HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908800908263424 |
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| author | Chan, Wing Allen, Richard |
| author_facet | Chan, Wing Allen, Richard |
| contents | Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per successful edit that combines model price with human review time. We release 50 public tasks and maintain a 50-task held-out private split for server-side evaluation, plus a standardized JSON schema and tooling for VLM and human-based judging. Across the evaluated models, per-attempt pass rates span 34-83 percent and effective cost per success spans USD 0.66-1.42. Models that have low per-image pricing are more expensive when you consider the total effective cost of retries and human reviews. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_00105 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models Chan, Wing Allen, Richard Computer Vision and Pattern Recognition Artificial Intelligence Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per successful edit that combines model price with human review time. We release 50 public tasks and maintain a 50-task held-out private split for server-side evaluation, plus a standardized JSON schema and tooling for VLM and human-based judging. Across the evaluated models, per-attempt pass rates span 34-83 percent and effective cost per success spans USD 0.66-1.42. Models that have low per-image pricing are more expensive when you consider the total effective cost of retries and human reviews. |
| title | HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2602.00105 |