Qwen3-Reranker

Highlights

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios.

Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.

Qwen3-Reranker-4B has the following features:

• Model Type: Text Reranking
• Supported Languages: 100+ Languages
• Number of Paramaters: 4B
• Context Length: 32k

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub.

Qwen3 Embedding Series Model list

Note:

• MRL Support indicates whether the embedding model supports custom dimensions for the final embedding.
• Instruction Aware notes whether the embedding or reranking model supports customizing the input instruction according to different tasks.
• Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.

Usage

Using Sentence Transformers

Install Sentence Transformers:

pip install sentence_transformers

from sentence_transformers import CrossEncoder

model = CrossEncoder("Qwen/Qwen3-Reranker-4B")

query = "What is the capital of China?"
documents = [
    "The capital of China is Beijing.",
    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]

pairs = [(query, doc) for doc in documents]
scores = model.predict(pairs)
print(scores)
# [  6.4375 -14.375 ]

rankings = model.rank(query, documents)
print(rankings)
# [{'corpus_id': 0, 'score': 6.4375}, {'corpus_id': 1, 'score': -14.375}]

By default, scores are raw logit differences. To get 0-1 probability scores, pass a Sigmoid activation function:

scores = model.predict([(query, doc) for doc in documents], activation_fn=torch.nn.Sigmoid())

The model uses a default prompt "query" which injects the instruction "Given a web search query, retrieve relevant passages that answer the query" into the chat template. You can provide a custom instruction via the prompts parameter:

model = CrossEncoder(
    "Qwen/Qwen3-Reranker-4B",
    prompts={"classification": "Classify whether the document matches the query topic"},
    default_prompt_name="classification",
)

Using Transformers

With Transformers versions earlier than 4.51.0, you may encounter the following error:

KeyError: 'qwen3'

# Requires transformers>=4.51.0
import torch
from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM

def format_i