# Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

arXiv: https://arxiv.org/abs/2601.04720 · HTML: https://mlsanity.com/paper/2601.04720/

## Key facts

- Cited by 230 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 45 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 30 of 37)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [Learned Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding](https://mlsanity.com/paper/2501.13106/) (2025)
- [NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models](https://mlsanity.com/paper/2405.17428/) (2024)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)
- [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://mlsanity.com/paper/2410.05160/) (2024)
- [ColPali: Efficient Document Retrieval with Vision Language Models](https://mlsanity.com/paper/2407.01449/) (2024)
- [E5-V: Universal Embeddings with Multimodal Large Language Models](https://mlsanity.com/paper/2407.12580/) (2024)
- [MMTEB: Massive Multilingual Text Embedding Benchmark](https://mlsanity.com/paper/2502.13595/) (2025)
- [VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents](https://mlsanity.com/paper/2507.04590/) (2025)
- [Gemini Embedding: Generalizable Embeddings from Gemini](https://mlsanity.com/paper/2503.07891/) (2025)
- [MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval](https://mlsanity.com/paper/2412.14475/) (2024)
- [Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks](https://mlsanity.com/paper/2511.07025/) (2025)

## Built on by (most cited first, 30 of 230)

- [Cosmos 3: Omnimodal World Models for Physical AI](https://mlsanity.com/paper/2606.02800/) (2026)
- [According to Me: Long-Term Personalized Referential Memory QA](https://mlsanity.com/paper/2603.01990/) (2026)
- [GEditBench v2: A Human-Aligned Benchmark for General Image Editing](https://mlsanity.com/paper/2603.28547/) (2026)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report](https://mlsanity.com/paper/2608.24053/) (2026)
- [CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding](https://mlsanity.com/paper/2601.21262/) (2026)
- [TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generation](https://mlsanity.com/paper/2602.07595/) (2026)
- [IPIBench: Evaluating Interactive Proactive Intelligence of MLLMs under Continuous Streams](https://mlsanity.com/paper/2605.27074/) (2026)
- [DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories](https://mlsanity.com/paper/2602.10809/) (2026)
- [TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings](https://mlsanity.com/paper/2603.04772/) (2026)
- [MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control](https://mlsanity.com/paper/2604.06156/) (2026)
- [OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence](https://mlsanity.com/paper/2604.07296/) (2026)
- [VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph](https://mlsanity.com/paper/2602.12735/) (2026)
- [OmniScience: A Large-scale Multi-modal Dataset for Scientific Image Understanding](https://mlsanity.com/paper/2602.13758/) (2026)
- [AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.24134/) (2026)
- [Small Vision-Language Models are Smart Compressors for Long Video Understanding](https://mlsanity.com/paper/2604.08120/) (2026)
- [Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation](https://mlsanity.com/paper/2604.19141/) (2026)
- [ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval](https://mlsanity.com/paper/2606.20280/) (2026)
- [Vesta: A Generalist Embodied Reasoning Model](https://mlsanity.com/paper/2606.20905/) (2026)
- [MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation](https://mlsanity.com/paper/2603.21937/) (2026)
- [MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding](https://mlsanity.com/paper/2604.00513/) (2026)
- [EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations](https://mlsanity.com/paper/2604.18320/) (2026)
- [Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs](https://mlsanity.com/paper/2602.05275/) (2026)
- [Reasoning-Augmented Representations for Multimodal Retrieval](https://mlsanity.com/paper/2602.07125/) (2026)
- [ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge](https://mlsanity.com/paper/2602.09839/) (2026)
- [MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiences](https://mlsanity.com/paper/2603.27813/) (2026)
- [CC-OCR V2: Fine-Grained Attribution of LMM Failures in Real-World Visual Document Understanding](https://mlsanity.com/paper/2605.03903/) (2026)
- [Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction](https://mlsanity.com/paper/2605.05134/) (2026)
- [EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams](https://mlsanity.com/paper/2605.07299/) (2026)
- [SteerVTE: Seamless Video Text Editing with Style and Glyph Control](https://mlsanity.com/paper/2606.23254/) (2026)

## Abstract (arXiv)

In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, they provide an end-to-end pipeline for high-precision multimodal search by mapping diverse modalities, including text, images, document images, and video, into a unified representation space. The Qwen3-VL-Embedding model employs a multi-stage training paradigm, progressing from large-scale contrastive pre-training to reranking model distillation, to generate semantically rich high-dimensional vectors. It supports Matryoshka Representation Learning, enabling flexible embedding dimensions, and handles inputs up to 32k tokens. Complementing this, Qwen3-VL-Reranker performs fine-grained relevance estimation for query-document pairs using a cross-encoder architecture with cross-attention mechanisms. Both model series inherit the multilingual capabilities of Qwen3-VL, supporting more than 30 languages, and are released in 2B and 8B parameter sizes to accommodate diverse deployment requirements. Empirical evaluations demonstrate that the Qwen3-VL-Embedding series achieves state-of-the-art results across diverse multimodal embedding evaluation benchmarks. Specifically, Qwen3-VL-Embedding-8B attains an overall score of 77.8 on MMEB-V2, ranking first among all models (as of January 8, 2025). This report presents the architecture, training methodology, and practical capabilities of the series, demonstrating their effectiveness on various multimodal retrieval tasks, including image-text retrieval, visual question answering, and video-text matching.