The Alibaba team has recently released their impressive Quen 3 models, featuring two open-source mixture of expert models. The Quen 3 235 billion parameter model with 22 billion active parameters and the Quen 3 30 billion parameter model with 3 billion active parameters are both available under the Apache 2.0 license through Hugging Face.

These models are exceptionally capable, matching the performance of top-tier models like DeepSeek R1 and outpacing them in various categories, including math, coding, and reasoning. The open-source nature of these models makes them an attractive option for developers and researchers.

To leverage the power of Quen 3, you can integrate it with an AI coding agent, such as the one provided by Codeium. This agent can be directly integrated into your IDE, allowing you to create, edit, and execute code autonomously, with your permission. You can set up the Quen 3 model by either using the free API from OpenRouter or by locally hosting the model using Lama.

In the provided examples, the Quen 3 model demonstrates its capabilities in various tasks, such as creating a modern-looking SaaS landing page, coding a TV with 0 to 9 channels, generating a butterfly representation in 3D, and creating an animated weather card. The model's performance in these tasks showcases its versatility and potential in the realm of creative programming and visual simulation.

Overall, the Quen 3 models are a remarkable addition to the AI landscape, offering open-source, high-performing capabilities that can be seamlessly integrated into your development workflow. Explore the power of Quen 3 and unlock new possibilities in your coding and creative endeavors.

The Quen 3 model, with its impressive performance across various tasks, is an excellent choice for creating a modern SaaS landing page. The 235 billion parameter model matches the capabilities of top-tier models like DeepSeek R1, outperforming them in areas like math, coding, and reasoning.

To leverage the Quen 3 model for this task, you can use the client AI coding agent within your IDE, such as VS Code. After setting up the necessary API credentials or local hosting with Lama, you can simply prompt the model to "create a modern-looking SaaS landing page." The model will then generate the necessary components, and you can review the resulting HTML file to see the fully developed landing page.

The Quen 3 model has demonstrated its prowess in this task, often outperforming the DeepSeek model in terms of the quality and creativity of the generated landing page. The model's ability to understand the requirements and translate them into a visually appealing and functional design makes it a valuable tool for SaaS businesses looking to create a compelling online presence.

Next up, we're going to have the Quen 3 model code out a TV with 0 to 9 channels. This prompt focuses on how well the model performs in creative programming and simulation with graphics and generation. We're trying to see how well the model can create visually engaging animations for nine different channels.

The model has generated the code, and I've pasted it into a p5 viewer. The result is impressive - there's an animation between the changing channels, which is something that many other models were not capable of doing. The model has demonstrated a lot of creativity in generating the different types of channels, and it has done a great job in visually simulating all the different animations. It has used spatial masking logic to achieve this.

Overall, this is probably one of the best generations I've seen for this prompt. The Quen 3 model has shown its prowess in creative programming and simulation, showcasing its ability to produce visually engaging and dynamic content.

The Quen 3 model, with its impressive capabilities, showcased its prowess in generating a visually captivating butterfly representation using SG code. Initially, the first attempt did not yield the desired result, but with the model's ability to follow instructions and refine its output, it was able to produce a stunning butterfly in just two iterations.

The generated butterfly displayed a well-structured head and a natural, organic shape that captured the essence of the delicate creature. The attention to detail and the model's understanding of spatial relationships and animation techniques were evident in the final result.

This demonstration highlights the Quen 3 model's versatility in tackling creative tasks, such as generating visually engaging graphics and animations. Its ability to refine its output based on feedback underscores the model's adaptability and problem-solving skills, making it a valuable tool for designers and developers alike.

To create an animated weather card using the Quen 3 model, we can leverage its impressive capabilities in generating visually engaging content. The task involves developing a dynamic dashboard that displays the current weather conditions, including temperature, weather type (sunny, cloudy, rainy, snowy), and an animated icon representing the weather.

The Quen 3 model, with its strong performance in coding and graphics generation, is well-suited for this task. By combining the model's abilities, we can create a functional and visually appealing weather card.

The process starts by instructing the model to generate the necessary HTML, CSS, and JavaScript code to build the weather card. The model should be able to create a responsive layout, incorporate dynamic temperature and weather type displays, and generate the animated weather icon.

Through iterative prompting and feedback, the model can refine the design and ensure the weather card looks visually appealing. While the initial generation may not be perfect, the model's ability to understand and improve upon the design based on the provided instructions is a valuable asset.

By harnessing the versatility of the Quen 3 model, we can create a functional and visually engaging weather card that showcases the model's capabilities in both coding and graphics generation.

I definitely think that this is a great option as a lightweight open-source coding model that you can use locally that also packs its performance in multiple different areas, especially with coding. So definitely recommend that you try this out. You can use different parameter size models to use this locally. I'll leave all these links in the description below so that you can easily get started.

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