Alibaba

Qwen3-VL Embedding 2B

About

Qwen3-VL Embedding 2B is a 2B open-weight model from Alibaba. Multimodal embedding model for text, image, and video inputs. It accepts text, images, and video. Context window is 8K. License is Apache-2.0. Released January 7, 2026.

Parameters
2B
Published parameter count.
Context window
8K
Tokens of context on a request.
License
Apache-2.0
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.013
Cached input / 1M	$0.013
Output / 1M	$0

Compare

Qwen3-VL Embedding 2B is the only embedding model in the catalog. The other rows take similar inputs and do a different job.

Model	What it does	Price	Parameters	Context
Qwen3-VL Embedding 2B	Multimodal embedding model for text, image, and video inputs.	$0.013 / 1M input	2B	8K
PP-OCRv6	PaddleOCR PP-OCRv6 medium text detection and recognition; scene OCR JSONL on image chat; text returns plain text; document_url fan-out via text.	$0.01 / 1M input	20M	—
Qwen3.8 27B	Reasoning VLM (image + video + text)	$0.35 / 1M input	27B	256K
Gemini 3.5 Flash Lite	Fastest and cheapest Gemini tier. Multimodal chat; emits no reasoning tokens.	$0.30 / 1M input	—	1M

Benchmarks

Published scores for Qwen3-VL Embedding 2B, from the Qwen3-VL-Embedding model card.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	MMEB image: 75.
Cost and performance	Lower listed rate, mid-pack latency

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
embed	An embedding vector

Estimate cost

Estimate. An hour of video is 15 frames a minute at 256 tokens a frame, plus the audio in that request, and 500 output tokens a minute.

Hours of video

$4.49

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.embeddings.create(
    model="qwen/qwen3-vl-embedding-2b",
    input="a blue parrot",
)

print(response.data[0].embedding)