Source: https://ailoot.ai/models/en/nvidia-nemotron-3-embed-1b-u003a-free/
Canonical: https://ailoot.ai/models/en/nvidia-nemotron-3-embed-1b-u003a-free/
Language: en

# NVIDIA: Nemotron 3 Embed 1B (free)

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## [NVIDIA: Nemotron 3 Embed 1B (free)](https://ailoot.ai/models/en/nvidia-nemotron-3-embed-1b-u003a-free/)

NVIDIA Nemotron 3 Embed 1B represents multilingual queries and passages as 2048-dimensional vectors for search and RAG. Its documented family supports a 32,768-token sequence and reduced vector sizes with re-normalisation.

- [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `nvidia/nemotron-3-embed-1b:free`](https://ailoot.ai/models/en/nvidia-nemotron-3-embed-1b-u003a-free/#method-0)

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

Nemotron 3 Embed 1B is NVIDIA’s text embedding family for multilingual and cross-language retrieval and semantic similarity. It transforms a question or passage into a dense vector which a search system can compare with other text representations. The manufacturer evaluates the documented 1B releases across thirty-four language and language-variant settings, including English, Arabic, Chinese, Russian, Japanese, Korean and Hinglish.

#### Encoder and representation

The maker’s model card describes a pruned, bidirectional transformer derived from Ministral 3. It has approximately 1.14 billion parameters and averages the token-level representations to obtain one embedding per text. The native output is a 2048-dimensional floating-point vector. This is a retrieval representation rather than a written answer to the question being embedded.

The manufacturer also supports retaining the first part of that representation, for example 1024 or 512 dimensions. The shortened vector must be L2-normalised again after slicing. Keep the selected representation length and normalisation consistent across the query and corpus sides of a search task. A vector’s length and the input sequence length are separate properties.

#### Query and passage preparation

The documented retrieval convention uses “query:” for a question and “passage:” for document text. The producer’s examples apply those roles before encoding and compare normalised vectors with a dot product, equivalent to cosine similarity. Use the preparation expected by the selected workflow instead of mixing a raw corpus representation with a differently prepared query.

A practical collection can contain technical explanations, policies or code-related documentation. A question about how a function handles a particular case should retrieve the source passage or code excerpt that describes that behaviour. Preserve identifiers, headings and relevant conditions with the actual passage text. The vector makes the candidate searchable; the retained source is what a reader or a later RAG answer stage uses.

#### Longer source material

The documented maximum sequence length is 32,768 tokens. Choose passage boundaries deliberately even when the model can accommodate substantial text. A complete section can preserve a definition and its exceptions; a focused excerpt can make a specific answer easier to inspect. Inputs beyond the model window need chunking or truncation, so the omitted tail should not be treated as represented content.

Keep each embedded item linked to its parent document and version. For a report, retain the section title and the explanation around the conclusion the question concerns. For a code search, keep the function name, relevant interface and behaviour beside the excerpt. This provides a concrete source artifact when the retrieval stage returns a candidate.

#### Family editions and result use

NVIDIA documents BF16 and NVFP4 versions of the 1B family. Its quantised model card says those two releases share an embedding space and recommends checking retrieval quality on representative material when changing versions. The declared common representation is a manufacturer statement about that named pair, not a general rule for every model with 2048 output values.

The useful result is a query vector or an indexed source collection with its model, dimension choice and preparation recorded. Return the candidate text with its identifier and examine whether it supplies the required information. The manufacturer explicitly notes that a retrieval model does not guarantee the correct passage for every question; a later reranking or answer stage works with those candidates as its own task.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `nvidia/nemotron-3-embed-1b:free`

[`nvidia/nemotron-3-embed-1b:free`](https://ailoot.ai/models/en/#m=nvidia%2Fnemotron-3-embed-1b%3Afree&route=Ultra&caller=nvidia%2Fnemotron-3-embed-1b%3Afree&fmt=embeddings)

NVIDIA /`nvidia/nemotron-3-embed-1b:free`

### Overview

NVIDIA Nemotron 3 Embed 1B represents multilingual queries and passages as 2048-dimensional vectors for search and RAG. Its documented family supports a 32,768-token sequence and reduced vector sizes with re-normalisation.

Accepted inputs

Text · token\_ids

Results

embedding

### Available in Ailoot · inputs & methods

**Ailoot Ultra · Embeddings** `POST /v1/embeddings`

Input types

`text`, `token_ids`

Output types

`embedding`

Context

32,768 tokens

Output format

json

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits nvidia/nemotron-3-embed-1b:free | Default— |
| Field`input` | Type`string\|string[]\|integer[]\|integer[][]` | RequirementRequired | Values & limits Minimum items: 1 Conditions: Nonempty homogeneous batch. Token IDs are nonnegative integers. Input budget: Maximum: 32,768 · Text unit: UTF8 bytes · Token unit: token-array elements | Default— |
| Field`encoding_format` | Type`string` | RequirementOptional | Values & limits float | Defaultfloat |
| Field`dimensions` | Type`integer` | RequirementNot supported | Values & limits Minimum: 1 | Default— |
| Field`user` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`session_id` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`trace` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— |

Input material combinations

**text\_to\_embedding**

Method parameters and lifecycle

| Parameter | Contract |
| --- | --- |
| Parameter`content_type` | Contractapplication/json |
| Parameter`required` | Contractmodel, input |
| Parameter`input_shapes` | Contractstring, string\[\], nonnegative\_integer\_token\_array, nonnegative\_integer\_token\_array\[\] |
| Parameter`encoding_format` | Contractfloat |
| Parameter`dimensions_override` | Contractfalse |
| Parameter`max_input_budget_units` | Contract32768 |
| Parameter`multimodal_objects` | Contractfalse |
| Parameter`material_types` | Contracttext, token\_ids |

Lifecycle: `synchronous_json`

Keep every vector index and dimension.

Multimodal model name does not admit image objects on this exact path.

### Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode

Standard rate When fast mode is selected

Flex mode

Standard rate When flex mode is selected

Maximum reasoning effort

Standard rate When maximum reasoning effort uses the default adjustment

#### Ailoot rates

#### Prices by configuration

| Configuration | Rate schedule | Charge |
| --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |

Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/)

### Example requests

#### Ailoot Ultra · Embeddings

curlPython SDK

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "nvidia/nemotron-3-embed-1b:free", "input": ["First document", "Second document"]}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))
PY_RESULT
```

```python
from openai import OpenAI
import os

client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.embeddings.create(model='nvidia/nemotron-3-embed-1b:free',input=["First document","Second document"])
print(result)
for item in result.data:print(item.index,item.embedding)
```

### Receive and save results

#### Ailoot Ultra · Embeddings

json

##### Keep all completed results

Keep every complete returned response, file and content part.

Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.

##### If the original response needs checking

No embedding job endpoint is introduced.

Response fields

| Field | Type |
| --- | --- |
| Field`object` | Typestring |
| Field`model` | Typestring |
| Field`data[].index` | Typeinteger |
| Field`data[].embedding` | Typefinite float\[\] |
| Field`usage` | Typeobject |

Errors and next steps

| Response | Condition | Next step |
| --- | --- | --- |
| Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |

### Aliases

#### Model aliases

**Ailoot Ultra** [`nvidia/nemotron-3-embed-1b:free`](https://ailoot.ai/models/en/#m=nvidia%2Fnemotron-3-embed-1b%3Afree&route=Ultra&caller=nvidia%2Fnemotron-3-embed-1b%3Afree&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`nvidia/nemotron-3-embed-1b:free`](https://ailoot.ai/models/en/#m=nvidia%2Fnemotron-3-embed-1b%3Afree&route=Ultra&caller=nvidia%2Fnemotron-3-embed-1b%3Afree&fmt=embeddings)

FAQ · NVIDIA: Nemotron 3 Embed 1B (free)

What inputs and results are available?

`POST /v1/embeddings` · `nvidia/nemotron-3-embed-1b:free`: `text`, `token_ids` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `nvidia/nemotron-3-embed-1b:free`: `model`, `input`, `encoding_format`, `provider`

What limits apply?

`POST /v1/embeddings` · `nvidia/nemotron-3-embed-1b:free`: `ctx`: `32768`; `input.budget.maximum`: `32768` (UTF8 bytes); `input.min_items`: `1`

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