense vector embedding model. Transforms natural language text into rich semantic embeddings for search and retrieval.
How much does E5 Base V2 cost?
E5 Base V2 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.
What is the context length of E5 Base V2?
E5 Base V2 supports a 512-token context window.
Does E5 Base V2 support tool calling and structured outputs?
No. E5 Base V2 does not support tool calling, so it cannot invoke external tools or functions.
What inputs and outputs does E5 Base V2 support?
E5 Base V2 is an embedding model: it takes text as input and returns vector embeddings as output.
How do I use E5 Base V2 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 "deepinfra/intfloat/e5-base-v2".
When was E5 Base V2 released?
E5 Base V2 was released on October 7, 2026.
Where is the E5 Base V2 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.