qwen3-max-2026-01-23
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qwen3-max-preview
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwen3 Next 80B A3B Instruct
阿里巴巴 · QwenQwen3-Next-80B-A3B-Instructは、思考の痕跡を残さずに高速で安定した応答を実現するよう最適化された、Qwen3-Nextシリーズのインストラクションチューニング済みチャットモデルです。推論、コード生成、知識QA、多言語対応など、複雑なタスクを対象としています…
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$0.09 / 100万入力トークンOpenRouter · qwen/qwen3-next-80b-a3b-instruct
詳細を見る ↗qwen3-next-80b-a3b-thinking
阿里巴巴 · QwenOver the past few months, we have observed increasingly clear trends toward scaling both total parameters and context lengths in the pursuit of more powerful and agentic artificial intelligence (AI). We are excited to share our latest advancements in addressing these demands, centered on improving scaling efficiency through innovative model architecture. We call this next-generation foundation models **Qwen3-Next**. **Qwen3-Next-80B-A3B** is the first installment in the Qwen3-Next series and features the following key enchancements: - **Hybrid Attention**: Replaces standard attention with the combination of **Gated DeltaNet** and **Gated Attention**, enabling efficient context modeling for ultra-long context length. - **High-Sparsity Mixture-of-Experts (MoE)**: Achieves an extreme low activation ratio in MoE layers, drastically reducing FLOPs per token while preserving model capacity. - **Stability Optimizations**: Includes techniques such as **zero-centered and weight-decayed layernorm**, and other stabilizing enhancements for robust pre-training and post-training. - **Multi-Token Prediction (MTP)**: Boosts pretraining model performance and accelerates inference. We are seeing strong performance in terms of both parameter efficiency and inference speed for Qwen3-Next-80B-A3B: - Qwen3-Next-80B-A3B-Base outperforms Qwen3-32B-Base on downstream tasks with 10% of the total training cost and with 10 times inference throughput for context over 32K tokens. - Leveraging GSPO, we have addressed the stability and efficiency challenges posed by the hybrid attention mechanism combined with a high-sparsity MoE architecture in RL training. Qwen3-Next-80B-A3B-Thinking demonstrates outstanding performance on complex reasoning tasks, not only **surpassing Qwen3-30B-A3B-Thinking-2507 and Qwen3-32B-Thinking**, but also **outperforming the proprietary model Gemini-2.5-Flash-Thinking** across multiple benchmarks.
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$0.15 / 100万入力トークンOpenRouter · qwen/qwen3-next-80b-a3b-thinking
詳細を見る ↗qwen3-reranker-0.6b
阿里巴巴 · QwenThe 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. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
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詳細を見る ↗qwen3-reranker-4b
阿里巴巴 · QwenThe 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. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
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詳細を見る ↗qwen3-reranker-8b
阿里巴巴 · QwenThe 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. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
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詳細を見る ↗qwen3-vl-235b-a22b
阿里巴巴 · QwenQwen3-VL-235B-A22B 是通义千问推出的超大规模视觉-语言 MoE 模型（激活参数约 220 亿），支持长上下文和多模态输入。 它能做图像理解、OCR、多模态推理、视频/多帧分析，并结合文本生成、界面交互和代码生成等功能。
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詳細を見る ↗Qwen3 VL 235B A22B Instruct
阿里巴巴 · QwenQwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...
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$0.21 / 100万入力トークンOpenRouter · qwen/qwen3-vl-235b-a22b-instruct
詳細を見る ↗qwen3-vl-235b-a22b-thinking
阿里巴巴 · QwenMeet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
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詳細を見る ↗Qwen3 VL 30B A3B Instruct
阿里巴巴 · QwenQwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception...
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$0.15 / 100万入力トークンOpenRouter · qwen/qwen3-vl-30b-a3b-instruct
詳細を見る ↗Qwen3 VL 30B A3B Thinking
阿里巴巴 · QwenQwen3-VL-30B-A3B-Thinkingは、画像や動画に対する強力なテキスト生成と視覚理解を統合したマルチモーダルモデルです。そのThinkingバリアントは、STEM分野や数学、複雑なタスクにおける推論能力を向上させます。優れた…
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$0.29 / 100万入力トークンOpenRouter · qwen/qwen3-vl-30b-a3b-thinking
詳細を見る ↗qwen3-vl-32b-instruct
阿里巴巴 · QwenMeet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
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詳細を見る ↗qwen3-vl-32b-thinking
阿里巴巴 · QwenThis generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment. 1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.
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詳細を見る ↗Qwen3 VL 8B Instruct
阿里巴巴 · QwenQwen3-VL-8B-Instructは、Qwen3-VLシリーズに属するマルチモーダルな視覚言語モデルで、テキスト・画像・動画間の高忠実度な理解と推論を実現するために構築されています。長距離視野に対応したInterleaved-MRoPEによるマルチモーダル融合が強化されています…
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$0.25 / 100万入力トークンOpenRouter · qwen/qwen3-vl-8b-instruct
詳細を見る ↗qwen3-vl-8b-thinking
阿里巴巴 · QwenMeet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
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詳細を見る ↗qwen3-vl-flash
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗qwen3-vl-flash-2025-10-15
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗qwen3-vl-flash-2026-01-22
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗qwen3-vl-plus
阿里巴巴 · QwenQwen3-VL-Plus 是 Qwen-VL 系列中的增强视觉-语言模型版本，具有更强的细节识别和文本识别能力，支持超高像素分辨率和极端宽高比的图像输入。 它在视觉理解（物体识别、OCR、空间分析等）与文本生成 / 模型推理等多模态任务中都有显著性能提升。
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詳細を見る ↗qwen3-vl-plus-2025-09-23
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗qwen3-vl-plus-2025-12-19
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗qwq-32b
阿里巴巴 · QwenQwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini. **This repo contains the QwQ 32B model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training (Supervised Finetuning and Reinforcement Learning) - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - For prompts exceeding 8,192 tokens in length, you must enable YaRN as outlined in this section. **Note:** For the best experience, please review the usage guidelines before deploying QwQ models.
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詳細を見る ↗qwq-32b-preview
阿里巴巴 · Qwen**QwQ-32B-Preview** is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations: 1. **Language Mixing and Code-Switching**: The model may mix languages or switch between them unexpectedly, affecting response clarity. 2. **Recursive Reasoning Loops**: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer. 3. **Safety and Ethical Considerations**: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it. 4. **Performance and Benchmark Limitations**: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding. **Specification**: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 32,768 tokens
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