whatmodel.sh / image / Grok Imagine Image Pro
Grok Imagine Image Pro
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Grok Imagine Image Pro is a closed-weights image model from SpaceXAI (xAI).
Score within the LMArena text-to-image comparison set only.
$ how it compares
The image models ranked either side of Grok Imagine Image Pro, out of 197 with a score.
| # | model | score | where |
|---|---|---|---|
| 26 | 66±2 | api $0.020/image | |
| 27 | 65±2 | api $0.10/image | |
| 28 | 65±2 | api $0.050/image | |
| 29 | 65±2 | api $0.035/image | |
| 30 | 64±1 | api $0.040/image |
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #24 of 126 * · 82% of its score · 1 test
Trails FLUX.2 [max], Recraft V4.1 Utility Pro here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena text-to-image | 1162.4 ✎ | ▂▂▃▄▇█▄▂▄▂·▂ beats 51/75 | 82% |
image to image / editing▁▁·▃▂▃█▃▃▄▁▁ #12 of 62 * · 3.5% of its score · 1 test
Beats Reve 2.0, Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image), GPT Image 1.5 here, though they rank higher overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| TECCI human overall success rate (IRCS+GGIS) | 20.0% ✎ | ▄········▄█▄ beats 3/4 | 3.5% |
other tests1 not in a task · 13% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Rapidata Benchmark.ai image benchmark (overall Elo) | 1009.7 ✎ | ▁▁▁▄▂█▂▁▁▂▁▁ beats 20/34 | 13% |
$ where to run it
Through an API
api · xAI · $0.050/image · docs.x.ai ↗ · price sourceUSD per generated image on the xAI API (output $0.05 per image). Product: xAI API image generation. API model: grok-imagine-image-quality; xAI's model page lists grok-imagine-image-pro as an alias of this model (with grok-imagine-image-quality-20260403 and -latest). Settings: one image. Price basis: published per-image list rate. xAI's pages show no end date; LiteLLM @c19ce71 records a 2026-11-02 deprecation date for this model (not confirmed on an xAI page). Currency: USD. xAI official models and pricing page, observed 2026-09-26.
$ how the score was worked out · LMArena leaderboard dataset 82%, Rapidata/text-to-image-human-… 13%, TECCI: Tricky Edits of Collec… 3%
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 | 82% | |
| 1 | 13% | |
| 1 | 3% | |
| prior · pull toward the middle | 2% | |
| weighted average | = 64.7 |
$ 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