whatmodel.sh / image / Imagen 4 Ultra
Imagen 4 Ultra
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Imagen 4 Ultra is a closed-weights image model from Google, released June 2025. Licence: proprietary.
Score within the LMArena text-to-image comparison set only.
$ how it compares
The image models ranked either side of Imagen 4 Ultra, out of 197 with a score, then more from Google.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 39 | 62±1 | api $0.039/image | |||
| 40 | 62±2 | api $0.030/image | |||
| 41 | 61±2 | api $0.060/image | |||
| 42 | 61±1 | api $0.042/image | |||
| 43 | 61±3 | api $0.030/image | |||
| more from Google | |||||
| 5 | 81±2 | api $0.034/image | |||
| 9 | 76±1 | api $0.067/image | |||
| 10 | 76±1 | api $0.13/image | |||
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #34 of 126 · 94% of its score · 3 tests
Beats Wan 2.7 Image Pro, Wan 2.7 Image here, though they rank higher overall. Trails ERNIE-Image, ERNIE-Image-Turbo, GPT Image 1 here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GenExam (relaxed overall, GPT-5 judge) | 53.0% ✎ | ▄█▂▂▂▂▆▄·▂·▂ beats 10/16 | 55% |
| LMArena text-to-image | 1147.7 ✎ | ▂▂▃▄▇█▄▂▄▂·▂ beats 42/75 | 35% |
| Qwen-Image-Bench (Q-Judger, v1) | 52.0% ✎ | █▅▅·▅▅█·█··▃ beats 5/17 | 3.8% |
other tests1 not in a task · 5.5% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Rapidata Benchmark.ai image benchmark (overall Elo) | 1025.9 ✎ | ▁▁▁▄▂█▃▁▁▂▁▁ beats 23/34 | 5.5% |
$ where to run it
Through an API
api · Google Cloud · $0.060/image · cloud.google.com ↗ · price sourceUSD per generated image, Imagen 4 Ultra text-to-image on Google Cloud generative AI (Vertex AI / Gemini Enterprise Agent Platform) pricing; upscaling billed separately. Not the Gemini Developer API (its pricing page no longer lists Imagen). Product: Google Cloud generative AI image generation. API model: Imagen 4 Ultra. Settings: text prompt, one image. Price basis: published list rate. Currency: USD. Google Cloud official pricing page, observed 2026-09-25.
$ how the score was worked out · GenExam: A Multidisciplinary… 55%, LMArena leaderboard dataset 35%, Rapidata/text-to-image-human-… 5% and 1 more
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
| 1 | 55% | |
| 1 | 35% | |
| 1 | 5% | |
| 1 | 4% | |
| prior · pull toward the middle | 1% | |
| weighted average | = 61.4 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json