| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-to-image |
| - image-to-image |
| language: |
| - en |
| tags: |
| - benchmark |
| - design |
| - typography |
| - color |
| - image-editing |
| pretty_name: "VybBench 1.0: Design Fidelity Benchmark" |
| --- |
| |
| # VybBench 1.0 |
|
|
| **Almost isn't good enough.** VybBench measures how well models maintain |
| design fidelity by comparing how effectively they generate specific |
| fonts and colors, and perform non-destructive edits. |
|
|
| Our dataset consists of 145 fonts, 100 colors, and 200 edits. We have each model |
| perform each task three times and score each generation individually. |
|
|
| We compare top image generation models: |
| ChatGPT Image (gpt-image-2.5-sunburst), Meta Muse, and Recraft v4.1. We also report |
| the results for [Vyb 1.0](https://cocolabs.ai/vyb). |
|
|
| Full Disclosure: VybBench was built by Coco Labs, the makers of Vyb. The full |
| methodology and rationale are in |
| [the launch post](https://cocolabs.ai/blog/introducing-vybbench). |
|
|
| ## Results |
|
|
| ### Font fidelity |
|
|
| We sample 145 [Google Fonts](https://fonts.google.com). For every font |
| we choose a word to generate. We generate that word programmatically |
| as well as through model calls. We compute the IoU of the ink between |
| the ground truth generation and each model generation. The higher the |
| score the better. As each model is allowed |
| to scale and place the text individually, we compute the noise floor by |
| measuring the IoU of multiple ground truth generations with the same font. |
|
|
| | Model | Mean | Worst 5% of cases | |
| |---|---|---| |
| | **Vyb 1.0** | **0.90** | **0.78** | |
| | ChatGPT Image (`gpt-image-2.5-sunburst`) | 0.76 | 0.28 | |
| | Muse (`meta/muse-image`) | 0.51 | 0.18 | |
| | Recraft (`v4.1`) | 0.46 | 0.14 | |
| | *Measurement noise floor* | *0.94* | *0.88* | |
|
|
|  |
|
|
| *An example from the benchmark. The ground truth is shown as is each model's |
| output. Only Vyb generates an output that is close to the ground truth.* |
|
|
| ### Color fidelity |
|
|
| To measure color fidelity, we pick 100 different colors. We ask each model to generate |
| a rectangle in the specified color, given as a hex code. We then calculate the |
| per-channel error for each of R, G, and B (out of 255) and report the maximum across |
| channels. |
|
|
| | Model | Mean | Worst 5% of cases | |
| |---|---|---| |
| | **Vyb 1.0** | **0.0** | **0.0** | |
| | ChatGPT Image (`gpt-image-2.5-sunburst`) | 3.9 | 8 | |
| | Muse (`meta/muse-image`) | 5.7 | 10 | |
| | Recraft (`v4.1`) | 27.2 | 56 | |
|
|
| Here we see that only Vyb reproduces every requested color exactly. |
|
|
| ### Edit fidelity |
|
|
| We provide every model the same starting design and the same edit prompt. |
| For Vyb, the design is provided in the layered SVG format it accepts. For |
| the leading image generation models, the SVG is rendered and provided in |
| the PNG format they accept. |
|
|
| For each model generation, we measure the fraction of pixels in the image |
| that were changed and have an LLM judge score whether the edit was performed |
| (acceptance rate). |
|
|
| We report the fraction of pixels changed (lower is better) and acceptance |
| rate (higher is better). |
|
|
| | Model | Pixels changed, mean | Worst 5% of cases | Acceptance rate | |
| |---|---|---|---| |
| | **Vyb 1.0** | **7.3%** | **31.4%** | 99.2% | |
| | ChatGPT Image (`gpt-image-2.5-sunburst`) | 95.5% | 99.7% | **100%** | |
| | Muse (`meta/muse-image`) | 91.9% | 99.6% | 98.0% | |
| | Recraft (`v4.1`) | 98.9% | 100% | 51.2% | |
|
|
|  |
|
|
| *An example of an edit where the request is to change the color |
| of the "30% OFF" text to coral pink. Models other than Vyb change unrelated parts of the image.* |
|
|
| ## Reproduce |
|
|
| To judge new outputs, you need a Gemini API key. |
|
|
| ``` |
| pip install google-genai |
| GEMINI_API_KEY=... python -m scoring.judge |
| ``` |
|
|
| You can rescore by running: |
|
|
| ``` |
| pip install numpy pillow scikit-image |
| python -m scoring |
| ``` |
|
|
| All designs are synthetic. They contain fictional brands with AI-generated |
| imagery. |
|
|
| Run: October 2026. |
|
|
| ## Add your model |
|
|
| We are happy to include your model. Create a discussion on this dataset with |
| your model's outputs (follow the `outputs/` layout, provide three attempts per |
| case). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{vybbench2026, |
| title = {VybBench 1.0: A Design Fidelity Benchmark}, |
| author = {{Coco Labs}}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/cocolabs/vybbench} |
| } |
| ``` |
|
|
