Nemotron 3 Embed 1B
unofficialembedding
Nemotron 3 Embed 1B specifications
| Context window | 33K |
|---|---|
| Max output tokens | — |
| Release date | — |
| Input | Text |
| Output | Vector |
Nemotron 3 Embed 1B pricing in rubles
You pay for what you use, per token and per unit. The final request cost is returned in the API response.
| Type | Price |
|---|---|
| Input tokens | 1,09 ₽ per 1M tokens |
Prices are in rubles and may change with exchange rates.
Nemotron 3 Embed 1B API: connection and code examples
The service exposes a single OpenAI-compatible API. Set our base_url and the key from your dashboard.
OpenAI Python SDK
from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.neuralbridge.ru/v1")
result = client.embeddings.create(model="nvidia/nemotron-3-embed-1b-10238", input="Текст для векторизации")
print(len(result.data[0].embedding))cURL
curl https://api.neuralbridge.ru/v1/embeddings \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "nvidia/nemotron-3-embed-1b-10238", "input": "Текст для векторизации"}'API endpoints
POST /v1/embeddingsprimary | Embeddings Representación vectorial del texto para búsqueda, clustering y evaluación de similitud. |
Supported request parameters
| Parameter | Default | Allowed values | Description |
|---|---|---|---|
dimensions | — | — | Dimensión del vector de embedding: vector más corto — menos espacio y búsqueda más rápida, pero similitud más gruesa. |
encoding_format | float | — | Cómo entregar el vector: como un array de números o como una cadena base64. |