Qwen3 VL Reranker 8B

Summary: Qwen3 VL Reranker 8B is a multimodal reranking model by Alibaba's Qwen team that scores the relevance between a query and a set of candidate documents, supporting both text and images. Supporting over 30 languages and a 32,768-token context window, this model excels as a precision refinement step in two-stage retrieval pipelines, making it ideal for applications such as multimodal search, visual document retrieval, and cross-lingual reranking where high-precision relevance scoring is required.

Intelligence	Speed	Sovereignty	Input	Output
High	Medium	Medium	Text, Image	Score

Central parameters

Description: Multimodal reranking model by Alibaba's Qwen team, scoring query-document relevance from text and image inputs across 30+ languages.

Model identifier: Qwen/Qwen3-VL-Reranker-8B

IONOS CLOUD AI Model Hub Lifecycle and Alternatives

IONOS Launch	End of Life	Alternative	Successor
May 12, 2026	N/A

Origin

Provider	Country	License	Flavor	Release
Qwen (Alibaba)	Community	Apache 2.0	-	January 8, 2026

Technology

Input Length	Parameters	Tensor Type	Multilingual	Further details
32768	8B	bfloat16	Yes	Hugging Face

Image tokenisation: Images are tokenised at 1 token per 32×32 pixel block. Images larger than 1,310,720 pixels are downscaled proportionally before tokenisation. The token cost for a given image is:

tokens = (resized_width / 32) × (resized_height / 32)

For example, a 1296×1936 px image (2,507,616 px) is downscaled to fit within 1,310,720 px, resulting in approximately 1,247 tokens. With a 32,768-token context window and ~200 tokens of query and prompt overhead, a single document can contain approximately 25 images of that resolution.

Modalities

Text	Image	Audio
Input and output	Input	Not supported

Endpoints

Chat Completions	Rerank
Not supported	v1/rerank

Features

Streaming	Reasoning	Tool calling
Not supported	Not supported	Not supported

Usage example

Rerank

The following example demonstrates how to rerank a set of documents by relevance to a query using Qwen3 VL Reranker 8B.

API Endpoint: POST https://openai.inference.de-txl.ionos.com/v1/rerank

Request:

{
  "model": "Qwen/Qwen3-VL-Reranker-8B",
  "query": "What is the capital of France?",
  "documents": [
    "Paris is the capital of France.",
    "London is the capital of England.",
    "Berlin is the capital of Germany."
  ],
  "top_n": 2
}

Response:

{
  "id": "rerank-abc123",
  "object": "list",
  "model": "Qwen/Qwen3-VL-Reranker-8B",
  "results": [
    {
      "index": 0,
      "document": {
        "text": "Paris is the capital of France."
      },
      "relevance_score": 0.9234
    },
    {
      "index": 2,
      "document": {
        "text": "Berlin is the capital of Germany."
      },
      "relevance_score": 0.0456
    }
  ],
  "usage": {
    "prompt_tokens": 65,
    "total_tokens": 65
  }
}

Multimodal rerank

The following example demonstrates how to rerank image-only documents using Qwen3 VL Reranker 8B. The first document is a hosted image URL; the second is a base64-encoded image.

API Endpoint: POST https://openai.inference.de-txl.ionos.com/v1/rerank

Request:

{
  "model": "Qwen/Qwen3-VL-Reranker-8B",
  "query": "Show me the Red Hat and IONOS partnership logo",
  "documents": [
    {
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "https://www.ionos.de/newsroom/wp-content/uploads/2026/01/Red-Hat_IONOS-1024x576.png"
          }
        }
      ]
    },
    {
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "data:image/jpeg;base64,/9j/4AAQSkZJRgAB..."
          }
        }
      ]
    }
  ],
  "top_n": 2
}

Response:

{
  "id": "rerank-def456",
  "object": "list",
  "model": "Qwen/Qwen3-VL-Reranker-8B",
  "results": [
    {
      "index": 0,
      "document" : {
        "text" : null,
        "multi_modal" : [ {
          "image_url" : {
            "url" : "https://www.ionos.de/newsroom/wp-content/uploads/2026/01/Red-Hat_IONOS-1024x576.png"
          },
          "type" : "image_url"
        } ]
      },
      "relevance_score": 0.9187
    },
    {
      "index": 1,
      "document" : {
        "text" : null,
        "multi_modal" : [ {
          "image_url" : {
            "url" : "data:image/jpeg;base64,/9j/4AAQSkZJRgAB..."
          },
          "type" : "image_url"
        } ]
      },
      "relevance_score": 0.0341
    }
  ],
  "usage": {
    "prompt_tokens": 820,
    "total_tokens": 820
  }
}

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