Source: https://ailoot.ai/models/en/qwen-qwen3-embedding-4b/
Canonical: https://ailoot.ai/models/en/qwen-qwen3-embedding-4b/
Language: en

# Qwen: Qwen3 Embedding 4B

[Markdown](https://ailoot.ai/models/en/qwen-qwen3-embedding-4b/index.md) · [JSON](https://ailoot.ai/models/en/qwen-qwen3-embedding-4b/facts.json)

## [Qwen: Qwen3 Embedding 4B](https://ailoot.ai/models/en/qwen-qwen3-embedding-4b/)

Qwen3-Embedding-4B represents text as semantic vectors for multilingual retrieval, code search, classification and clustering. This 4B model supports a 32K-token sequence length and custom vector dimensions from 32 to 2,560.

- [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `qwen/qwen3-embedding-4b`](https://ailoot.ai/models/en/qwen-qwen3-embedding-4b/#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.

Qwen3-Embedding-4B is the 4B text-embedding edition in Qwen’s embedding and reranking family. Built on the dense Qwen3 foundation model, it converts a text segment into a numerical representation of its meaning. Qwen identifies text retrieval, code retrieval, classification, clustering and bitext mining as application areas. It is useful when an application needs to find related passages, group similar records or compare text across languages. The model’s output is an embedding vector, not a conversational response or a rewritten document.

The exact edition has a 32K-token sequence length and an embedding dimension of up to 2,560. It supports custom output dimensions from 32 to 2,560 through Matryoshka Representation Learning. The chosen vector length affects how the representation is stored and compared, so keep it consistent between query and document vectors in one collection. Qwen3-Embedding-8B has a different model size and a 4,096-dimension maximum; neither that output size nor the 8B model’s identity transfers to this 4B edition. An existing index should retain its own consistent representation choices.

The producer describes support for more than 100 languages, including programming languages, with multilingual, cross-language and code retrieval. An illustrative help-centre search can compare a user’s question with passages written in several languages, then return the original passages that match the topic. A code search can compare a natural-language description with relevant functions and their documentation. Preserve meaningful headings, domain terms and identifiers when preparing text, and keep each represented passage connected to its source so the application can show the matching material.

Qwen3-Embedding-4B is instruction-aware. Qwen recommends giving each query a one-sentence description of the task, tailored to the intended retrieval relationship, and recommends English instructions for multilingual scenarios. The direct example places Instruct: before the task description and Query: before the query text. Its retrieval documents are encoded without that query instruction. A practical original instruction can specify finding documentation passages that explain a reported error or finding source-code excerpts that implement the described behaviour. This directs representation towards the application’s question rather than asking the model to write an answer.

The official examples encode query and document texts, compare their embeddings using cosine similarity and demonstrate pooling the final token’s hidden state followed by normalisation. In that lower-level example, the input is truncated at a chosen 8,192-token setting; the documented model sequence length remains 32K. For long material, decide which passages to represent and ensure that truncation does not silently discard the part the query needs. Store vectors with stable source references, compare the prepared query with the collection and return the associated passages in similarity order.

Qwen’s embedding model uses a dual-encoder approach, whereas its separate reranking models use a cross-encoder to evaluate query-document pairs. The release describes using those modules together: embeddings find initial candidates, and a reranker can then refine their order. Qwen3-Embedding-4B itself supplies the single-text representation. Its vector is not a reranker score, a verified statement or a generated answer. If the final workflow answers questions from retrieved material, the selected source passages remain the evidence passed to that later stage.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `qwen/qwen3-embedding-4b`

[`qwen/qwen3-embedding-4b`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-embedding-4b&route=Ultra&caller=qwen%2Fqwen3-embedding-4b&fmt=embeddings)

Alibaba /`qwen/qwen3-embedding-4b`

### Overview

Qwen3-Embedding-4B represents text as semantic vectors for multilingual retrieval, code search, classification and clustering. This 4B model supports a 32K-token sequence length and custom vector dimensions from 32 to 2,560.

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`

Output format

json

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits qwen/qwen3-embedding-4b | 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

Not specified When fast mode is selected

Flex mode

Not specified When flex mode is selected

Maximum reasoning effort

Not specified 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 **$0.00004** / 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": "qwen/qwen3-embedding-4b", "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='qwen/qwen3-embedding-4b',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** [`qwen/qwen3-embedding-4b`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-embedding-4b&route=Ultra&caller=qwen%2Fqwen3-embedding-4b&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`qwen/qwen3-embedding-4b`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-embedding-4b&route=Ultra&caller=qwen%2Fqwen3-embedding-4b&fmt=embeddings)

FAQ · Qwen: Qwen3 Embedding 4B

What inputs and results are available?

`POST /v1/embeddings` · `qwen/qwen3-embedding-4b`: `text`, `token_ids` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `qwen/qwen3-embedding-4b`: `model`, `input`, `encoding_format`, `provider`

What limits apply?

`POST /v1/embeddings` · `qwen/qwen3-embedding-4b`: `input.budget.maximum`: `32768` (UTF8 bytes); `input.min_items`: `1`

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