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Tencent rolls out Hy Image 3.5 preview

Tencent Holdings has launched Hy Image 3.5 Preview, its latest image-generation model, expanding its push into generative artificial intelligence as competition intensifies with ByteDance and Alibaba Group.

The model supports text-to-image creation, image-to-image generation and multi-round editing, according to Tencent Cloud documentation published alongside the release. It can accept multimodal inputs combining text and reference images, preserve context across successive editing turns and produce images at resolutions of up to 4K.

Tencent said the model improves on Hy Image 3.0 in text rendering, realism, stylistic expression and editing consistency. The company said internal evaluations showed an overall capability improvement of about 30 per cent over the previous generation, though those performance claims have not been independently established.

The new system is being integrated into Tencent products including its Yuanbao chatbot and a range of design, productivity and creative tools, while developers can access it through Tencent Cloud’s TokenHub platform. Tencent’s cloud documentation lists the model under the identifier hy-image-v3.5-preview and says it can handle up to 100,000 input tokens for conversational image-generation and editing requests.

The release places greater emphasis on iterative visual production rather than single-prompt image creation. Users can provide reference pictures, request changes over several rounds and retain earlier context, allowing the model to perform tasks such as poster design, product-image editing and other workflows that require repeated adjustments.

Tencent also says the model can use external search enhancement and automatic prompt rewriting, while its application programming interface allows developers to specify image dimensions or let the system determine an appropriate format. The model supports output of up to 4,096 by 4,096 pixels under Tencent Cloud’s documented limits.

The launch comes as China’s largest technology groups broaden their AI offerings beyond large language models and compete more aggressively in image, video and agent-based systems. ByteDance has built a strong position in visual-generation tools through its Seedream family, while Alibaba has expanded its Qwen image models alongside a wider push into cloud infrastructure and AI chips.

Tencent said testing among hundreds of its in-house designers found Hy Image 3.5 Preview performed on a level comparable with ByteDance’s Seedream 5.0 Pro. It also claimed the model slightly exceeded Google’s Nano Banana Pro and Alibaba’s Qwen-Image-3.0 Pro in its own assessments. The company did not disclose full methodology or independent benchmark results for those comparisons.

The timing also coincided with Alibaba’s annual Apsara conference in Hangzhou, where the company outlined plans for a much larger next-generation AI model and introduced its Zhenwu V900 accelerator chip. Alibaba Chief Executive Eddie Wu said the planned model would contain between five trillion and 10 trillion parameters, potentially several times the size of its current flagship system.

Tencent has been accelerating investment in its Hunyuan model family while placing more emphasis on deployment across its existing products. Its Hy3 language model, released in July, was designed around a mixture-of-experts architecture and has since been integrated into services including WorkBuddy, Yuanbao and Tencent Cloud.

The company has also strengthened its AI research leadership. Former OpenAI researcher Yao Shunyu serves as Tencent’s chief AI scientist, while computer-vision specialist Tian Yonglong joined the company in July and leads vision-language model development within the Hunyuan team.

Tencent’s Hong Kong-listed shares climbed more than 6 per cent during Tuesday trading as investors reacted to the product launch and a broader rise in technology stocks. The stock had also advanced in the previous session.

For business users, Tencent Cloud is positioning Hy Image 3.5 Preview as a professional visual-production model rather than a consumer-only image generator. Its documentation says users can combine multiple reference images with text instructions, carry generated images forward into later editing rounds and receive synchronously generated results without submitting separate polling requests.



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