Reproducible Benchmark: Qwen Image 2512 vs SDXL vs FLUX for Text-in-Image

Hi, everyone, I’m Dora. Recently I’ve been running text rendering benchmarks, pitting Qwen Image 2512, SDXL, and FLUX against each other. I spent the last three weeks testing text-in-image generation because I kept seeing claims that “this model finally solves text rendering.” The claims felt loud. The proof felt thin.

So I built a reproducible benchmark using Qwen Image 2512, SDXL, and FLUX—three models people keep comparing. I wanted to see what actually happens when you ask them to render posters, menus, and mixed layouts. Not cherry-picked examples. Not marketing screenshots. Just consistent tests across identical prompts.

Why Reproducible Benchmarks Matter

Most comparisons I’ve seen show single examples. One beautiful poster from Model A, one broken sign from Model B. It tells you something happened once—not what happens reliably.

I needed to understand trade-offs. When does SDXL struggle? Where does FLUX shine? What does Qwen Image 2512 actually deliver when you push it with long text or complex layouts?

According to Hugging Face’s model documentation, Qwen Image 2512 improves text rendering accuracy and layout quality, with over 10,000 rounds of blind evaluations showing it as a leading open-source model. Meanwhile, community testing found FLUX clearly wins at text rendering compared to SDXL, generating correct text in each test image while SDXL struggles. But those evaluations didn’t answer my specific question: what happens with poster layouts versus menu text versus thumbnail graphics?

Benchmark Setup

I tested all three models using the same hardware—an NVIDIA RTX 4090 with 24GB VRAM. Each model ran with its recommended settings to avoid unfair handicaps.

Same prompt set across models

Twenty prompts total, organized into four categories. Each prompt specified exact text content, layout requirements, and visual style. I ran each prompt three times per model to catch inconsistencies.

I didn’t use negative prompts for FLUX because FLUX uses flow matching instead of classifier-free guidance, which means it doesn’t support negative conditioning. To keep comparisons fair, I skipped negative prompts for all models.

Same aspect ratios and parameters

Every test used 1024×1024 resolution.

SDXL ran at 30 steps with CFG scale 7
FLUX Dev used 20 steps with guidance scale 5
Qwen Image 2512 ran at 28 steps with guidance scale 5, which community testing suggests balances quality and prompt adherence

Generation times varied significantly. SDXL took about 13 seconds for four images, while FLUX Dev needed 57 seconds—roughly four times longer. Qwen Image 2512 fell between them at around 5 seconds per image with optimized settings.

Prompt Set (Open Source)

I’m sharing the full prompt set because reproducibility requires seeing the actual tests. These aren’t perfect prompts—they’re realistic scenarios I actually encounter.

To make prompt-level comparisons easier to reproduce and extend, we’re also testing the same prompt sets across different execution environments, including WaveSpeed, which provides a consistent interface for running multiple image models with comparable parameters.

As with all results here, outputs remain sensitive to prompt wording, step count, and guidance scale—so results should be interpreted as directional rather than absolute.

Poster prompts (5 examples)

“Event poster with bold title ‘Summer Festival’ at top, subtitle ‘July 15-17’ below, three bullet points listing activities, and footer text ‘Register at summerfest.com’”
“Movie poster style with large text ‘THE LAST HORIZON’ centered, smaller text ‘Coming Soon’ at bottom”
“Workshop announcement with heading ‘Learn Python in 5 Days’, date and time details, instructor name, registration info”
“Concert poster with band name in decorative font, venue details, ticket pricing”
“Book cover layout with author name, title in serif font, subtitle, publisher logo”

Thumbnail prompts (5 examples)

“YouTube thumbnail with large text ‘TOP 5 TIPS’ and small badge saying ‘NEW’”
“Product thumbnail showing text ‘50% OFF’ prominently with smaller ‘Limited Time’ label”
“Course thumbnail with title ‘Advanced AI’ and difficulty indicator ‘Expert Level’”
“Recipe thumbnail with dish name and ‘Ready in 30 min’ badge”
“News thumbnail with headline and ‘BREAKING’ tag”

“Coffee shop menu board with five items, prices, and ‘Daily Specials’ header”
“Restaurant sign showing ‘Now Open’ with business hours listed below”
“Store window sign with ‘Grand Opening’ and date information”
“Cafe chalkboard menu with three sections and decorative borders”
“Retail signage with ‘Clearance Sale’ and percentage discounts”

Mixed content prompts (5 examples)

“Infographic with title, three numbered steps, and summary box”
“Social media post with quote text overlaid on gradient background”
“Presentation slide with bullet points and footer text”
“Magazine layout with headline, body text preview, and page numbers”
“Advertisement with product name, feature list, and call-to-action”

Evaluation Rubric

I scored each output on four dimensions using a 1–5 scale. I didn’t use OCR automation because I wanted to catch layout problems that pure character recognition misses.

Text readability (1–5)

Can you read every word without squinting? Are characters correctly formed? Do letters blur together or show artifacts?

Score 5: Every character is crisp and legible. No spelling errors, no merged letters, no missing strokes.
Score 3: Most text is readable but shows minor issues—slight blur, occasional character confusion.
Score 1: Text is largely illegible or contains major spelling errors.

Layout accuracy (1–5)

Does the text appear where the prompt specified? Are hierarchies respected—headers larger than body text, proper spacing between elements?

Qwen Image 2512 impressed me here. According to testing documentation, it improves layout quality and multimodal composition, which reduces retry counts for complex designs.

Visual fidelity (1–5)

Beyond readable text, does the overall image look coherent? Are fonts appropriate to the context? Does the text integrate naturally with background elements?

This is where differences became obvious. Some models rendered perfect text on incoherent backgrounds. Others created beautiful images with broken text.

Overall aesthetics (1–5)

Would you actually use this output? Does it look finished or does it need significant post-processing?

Results Summary

After 180 total generations (20 prompts × 3 models × 3 attempts), patterns emerged that surprised me.

Where Qwen Image 2512 wins

Poster layouts with 50+ characters. When I asked for event posters with multiple text blocks, Qwen Image 2512 consistently placed elements correctly. Text stayed crisp even with longer strings.

The model emphasizes text rendering quality with clearer characters, stable line spacing, and predictable alignment—especially valuable for marketing visuals and design drafts. I noticed this especially with Chinese–English mixed content, though my tests focused on English.

Speed was notable. Five seconds per image meant I could iterate quickly without losing quality. That matters when you’re refining a design through multiple attempts.

Where SDXL wins

Artistic styles and fast iteration. When prompts emphasized style over text precision—“retro poster aesthetic” or “vintage sign look”—SDXL delivered more consistent artistic interpretation. SDXL’s dual-architecture approach with base and refiner models gives it strong aesthetic performance, especially for stylized content. The ecosystem advantage matters too: more LoRAs, more ControlNet options, more community resources.

Generation speed gave SDXL an edge for rough drafts. Thirteen seconds for four images beats waiting a minute when you’re just exploring concepts.

Where FLUX wins

Short text with complex prompts. For thumbnails and simple signs, FLUX Dev rarely made spelling mistakes. Community testing shows FLUX excels at kerning, spacing, and font style reproduction, producing crisp text that matches professional typography standards.

The T5 encoder seems to make a difference. FLUX uses T5 technology from Google’s language models, which improves understanding of complex prompts and text rendering quality.

But FLUX struggled with longer text blocks. After about 30 characters, accuracy dropped noticeably. And independent testing confirmed that while FLUX shows improvements over earlier models, outputs often fall short of the flawless examples in marketing materials.

Recommendations by Use Case

If you’re generating posters with multiple text elements and need reliable layout: Qwen Image 2512 handled this better than I expected. The 28-step generation provided good quality without excessive wait times.

If you’re prototyping designs and style matters more than perfect text: SDXL gives you speed plus artistic flexibility. You’ll probably fix text in post-production anyway.

If you’re creating thumbnails or short signage and text accuracy is critical: FLUX Dev delivered the cleanest short-form text. Just don’t ask it to render paragraphs.

For mixed workflows, I found myself using different models for different stages. SDXL for exploring visual directions quickly. Qwen Image 2512 when layout complexity increased. FLUX Dev when final text needed to be pixel-perfect for shorter content. What surprised me most wasn’t which model won overall—because no single winner exists. It was realizing that “text-in-image” isn’t one problem. It’s at least three: character accuracy, layout precision, and aesthetic integration. Different models solve different pieces.

The benchmark prompts are available for anyone who wants to verify these findings or test other models. I’m curious whether these patterns hold across different hardware configurations or prompt styles.

Have you tested text rendering recently? Which model surprised you the most (or frustrated you the most)? Feel free to share your results and prompts in the comments!