Paraphrase MiniLM L6 V2
Embedding model by Sentence Transformers.
sentence-transformers dense vector embedding model. Transforms natural language text into rich semantic embeddings for search and retrieval.
Context window
Maximum input context length.
512
Maker
Sentence Transformers
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.
512
tokens—
embedding
Pricing
Rates are read live
Pricing for sentence-transformers/paraphrase-MiniLM-L6-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 Paraphrase MiniLM L6 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 Paraphrase MiniLM L6 V2?
sentence-transformers dense vector embedding model. Transforms natural language text into rich semantic embeddings for search and retrieval.
How much does Paraphrase MiniLM L6 V2 cost?
Paraphrase MiniLM L6 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 Paraphrase MiniLM L6 V2?
Paraphrase MiniLM L6 V2 supports a 512-token context window.
Does Paraphrase MiniLM L6 V2 support tool calling and structured outputs?
No. Paraphrase MiniLM L6 V2 does not support tool calling, so it cannot invoke external tools or functions.
What inputs and outputs does Paraphrase MiniLM L6 V2 support?
Paraphrase MiniLM L6 V2 is an embedding model: it takes text as input and returns vector embeddings as output.
How do I use Paraphrase MiniLM L6 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/sentence-transformers/paraphrase-MiniLM-L6-v2".
When was Paraphrase MiniLM L6 V2 released?
Paraphrase MiniLM L6 V2 was released on October 7, 2026.
Where is the Paraphrase MiniLM L6 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.