Can AI Master the Kitchen?
Authors: Cobaia Kitchen, Claude 3.7 Sonnet
Photos: Cobaia Kitchen, DALL-E 3
The sweet, comforting aroma of banana bread has filled my kitchen for years. My recipe, perfected over countless baking sessions, has become my gold standard—the one friends request and family members devour within hours. But recently, as I watched generative AI transform industry after industry, a question began to simmer in my mind: could artificial intelligence create a banana bread recipe that rivals—or even surpasses—my own?
This curiosity sparked “The Banana Bread Benchmark,” an experiment designed not just to satisfy my sweet tooth, but to establish a practical, delicious standard for measuring AI’s culinary capabilities. After all, what better way to evaluate AI’s kitchen prowess than through something as universally beloved as banana bread?
The Contenders Enter the Kitchen
For our inaugural bake-off, I invited three sophisticated AI models to don virtual aprons:
- Claude 3.7 Sonnet from Anthropic, renowned for thoughtful reasoning and creative flair
- Deepseek R1, a model built to tackle complex problems with precision
- Perplexity’s Deep Research, designed to synthesize information with depth and nuance
Each AI received the same challenge: create an original, delicious banana bread recipe. No additional guidance, no hints about ingredients or techniques—just pure AI creativity at work.
From Code to Kitchen
With recipes in hand (and pinned to this blog for you to try at home), I rolled up my sleeves and began the baking marathon. My kitchen transformed into a laboratory of sorts, with four different batters coming to life—three AI-created recipes plus my trusted standard.
As each loaf emerged from the oven, I couldn’t help but notice the visual differences. My traditional banana bread and Claude’s had the most appealing appearance overall, while Perplexity’s Deep Research creation looked the most tempting with its unique topping.
The Verdict: AI vs. Tradition
I recruited three friends and family members to serve as guinea pigs for our taste test. These brave volunteers gathered around the kitchen table, armed with forks and unbiased palates. The results were fascinating:
Claude’s creation had a firmer texture that sliced beautifully—better than my own recipe—but tasted less like banana and more like Christmas spices. The texture was noticeably drier than my traditional loaf.
Deepseek’s banana bread was rather bland compared to the others. While it also sliced well, it lacked moisture and excitement, with too few chocolate chips to make it interesting.
Perplexity’s Deep Research model produced the most divisive loaf. One person loved its crispy top and ranked it first, saying it tasted like a delicious cake, though “not really like banana bread.” Another person wasn’t a fan at all, while a third one appreciated the streusel-like topping. It was notably crumbly and difficult to slice.
My traditional recipe, while more likely to fall apart when sliced than Claude’s, won points for its superior moisture and authentic banana flavor.
The Final Rankings
Two of our guinea pigs ranked the breads: 1. My recipe, 2. Claude, 3. Deepseek, 4. Deep Research
Our third tester had a different take: 1. Deep Research (though with the caveat that it didn’t taste like traditional banana bread), 2. My recipe (which they called “the best of the obvious banana breads”), followed by the others which were deemed “okay but not special.”
Chef’s Notes
From my perspective as the baker, each AI recipe revealed something interesting about current AI capabilities:
Deep Research created something more akin to an afternoon coffee cake than a quick banana bread. The recipe was unnecessarily complicated, used too much coconut oil (which gave it an odd smell while baking), and produced an excessive quantity. The flavor of the topping was somewhat strange, though the presentation was impressive.
Claude’s creation had a distinct holiday flavor profile with its heavy use of spices. While firmer and less moist than mine, it would freeze and slice better. The spices, though interesting, weren’t suitable for everyday enjoyment. The preparation process was straightforward, similar to my own recipe.
Deepseek produced a rather mediocre loaf—too dry and uninteresting. The recipe also made too much batter, but was simple to prepare. It lacked the flavor intensity of Claude’s bread and the chocolate chips were too sparse to add much interest.
What This Tells Us About AI in the Kitchen
This banana bread benchmark reveals that AI can indeed create functional recipes, but still struggles with the nuanced understanding of flavor combinations, textures, and proportions that come naturally to experienced human bakers.
While none of the AI recipes dethroned my traditional banana bread, each brought something unique to the table. Claude understood structure, Deepseek grasped the basics, and Deep Research pushed creative boundaries—even if it strayed from what makes banana bread so beloved.
As I clean up my flour-dusted kitchen and package the leftover slices for friends, I’m left thinking that AI has potential as a sous chef, but isn’t quite ready to be head baker. At least not yet. But that’s what makes this benchmark so exciting—we now have a delicious way to measure AI’s culinary progress as these models continue to evolve.
Stay tuned for the next installment of the Banana Bread Benchmark, where we’ll put a new generation of AI models to the test!
Disclaimer
No animals were harmed during the Banana Bread Benchmark experiment. While my human friends were referred to as “guinea pigs” in the taste-testing process, no actual guinea pigs were involved. In fact, banana bread is not a suitable food for real guinea pigs due to its high sugar content and various ingredients that can be harmful to them. However, these adorable pets may enjoy a small slice of fresh banana as an occasional treat!
