Single‑Pass Image Editing Showdown: Style Wins, Precision Still Hard

A head-to-head benchmark of six image-editing models across 12 single-pass, text-only tasks finds Seedream 4 best overall (9/12), with Gemini 2.5 Flash second (7/12). Models succeed more with stylized insertions and global changes than with precise spatial swaps or selective removals. The most error-prone behaviors include over-editing (especially by gpt-image-1), violating constraints, and failing to preserve texture.

Key Points

• Seedream 4 tops the benchmark (9/12), with Gemini 2.5 Flash second (7/12); OmniGen2 lags (1/12).
• Precise, localized, and spatially constrained edits (block position swaps, M&M filtering, Pisa straightening, neck shortening) remain the hardest.
• Models generally handle stylized insertions and global aesthetic changes better (e.g., Great Wave surfer, PAWS poster, Girl with a Pearl Earring lighting).
• OpenAI gpt-image-1 often alters entire scenes, hurting constraint adherence; Qwen-Image-Edit is strong for a locally hostable model but can overmodify.
• Texture and style preservation under targeted replacement (e.g., weathered sign, card suit change without touching the Ace) are frequent failure points.

Sentiment

The community is largely positive and appreciative of the hands-on comparison format. Most commenters engage constructively, sharing real-world experiences with the tested models, offering alternative model recommendations, and providing methodology suggestions. The OP's active participation and responsiveness to feedback elevates the discussion quality. While there are methodological critiques, they are delivered respectfully and the overall tone reflects genuine enthusiasm for the rapid progress in image editing capabilities.

In Agreement

• Models have improved dramatically for style-consistent image edits compared to earlier Stable Diffusion generations
• Seedream 4 deserves its top ranking for precision and adherence, though it introduces subtle color gradation changes
• The number-of-attempts metric is one of the most valuable and underappreciated aspects of the comparison, directly measuring real-world steerability
• Prompt engineering remains crucial—structured multi-step prompting can dramatically improve results even with capable models
• This practical, side-by-side comparison format is far more useful than synthetic benchmarks for evaluating image editing models

Opposed

• The methodology is weakened by varying prompts across models and showing only the best result; standardized prompts with fixed seeds would be more rigorous
• Specific prompt phrasing choices may have inadvertently disadvantaged some models, such as describing the tower as leaning in the prompt
• A pass/fail scoring system is too binary—a graduated scoring scale would better capture the nuances between model outputs
• Self-hosting economics rarely justify GPU purchases purely for image generation; most users are better served by API access