Best Qwen3 Embedding Alternatives in 2025
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Boost search accuracy with Qwen3 Reranker. Precisely rank text & find relevant info faster across 100+ languages. Enhance Q&A & text analysis.
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Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud.
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Qwen2.5 series language models offer enhanced capabilities with larger datasets, more knowledge, better coding and math skills, and closer alignment to human preferences. Open-source and available via API.
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EmbeddingGemma: On-device, multilingual text embeddings for privacy-first AI apps. Get best-in-class performance & efficiency, even offline.
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FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models. Please open a Github issue if you want us to add a new model.
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Qwen2-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
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Qwen2.5-Turbo by Alibaba Cloud. 1M token context window. Faster, cheaper than competitors. Ideal for research, dev & business. Summarize papers, analyze docs. Build advanced conversational AI.
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jina-embeddings-v3 is a frontier multilingual text embedding model with 570M parameters and 8192 token-length, outperforming the latest proprietary embeddings from OpenAI and Cohere on MTEB.
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Qwen-MT delivers fast, customizable AI translation for 92 languages. Achieve precise, context-aware results with MoE architecture & API.
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Snowflake Arctic embed: High-performance, efficient open-source text embeddings for RAG & semantic search. Improve AI accuracy & cut costs.
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Qwen2-Math is a series of language models specifically built based on Qwen2 LLM for solving mathematical problems.
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The SFR-Embedding-Mistral marks a significant advancement in text-embedding models, building upon the solid foundations of E5-mistral-7b-instruct and Mistral-7B-v0.1.
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embaas offers powerful features like embedding generation, document text extraction, document to emb
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Eagle 7B : Soaring past Transformers with 1 Trillion Tokens Across 100+ Languages (RWKV-v5)
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CodeQwen1.5, a code expert model from the Qwen1.5 open-source family. With 7B parameters and GQA architecture, it supports 92 programming languages and handles 64K context inputs.
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Rerank 3 is an advanced model optimized for enterprise search and retrieval assistance generation (RAG) systems.
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XVERSE-MoE-A36B: A multilingual large language model developed by XVERSE Technology Inc.
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DeepSeek-VL2, a vision - language model by DeepSeek-AI, processes high - res images, offers fast responses with MLA, and excels in diverse visual tasks like VQA and OCR. Ideal for researchers, developers, and BI analysts.
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Discover EXAONE 3.5 by LG AI Research. A suite of bilingual (English & Korean) instruction - tuned generative models from 2.4B to 32B parameters. Support long - context up to 32K tokens, with top - notch performance in real - world scenarios.
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Gemma 3 270M: Compact, hyper-efficient AI for specialized tasks. Fine-tune for precise instruction following & low-cost, on-device deployment.
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VectorDB is a simple, lightweight, fully local, end-to-end solution for using embeddings-based text retrieval.
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Marqo is more than a vector database, it's an end-to-end vector search engine. Vector generation, storage and retrieval are handled out of the box through a single API. No need to bring your own embeddings.
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Qwen2-Audio, this model integrates two major functions of voice dialogue and audio analysis, bringing an unprecedented interactive experience to users
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Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance.
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Qwen Code: Your command-line AI agent, optimized for Qwen3-Coder. Automate dev tasks & master codebases with deep AI in your terminal.
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Seed-X: Open-source, high-performance multilingual translation for 28 languages. Gain control, transparent AI & unparalleled accuracy.
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Reka Flash 3: Low-latency, open-source AI reasoning model for fast, efficient apps. Powering chatbots, on-device AI & Nexus.
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MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models.
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Phi-3 Mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-2 - synthetic data and filtered websites - with a focus on very high-quality, reasoning dense data.
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Yuan2.0-M32 is a Mixture-of-Experts (MoE) language model with 32 experts, of which 2 are active.