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The Kimi Open Platform launched the Kimi K2 High-Speed Version (kimi-k2-turbo-preview), increasing the output speed from 10 Tokens per second to 40 Tokens per second while maintaining the original parameter scale, significantly improving response efficiency. This upgrade is mainly targeted at applications requiring higher real-time performance, aiming to optimize user experience.

kimi-k2-turbo-preview, the high-speed version of Kimi K2, is officially released. According to the information, this high-speed version model maintains the same parameter settings as the original kimi-k2, but achieves significant performance improvements. The output speed has jumped from 10 Tokens per second to 40 Tokens per second, providing users with a smoother and more efficient experience.

San Francisco AI startup Deep Cogito launches the Cogito v2 series of open-source large models, focusing on machine intuition and self-improvement capabilities. The series includes four dense and mixture-of-experts models with 70B to 671B parameters. The flagship 671B MoE model has a reasoning efficiency that is 60% higher than DeepSeek R1, and its performance is close to Claude4Opus. Its core technology involves internalizing reasoning paths into model weights through introspective reasoning, enabling continuous evolution while in use. Tests show that the model can

The Qwen3-Coder series has welcomed a new member - Qwen3-Coder-Flash, a programming model affectionately called "a dessert-level" by developers. With its outstanding performance and efficient operation speed, this model brings new surprises to the field of programming. The full name of Qwen3-Coder-Flash is Qwen3-Coder-30B-A3B-Instruct. It performs excellently in terms of performance and efficiency, especially in agentic coding and browsing

The Qwen3-30B-A3B model has undergone a major update, launching the new version Qwen3-30B-A3B-Thinking-2507. This new version has achieved significant improvements in reasoning capabilities, general abilities, and context length, marking that the model is not only lighter but also has stronger thinking capabilities. Compared to the Qwen3-30-A3B model open-sourced on April 29, the new model has made progress in multiple key areas. In terms of mathematical abilities, Qwen3-30B-A3B-Thin

Alibaba's Qwen team released Qwen3-30B-A3B-Instruct-2507, a 3B-parameter model rivaling Gemini2.5-Flash and GPT-4o. It excels in multilingual tasks, long-context processing, and benchmarks, with some metrics surpassing GPT-4o. Now available on ModelScope and HuggingFace.....

On July 29, a new version of the Qwen3-30B-A3B model, called Qwen3-30B-A3B-Instruct-2507, was released. This new version achieves significant improvements in multiple key areas, marking that the model can achieve performance comparable to top closed-source models such as Gemini2.5-Flash (non-thinking) and GPT-4o, by activating only 3B parameters in non-thinking mode.

Pudong New Area launched a seed fund with a total scale of 2 billion yuan for artificial intelligence. The first phase is 500 million yuan, making it the first state-owned capital venture capital fund in China focusing on the pre-seed stage. It has signed 18 projects. The AI industry scale in Pudong exceeds 160 billion yuan, accounting for 40% of the city, and has gathered more than 200 specialized model enterprises. In the future, five major measures will be implemented: releasing over 30 application scenarios, building a heterogeneous robot training field, establishing a full-cycle fund matrix, optimizing industrial policies, and providing talent services. The goal is to add 1,000 AI companies within three years, and the core output value will exceed 200 billion yuan, aiming to build a comprehensive AI ecosystem.

VLM2Vec-V2, a multimodal embedding framework developed by Salesforce Research and other institutions, breaks through the limitations of traditional models. Based on the Qwen2-VL architecture, this framework innovatively unifies image, video, and visual document retrieval tasks. It adds five new evaluation tasks to expand the MMEB dataset. With key technologies such as dynamic resolution and M-RoPE, it achieved an average score of 58.0 across 78 datasets, leading in performance, especially in video tasks. Although its document retrieval is slightly behind ColPali, it

Zhiyuan Robotics announced the open-source plan for the Zhiyuan Lingqu OS, an embodied intelligence operating system, at the 2025 World Artificial Intelligence Conference. The humanoid robot Lingxi X2 demonstrated intelligent interaction capabilities through natural dialogue. The system adopts a layered open-source model, including underlying middleware, agent framework, and toolchain, with plans to launch in the fourth quarter. Zhiyuan Robotics has already achieved application in fields such as industrial logistics, and its Qiyuan general embodied large model won the SAIL Star Award. The company will continue to improve the operating system and promote the development of embodied intelligence industry.

Tongyi Lingma announces the launch of a new Qwen3-Coder model, and this powerful feature will be freely available to users without any usage limits. Users can now easily experience the efficient programming assistance provided by Qwen3-Coder in the Tongyi Lingma AI IDE, VSCode, and JetBrains plugin.

Today, the 2025 World Artificial Intelligence Conference (WAIC) officially kicked off, and Alibaba announced the technical progress of its first self-developed AI glasses - 'Qwen AI Glasses' at this event, and showcased a real machine on site. From the current display, the Qwen AI Glasses do not have a screen, but instead use a combination of lenses and voice interaction, which is considered one of the more suitable forms of AI carriers at present.

Alibaba's Qwen3-Coder tops Hugging Face's model rankings as the hottest open-source AI coding model, outperforming GPT-4.1 and Claude4 with its MoE architecture, excelling in multi-agent tasks, and surpassing 20M downloads. Tech leaders praise it as a breakthrough for Chinese open-source AI.....

The Qwen-MT machine translation model is officially released. Qwen-MT is based on the powerful Qwen 3 model, and has been trained on a massive amount of multilingual and translation data. It combines reinforcement learning technology to significantly improve the accuracy and fluency of translation results. Developers can experience its fast and accurate translation capabilities directly through the Qwen API (qwen-mt-turbo). The core highlights of Qwen-MT include supporting bidirectional translation between 92 languages, covering more than 95% of the global population, and meeting a wide range of language communication needs.

The Kimi team released the Kimi K2 technical report. This trillion-parameter large model, with 32 billion activated parameters, won the global open-source model competition, with performance comparable to top closed-source models such as GPT4.5. Its innovations include: using the MuonClip optimizer to process 15.5 trillion token data without loss; developing a multi-domain tool data synthesis pipeline; pioneering the restatement method to improve training efficiency, achieving the effect of ten rounds of original data in one round of rewritten data; optimizing model performance through supervised fine-tuning and reinforcement learning. The model is based on N

Alibaba's Qwen3-Coder, a 48B-parameter MoE-based AI model, excels in complex programming tasks with 256K context support. Trained on 7.5TB data, it outperforms GPT4.1 in benchmarks and is open-sourced on ModelScope and Alibaba Cloud.....

On July 23, Alibaba Cloud officially announced that its latest AI programming large model, Qwen3-Coder, is fully open-sourced, which quickly sparked a buzz in the field of intelligent programming. Qwen3-Coder has achieved top performance among open-source models in agentic coding, agentic browser use, and basic coding tasks, thanks to its outstanding code generation and agent capabilities, marking a new stage in intelligent programming technology. The Qwen3-Coder model series provides