OpenAI's previous generation image model with higher quality than DALL-E 2 and support for larger resolutions
Settled by community votes across 2 shared challenges, with an AI judge weighing in on each.
DALL-E 3
#35 of 44 in Text-to-Image
Not enough comparable category data
The chart appears once both models have ratings across at least three shared arena categories.
FLUX.2 [klein] 9B
#7 of 23 in Image Editing
Where the votes landed
DALL-E 3
0%
win rate
Ties
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FLUX.2 [klein] 9B
0%
win rate
Challenge by challenge
The strongest take from each model on every shared challenge, with the AI judge's read.
Magic Burger Explosion: Fiery Photorealism Challenge
Text-to-Image“Ad for 'Magic Burger'. Dynamic, exploded burger with all components (bun, patty, cheese, lettuce, tomato, sauce) suspended in mid-air. Emphasize photorealistic detail and a sense of motion. Dark, fiery background with glowing embers. Integrate text: 'MAGIC BURGER' as a prominent title, 'LIMITED TIME ONLY' as a secondary message, and '€6.99' in a starburst, all rendered with a fiery, glowing effect.”
AI Judge Analysis
DALL-E 3
- + Excellent 'exploded' effect with distinct separation of ingredients
- + Impressive lighting and glow effects on the food
- + Dynamic and high-energy composition
- − Several spelling errors in the text ('MAGIC BURGR', 'Limiited')
- − Failed to render the price in a starburst icon as requested
FLUX.2 [klein] 9B
- + Perfect text accuracy and typography rendering
- + Successfully integrated the price in a flaming starburst
- + Consistent fiery theme across all text elements
- − The burger is not truly 'exploded' as much as it is a standard burger tilted in mid-air
- − Missing some specified ingredients in the mid-air suspension, such as sauce droplets and tomato slices
Verdict: DALL-E 3 creates a much more exciting and literal 'exploded' view of the burger, but it fails significantly on text rendering and specific prompt layout instructions. FLUX.2 provides a professional, ad-ready layout with perfect text and price integration, although the actual burger explosion is less dynamic.
Chalkboard Menu
Text-to-Image“Handwritten-style chalkboard menu in a cozy café, all text rendered in the exact same realistic chalk handwriting style with natural variations in letter size, slight slant, and chalk texture — no printed or digital fonts anywhere on the board. Title at the top in elegant cursive chalk handwriting: ‘TODAY’S SPECIALS – APRIL 30, 2026’. Below it, three menu items also in the same handwritten chalk style: ‘Truffle Mushroom Risotto – $24’, ‘Grilled Octopus with Lemon & Herbs – $28’, ‘Brown Butter Chocolate Chip Cookies – $9’. At the very bottom, smaller text in the identical handwritten chalk style (slightly smaller but still clearly legible with the same handwriting characteristics): ‘All items made fresh daily • Ask about our gluten-free options’. Warm ambient café lighting, visible chalk dust and smudges, realistic handwriting imperfections, no clean printed text anywhere.”
AI Judge Analysis
DALL-E 3
- + Ornate and artistic composition with decorative flourishes.
- + Atmospheric lighting and café setting details.
- − Numerous spelling errors including 'Trufle', 'Occtus', and 'Clutee Cokies'.
- − Text is cluttered and messy, making it difficult to read.
- − Fails to follow the specific text structure requested in the prompt.
FLUX.2 [klein] 9B
- + Excellent text rendering with no spelling errors in the main items.
- + Beautifully realized chalk texture and realistic handwriting style.
- + Perfect adherence to the requested prompt content and layout.
- − Minor typo 'fress' instead of 'fresh' at the very bottom.
- − Less environmental detail compared to the first image.
Verdict: FLUX.2 [klein] 9B followed the prompt with near-perfect accuracy, correctly rendering all the requested menu items with legible and aesthetically pleasing handwriting. DALL-E 3 struggled significantly with spelling and coherence, producing a chaotic board full of gibberish and incorrect prices.
Explore each model
Black Forest Labs' distilled 9 billion parameter image generation model with sub-second inference and multi-reference support