---
title: "Benchmark: Fine-Line Mandala"
description: "15 image models compared on the canonical \"Fine-Line Mandala\" prompt (Print Detail & Resolution)."
url: "https://img.ly/ai-benchmarks/prompts/r01-mandala/"
type: "benchmark-prompt"
category: "Print Detail & Resolution"
suite: "pilot-0"
---

> This is the markdown version of [Benchmark: Fine-Line Mandala](https://img.ly/ai-benchmarks/prompts/r01-mandala/). For all pages in one file, see [llms-full.txt](https://img.ly/llms-full.txt). For an index of all available pages, see [llms.txt](https://img.ly/llms.txt).

---

# Benchmark: Fine-Line Mandala

Category: Print Detail & Resolution.
Use cases: Print products (U2).
Suite: pilot-0. Every model runs the identical prompt with default
parameters and fixed seeds. No per-model tuning.

## Why this benchmark

Fine symmetrical linework is a torture test for native resolution and detail retention, and mandalas are a real print and coloring-book product. Uniform strokes with no gray shading make degradation obvious at output size.

## Canonical prompt

> Intricate symmetrical mandala line art, extremely fine linework, black ink on cream paper, uniform stroke weight, no gray shading.

## Scored assertions

- symmetrical mandala line art
- extremely fine, crisp linework
- black ink on cream paper
- uniform stroke weight with no gray shading

## Results by model

### FLUX.2

[Model profile](https://img.ly/ai-benchmarks/models/flux-2.md)

- seed 1111: 1024×768, 2.0s, $0.013, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 5.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 2.0 / 5
- seed 2222: 1024×768, 2.0s, $0.013, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 5.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 2.0 / 5
- seed 3333: 1024×768, 2.4s, $0.013, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 2.0 / 5

### FLUX.2 [pro]

[Model profile](https://img.ly/ai-benchmarks/models/flux-2-pro.md)

- seed 1111: 1024×768, 28.1s, $0.040, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2-pro--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 2.0 / 5
- seed 2222: 1024×768, 16.4s, $0.040, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2-pro--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 2.0 / 5
- seed 3333: 1024×768, 22.3s, $0.040, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2-pro--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 2.0 / 5

### FLUX.2 [dev] Turbo

[Model profile](https://img.ly/ai-benchmarks/models/flux-2-turbo.md)

- seed 1111: 1024×768, 2.1s, $0.015, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2-turbo--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 2.0 / 5
- seed 2222: 1024×768, 2.1s, $0.015, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2-turbo--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 2.0 / 5
- seed 3333: 1024×768, 2.0s, $0.015, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--flux-2-turbo--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 2.0 / 5

### Gemini 2.5 Flash Image

[Model profile](https://img.ly/ai-benchmarks/models/gemini-25-flash-image.md)

- sample 1: 1024×1024, 7.0s, $0.039, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--gemini-25-flash-image--n0.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 2: 1024×1024, 9.1s, $0.039, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--gemini-25-flash-image--n1.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 3.0 / 5
- sample 3: 1024×1024, 7.5s, $0.039, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--gemini-25-flash-image--n2.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 3.0 / 5

### GPT Image 1.5

[Model profile](https://img.ly/ai-benchmarks/models/gpt-image-1-5.md)

- sample 1: 1024×1024, 38.7s, $0.120, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--gpt-image-1-5--n0.webp)
  - Cost: 3.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 2: 1024×1024, 38.3s, $0.120, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--gpt-image-1-5--n1.webp)
  - Cost: 3.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 3: 1024×1024, 41.3s, $0.120, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--gpt-image-1-5--n2.webp)
  - Cost: 3.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5

### Ideogram 3.0

[Model profile](https://img.ly/ai-benchmarks/models/ideogram-v3.md)

- seed 1111: 1024×1024, 15.6s, $0.060, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--ideogram-v3--s1111.webp)
  - Cost: 3.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- seed 2222: 1024×1024, 19.1s, $0.060, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--ideogram-v3--s2222.webp)
  - Cost: 3.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 3.0 / 5
- seed 3333: 1024×1024, 17.5s, $0.060, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--ideogram-v3--s3333.webp)
  - Cost: 3.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5

### Luma Photon

[Model profile](https://img.ly/ai-benchmarks/models/luma-photon.md)

- seed 1111: 1536×1536, 21.2s, $0.019, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--luma-photon--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 4.0 / 5
- seed 2222: 1536×1536, 14.8s, $0.019, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--luma-photon--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 4.0 / 5
- seed 3333: 1536×1536, 17.0s, $0.019, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--luma-photon--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 4.0 / 5

### Nano Banana 2

[Model profile](https://img.ly/ai-benchmarks/models/nano-banana-2.md)

- sample 1: 1408×768, 13.4s, $0.100, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-2--n0.webp)
  - Cost: 3.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 2: 1408×768, 13.1s, $0.100, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-2--n1.webp)
  - Cost: 3.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 3: 1408×768, 11.9s, $0.100, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-2--n2.webp)
  - Cost: 3.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5

### Nano Banana 2 Lite

[Model profile](https://img.ly/ai-benchmarks/models/nano-banana-2-lite.md)

- sample 1: 1408×768, 3.6s, $0.020, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-2-lite--n0.webp)
  - Cost: 4.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 2: 1408×768, 5.8s, $0.020, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-2-lite--n1.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 3: 1408×768, 4.2s, $0.020, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-2-lite--n2.webp)
  - Cost: 4.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5

### Nano Banana Pro

[Model profile](https://img.ly/ai-benchmarks/models/nano-banana-pro.md)

- sample 1: 1024×1024, 24.7s, $0.360, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-pro--n0.webp)
  - Cost: 1.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 2: 1024×1024, 19.8s, $0.360, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-pro--n1.webp)
  - Cost: 1.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5
- sample 3: 1024×1024, 25.0s, $0.360, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--nano-banana-pro--n2.webp)
  - Cost: 1.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 3.0 / 5

### Qwen-Image

[Model profile](https://img.ly/ai-benchmarks/models/qwen-image.md)

- seed 1111: 1024×768, 8.6s, $0.030, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--qwen-image--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 2.0 / 5
- seed 2222: 1024×768, 5.2s, $0.030, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--qwen-image--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 2.0 / 5
- seed 3333: 1024×768, 7.2s, $0.030, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--qwen-image--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 2.5 / 5
  - Resolution: 2.0 / 5

### Recraft V3

[Model profile](https://img.ly/ai-benchmarks/models/recraft-v3.md)

- seed 1111: 1024×1024, 7.0s, $0.040, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--recraft-v3--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 3.0 / 5
- seed 2222: 1024×1024, 6.9s, $0.040, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--recraft-v3--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 3.0 / 5
- seed 3333: 1024×1024, 8.8s, $0.040, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--recraft-v3--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 3.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 3.0 / 5

### Seedream 4.5

[Model profile](https://img.ly/ai-benchmarks/models/seedream-4-5.md)

- seed 1111: 2048×2048, 12.1s, $0.048, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--seedream-4-5--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 5.0 / 5
- seed 2222: 2048×2048, 12.3s, $0.048, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--seedream-4-5--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 5.0 / 5
- seed 3333: 2048×2048, 13.8s, $0.048, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--seedream-4-5--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 2.0 / 5
  - Prompt Adherence: 5.0 / 5
  - Resolution: 5.0 / 5

### Seedream 5.0 Lite

[Model profile](https://img.ly/ai-benchmarks/models/seedream-5-lite.md)

- seed 1111: 2048×2048, 24.0s, $0.020, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--seedream-5-lite--s1111.webp)
  - Cost: 4.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 5.0 / 5
- seed 2222: 2048×2048, 46.5s, $0.020, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--seedream-5-lite--s2222.webp)
  - Cost: 4.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 5.0 / 5
- seed 3333: 2048×2048, 31.1s, $0.020, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--seedream-5-lite--s3333.webp)
  - Cost: 4.0 / 5
  - Latency: 1.0 / 5
  - Prompt Adherence: 3.8 / 5
  - Resolution: 5.0 / 5

### Stable Diffusion 1.5

[Model profile](https://img.ly/ai-benchmarks/models/stable-diffusion-1-5.md)

- seed 1111: 512×512, 3.8s, $0.010, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--stable-diffusion-1-5--s1111.webp)
  - Cost: 5.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 2.5 / 5
  - Resolution: 1.0 / 5
- seed 2222: 512×512, 2.0s, $0.010, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--stable-diffusion-1-5--s2222.webp)
  - Cost: 5.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 2.5 / 5
  - Resolution: 1.0 / 5
- seed 3333: 512×512, 2.1s, $0.010, [image](https://storage.googleapis.com/imgly-ai-benchmarks/r01-mandala--stable-diffusion-1-5--s3333.webp)
  - Cost: 5.0 / 5
  - Latency: 4.0 / 5
  - Prompt Adherence: 2.5 / 5
  - Resolution: 1.0 / 5

Result images: [https://img.ly/ai-benchmarks/prompts/r01-mandala/](https://img.ly/ai-benchmarks/prompts/r01-mandala/) (interactive grid with lightbox).

## Frequently asked questions

**Why am I seeing three samples per benchmark?**

Image generation is stochastic: the same prompt produces different images on every run, so a single sample measures luck, not ability. Every model runs each benchmark three times with fixed seeds (1111, 2222, 3333), or three unseeded samples where the API accepts no seed. Scores average all three samples, and the consistency benchmarks measure the variation itself.

**Do all AI models get the identical prompt?**

Yes. Every model receives the same prompt text with default parameters and no per-model tuning, so differences in output reflect the model, not prompt engineering. The suite is versioned and prompts are append-only, which keeps historical scores comparable.

**Which AI image model is best at print detail & resolution?**

The results on this page are scored on this exact prompt, so the strongest model for it is easy to spot; this benchmark sits in the Print Detail & Resolution category. Scores are per model version and reflect this benchmark only. For a ranking across every benchmark see the model rankings, and to weight the numbers by a specific job see the use-case pages.

**How are the benchmark scores calculated?**

Each image is scored 0 to 5 per criterion. Measured criteria (resolution, latency, cost, transparency, color accuracy) are computed automatically; quality criteria (text accuracy, prompt adherence, composition) are judged by an automated Claude vision tier against each prompt’s checklist. Page scores are unweighted means over all of a model’s runs in that scope. Blind expert-panel review has not run yet; the dataset is pilot-0.

**Can I use AI-generated images like these in production?**

Model output is a starting point, not a finished asset. Production work usually needs background removal, exact brand colors or editable text, none of which generation guarantees on every run. The IMG.LY AI Editor gives your users those controls to refine any model’s output to production quality.

**How often is the benchmark updated?**

The suite re-runs on notable model releases so the rankings stay current. The results shown are the pilot-0 dataset, scored by measured criteria plus an automated Claude vision tier, with blind expert review planned. Prompts are versioned and append-only, so scores stay comparable across runs.


---

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