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.
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.
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.
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.
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.
", "aboutText": "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.\n\n### Encoder and representation\n\nThe 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.\n\nThe 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.\n\n### Query and passage preparation\n\nThe 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.\n\nA 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.\n\n### Longer source material\n\nThe 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.\n\nKeep 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.\n\n### Family editions and result use\n\nNVIDIA 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.\n\nThe 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.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "692306f1f6e703b6df48fd6d6da7e28a01b9ca2b0c070dff34f0dd1a091ac642", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "nvidia/nemotron-3-embed-1b:freeNVIDIA 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.
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.
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.
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.
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.
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.
POST /v1/embeddingstext, token_idsembedding| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits nvidia/nemotron-3-embed-1b:free | Default— |
Fieldinput | Typestring|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— |
Fieldencoding_format | Typestring | RequirementOptional | Values & limits float | Defaultfloat |
Fielddimensions | Typeinteger | RequirementNot supported | Values & limits Minimum: 1 | Default— |
Fielduser | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldsession_id | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldtrace | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— |
text_to_embedding
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, input |
Parameterinput_shapes | Contractstring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[] |
Parameterencoding_format | Contractfloat |
Parameterdimensions_override | Contractfalse |
Parametermax_input_budget_units | Contract32768 |
Parametermultimodal_objects | Contractfalse |
Parametermaterial_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.
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "nvidia/nemotron-3-embed-1b:free", "input": ["First document", "Second document"]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.embeddings.create(model='nvidia/nemotron-3-embed-1b:free',input=["First document","Second document"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\njson
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.
No embedding job endpoint is introduced.
| Field | Type |
|---|---|
Fieldobject | Typestring |
Fieldmodel | Typestring |
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[] |
Fieldusage | Typeobject |
| 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. |
Ailoot Ultra nvidia/nemotron-3-embed-1b:free
Use the method paths shown above.