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6Qwen Research (via RSSHub)·1mo ago

QwQ-Max-Preview Released by Qwen Team

Alibaba's Qwen team has released QwQ-Max-Preview, a preview version of their reasoning-focused model built on top of Qwen2.5-Max. The post is itself generated by the model, serving as a demonstration of its capabilities. As a preview release, it signals an upcoming full model launch in the Qwen series.

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7Qwen Research·1mo ago·source ↗

QVQ-72B-Preview: Qwen Visual Reasoning Model Release

Alibaba's Qwen team has released QVQ-72B-Preview, a 72-billion parameter multimodal model designed to integrate visual understanding with advanced reasoning capabilities. The model is positioned as an extension of Qwen's language reasoning work into the visual domain. It is available on GitHub, Hugging Face, ModelScope, and Kaggle with a live demo.

7Qwen Research·1mo ago·source ↗

QwQ-32B-Preview: Alibaba's Qwen Reasoning Model with Deep Reflection Capabilities

Alibaba's Qwen team has released QwQ-32B-Preview, a 32-billion parameter model designed for deep reasoning across mathematics, code, and general knowledge. The model is positioned as a reasoning-focused system that emphasizes uncertainty and iterative questioning as core design principles. It is available on GitHub, Hugging Face, ModelScope, and via a demo interface.

7Qwen Research·1mo ago·source ↗

QVQ-Max: Alibaba Qwen Releases Visual Reasoning Model with Multimodal Chain-of-Thought

Alibaba's Qwen team has officially released QVQ-Max, a visual reasoning model succeeding the December 2024 QVQ-72B-Preview. The model is designed to analyze and reason over images and videos, covering domains including mathematics, programming, and creative tasks. It represents a step beyond the exploratory preview, positioning as a production-grade multimodal reasoning system.

7Hacker News·1mo ago·source ↗

Qwen3.7-Max: The Agent Frontier

Alibaba's Qwen team has announced Qwen3.7-Max, positioned as a frontier model for agentic tasks. The announcement appears on the official Qwen blog and generated significant community discussion on Hacker News with 559 points and 217 comments. The model name suggests it is part of the Qwen 3 generation, with a focus on agent capabilities.

7Qwen Research·1mo ago·source ↗

Qwen2.5-Max: Large-Scale MoE Model Release by Alibaba's Qwen Team

Alibaba's Qwen team announces Qwen2.5-Max, a large-scale Mixture-of-Experts language model. The post acknowledges that scaling insights for very large MoE models have been limited, citing DeepSeek V3's recent disclosures as a reference point. The model is positioned as a frontier-scale MoE system developed concurrently with ongoing Qwen2 research.

6Hacker News·1mo ago·source ↗

Qwen 3.7 Preview Announced by Alibaba

Alibaba's Qwen team has announced a preview of Qwen 3.7, the next iteration in their Qwen 3 model series. The announcement appeared on Twitter/X and generated notable community discussion on Hacker News with 179 points and 67 comments. Specific capability details and model specifications are not available from this source alone.

6Qwen Research·1mo ago·source ↗

Introducing Qwen-VL-Plus and Qwen-VL-Max: Upgraded Multimodal Models from Alibaba

Alibaba's Qwen team has launched two enhanced versions of their multimodal model, Qwen-VL-Plus and Qwen-VL-Max, building on the open-sourced Qwen-VL released in September 2023. Key improvements include substantially boosted image reasoning capabilities, enhanced detail recognition and text extraction from images, and support for high-definition images exceeding one million pixels across various aspect ratios. The upgrades represent a significant step forward in the Qwen-VL series' generalization and visual understanding capabilities.

8Qwen Research·1mo ago·source ↗

Qwen2 Model Family Released: Five Sizes, 128K Context, Multilingual

Alibaba's Qwen team has released Qwen2, an evolution from Qwen1.5, comprising five pretrained and instruction-tuned models ranging from 0.5B to 72B parameters, including a 57B mixture-of-experts variant (57B-A14B). The release highlights training on 27 additional languages beyond English and Chinese, significantly improved coding and mathematics performance, and extended context support up to 128K tokens for the 7B and 72B instruct variants. Benchmark results are claimed to be state-of-the-art across a large number of evaluations.