• Canva launched Canva Craft, a tool similar to AI image models by Google, Meta, and OpenAI.
• Surprisingly, Canva fell short on a design task but did well at generating more realistic images.
• Here is how Canva's new image generation tool stacks up against ChatGPT, Gemini, and Muse.

Canva wants a shot at winning the AI image generation race.

The design platform launched Canva Craft on Thursday, its first image-generation model, similar to those from Google, Meta, and OpenAI.

Users can access Craft through Canva’s Dream Lab, including with free accounts, though it is subject to AI usage limits. You’ll need to sign in to a Canva account to get started.

In the release announcement, Canva said the tool plans a design’s layout, proportions, and text before generating an image, an approach that aims to help it produce legible lettering and accurate details.

The company also said that the model was trained on human-designed Canva templates, including posters, menus, and presentations, rather than photographs.

To test how Canva performs, Business Insider created three image generation prompts with escalating difficulty levels — from designing a simple greeting card to creating a photorealistic image from scratch.

Here is how Canva's brand-new image generation tool stacked up against three of the most powerful models.

Canva did not respond to a request for comment.

Starting off with an illustration

We started off simple with a prompt for a birthday card.

Create a cartoon "Happy Birthday" greeting card for a friend named Jessica who has pink hair, brown eyes, likes to wear paisley, and is turning 28 years old in November.

Surprisingly, Canva, the design platform, fell short in its design of the birthday card, from the awkward font to the outsize head of the cartoon character. It didn't even bother to include any paisley.

By comparison, ChatGPT and Muse understood the assignment.

ChatGPT guessed that the recipient's favorite color might be pink, while Muse thoughtfully wove in cozy fall elements for the November birthday girl.

Meanwhile, Gemini took the request literally and produced an image of an actual card. Extra points!

Winner: Muse

Editing an image

For the second task, I tested the AI models' ability to identify an element and place it in a brand-new setting.

Edit this image of me holding a necklace pendant. Take the red garnet pendant off my hand and make it look as if it is displayed in a vitrine in a jewelry showroom.

Canva did a great job of identifying the pendant and creating an atmospheric setting, but, like Gemini, it depicted the jewelry in a way that defied physics and common sense. It would be unrealistic for a pendant to float in the air or sit propped on a metal pole.

ChatGPT and Muse once again outperformed other models in understanding how things typically look in reality. Muse, however, made some alterations to the pendant, which I didn't ask for.

Winner: ChatGPT

Creating a photorealistic image from scratch

With Halloween approaching, I asked for a realistic depiction of my favorite animal.

Generate a photorealistic image of a long-haired orange tabby cat in a Halloween costume with bat wings, perched on the edge of a gothic medieval castle under the full moon and a starless night sky.

Though the cat's fur looks a little too strandy and high-contrast in Canva's creation, no components are missing or malformed, and the image composition pretty much matches the quality of Muse and Gemini.

The clear winner for me in this round, however, would be ChatGPT.

Not only is the cat's posture majestic, but the soft glow on his left side is extremely realistic, and the white balance is well done. There are also clouds softly cradling the moon, which could be a response to my request for a starless sky.

Winner: ChatGPT

Overall, it's evident that Canva still has a lot of room to grow compared to other models, especially if graphic design is its focus. For now, I'll stick to moving components around by hand.

• Hugging Face released a new AI model that allows users to "chat with DNA."
• Carbon-X can analyze and generate DNA and allows researchers to ask questions in plain language.
• Hugging Face said the model can help researchers who work outside the big AI labs.

Hugging Face is releasing an AI model that could make it easier for scientists to study DNA and develop new genetic treatments, the company announced Thursday.

Carbon-X is a "first of its kind" model that combines DNA analysis and generation with plain language, allowing researchers to ask questions and essentially "chat with DNA," according to Lewis Tunstall, a senior research scientist at Hugging Face.

Hugging Face, which Nvidia agreed to acquire for nearly $13 billion in September, is a platform for sharing and developing AI models and also releases its own. The company worked with research labs in Italy and China on Carbon-X, which is an open-source model. Hugging Face built Carbon-X by training an existing language model, Qwen3-14B, on genomic sequences and natural language. It was trained on DNA sequences from humans, other mammals, plants, and fungi.

As an example of how Carbon-X can be used, Tunstall said a researcher could provide a DNA sequence and ask whether a particular mutation could increase a person's risk of disease. The model would then offer a prediction and explanation. It can also generate new DNA sequences, which could help researchers develop new gene therapies.

Tunstall said the company was partly motivated to build Carbon-X to prevent biology researchers from becoming dependent on the major AI labs, noting how AI has disrupted mathematics.

"If you want to really push the frontiers of mathematics, you likely have to be at one of these AI labs like OpenAI or Anthropic, because they're the ones who have the best mathematics models," he said, suggesting a similar situation is likely to play out in other fields of science.

"We wanted to provide the open research community with tools that don't make them fully dependent on some external single provider," he added.

Researchers can run Carbon-X on their own computers, avoiding the need to share sensitive patient data with external AI companies. He also said that some of the misalignment behavior observed in larger models, such as the OpenAI agents that famously hacked Hugging Face in July, has not yet been observed in smaller, more specialized models.

Tunstall said generalist chatbots also sometimes refuse to answer legitimate biology questions from researchers because they mistake them for attempts to "design a bioweapon." He said viruses were explicitly excluded from Carbon-X's training data in order to avoid misuse of the model.

While Carbon-X is likely to be used by researchers, Tunstall said the model represents a small step toward a future where AI can help provide more personalized medical care.

"I think the holy grail is, in general, this concept of personalized medicine where we're able to, as individuals, take our own patient records, our own genomes, and use this to ultimately find or even maybe design our own treatments," he said.