GPT Image 1.5vs.Wan 2.7 Image
Which one is better?
In our own test both are on par. Wan 2.7 Image has no arena rating yet. Wan 2.7 Image has no API price.
Same prompt, two images
Same task, the first image of each. The task with the biggest difference is preselected.
Result for “Product photography”:Wan 2.7 Image ahead
What the rating looks at: Materials, reflections, and fine details.
“Ahead”: better in all three rating rounds. “Slightly ahead”: a higher value, but not in every round.
Test protocol of the images
Both models receive the same archived prompt in exactly one request, and the first result counts. Files are normalized to 1,024 × 1,024 pixels without cropping. Provider resolution, randomness, quality tier and prompt processing can still differ. The images are a practical visual test, not a reconstruction of the arena configuration.
Character and detail
GPT Image 1.5: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:17 PM. Source
Wan 2.7 Image: fal, requested 1024x1024, n=1, seed=2030081785, estimated cost $0.030000, generated Nov 1, 2026, 9:22 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6
GPT Image 1.5 image SHA 256: 0dff52ccd39400ac66ac8870c9ea937512ff1474688e70118688f420c38c2296
Wan 2.7 Image image SHA 256: 62258e364ab7627aa6c1d0d4214ad1cc32287ede3b059aec041acf4dcdb20cf4
Infographic
GPT Image 1.5: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:17 PM. Source
Wan 2.7 Image: fal, requested 1024x1024, n=1, seed=1843752681, estimated cost $0.030000, generated Nov 1, 2026, 9:22 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f
GPT Image 1.5 image SHA 256: 1c9ff59c32621c8b48b002dacb0ae66487262280c7c0a5b3d6456c81b248ebe9
Wan 2.7 Image image SHA 256: f5707f29066b971a3c094495e0ff2a1bdd0479aa5c583a2a4d3b607ce7ca0636
Product photography
GPT Image 1.5: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 12:16 PM. Source
Wan 2.7 Image: fal, requested 1024x1024, n=1, seed=1918862783, estimated cost $0.030000, generated Nov 1, 2026, 9:21 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3
GPT Image 1.5 image SHA 256: 67cfa2f324447027ceb5f82e9446d40a1395b46ce425890a74606b4d97731cf6
Wan 2.7 Image image SHA 256: 6b7d5e90e75158d052d09c570b3af8fbb633ed1e20d9d84b8d23e864aeb446ac
Typography and layout
GPT Image 1.5: together, 1024x1024, n=1, seed=provider random, provider default steps, generated Aug 14, 2026, 1:59 PM. Source
Wan 2.7 Image: fal, requested 1024x1024, n=1, seed=457490918, estimated cost $0.030000, generated Nov 1, 2026, 9:21 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419
GPT Image 1.5 image SHA 256: 6a4cec86850fd371b65274198e2bf630b3cf36a096f9fb953d4bfe17c3222ebe
Wan 2.7 Image image SHA 256: 60d9602757484b80c9a30506243d50d74d1160ff6998e80ba21aeb99338f5587
The numbers at a glance
GPT Image 1.5
Rank 13 of 22
Wan 2.7 Image
not in the arena
GPT Image 1.5
Rank 43 of 74
Wan 2.7 Image
Rank 16 of 74
GPT Image 1.5
expensive
Wan 2.7 Image
no API price
Ranks among the 22 image models with an arena rating and the 74 with a GraduallyBench run; prices compared with 28 models with an API price.
GPT Image 1.5
- Subscription
- No OpenAI subscription includes this model. Through other providers from ≈ €6.39 a month (ElevenLabs Starter, with limited credits). 42 plans in Germany.
- Via API
- $0.13 per image, 100 images $13.30.
Wan 2.7 Image
- Subscription
- At Alibaba from ≈ €6.39 a month (Token Plan Individual Lite, with limited credits). Through other providers from €6.83 a month (Poe Basic, with limited points). 12 plans in Germany.
- Via API
- No API price known. Access: API.
- Weights
- unknown.
Subscriptions only count plans that name exactly this model. Subscription prices include VAT, API prices are in dollars.
Arena rating (LMArena) from Oct 28, 2026 · GraduallyBench from Nov 1, 2026 · API prices from Oct 28, 2026 · Subscription database from Sep 29, 2026 (34 days old)
All data side by side
The better value is bold. For ratings only when the gap is clear.
| Kennzahl | GPT Image 1.5 | Wan 2.7 Image |
|---|---|---|
| Ratings | ||
| Arena ratingfrom votes by users, higher is better | 1,236 | n/a |
| Arena rank | Rank 13 of 22 | n/a |
| Arena votesmore votes, a safer rating | 174,012 | n/a |
| GraduallyBenchour own rating, 0 to 100 | 54 | 65 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 58 | 74 |
| Product photographyMaterials, reflections, and fine details | 38 | 83 |
| Character and detailAnatomy, clothing, and spatial consistency | 64 | 46 |
| InfographicNumbers, labels, and visual organization | 56 | 56 |
| API price | ||
| Per imagelower is better | $0.13 | n/a |
| 100 images | $13.30 | n/a |
| Profile | ||
| Released | December 2025 | n/a |
| Weightsopen means: runs locally | proprietary | unknown |
| Lifecycle | Marked as deprecated by OpenAI; removed from the API on December 1, 2026 | no notice |
| Access | API, Web app | API |
| Parameters | not published | not published |
| Largest output | 1,536 × 1,024 pixels | 2K |
| Plans in Germanynaming exactly this model | 42 | 12 |
API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.
Sources: GPT Image 1.5 model source, status source (verified Aug 15, 2026) · Wan 2.7 Image model source (verified Nov 1, 2026)
Arena ratings by category
From blind votes on LMArena. “Ahead” means the lead is clear.
No shared arena score in the stored snapshot
The stored benchmark database contains no measurement for GPT Image 1.5 and Wan 2.7 Image from the same arena and category. Older model versions are not used as substitutes.
Compare another matchup
More matchups
Related image model comparisons
Compare GPT Image 1.5 and Wan 2.7 Image with other important image models.