Model guide · OpenMOSS
MOSS-Image vs GPT Image 2: benchmarks and API access
In OpenMOSS's tests, GPT Image 2 leads on RealLayoutArena end-to-end design, while MOSS-Image leads on 7Bench object placement. See what each test measures, how MOSS-Image layout prompts work, and where its API is available.
We do not support adult, explicit, privacy-invasive, or unsafe image generation. Blocked requests do not use credits. See Acceptable Use.
What is MOSS-Image?
OpenMOSS released MOSS-Image-Base and MOSS-Image-SFT on October 16, 2026. They are text-to-image models built around a 5B-parameter backbone. The official project highlights readable Chinese and English text, long prompts and experimental control over the position of text and objects. The weights are available under Apache 2.0.
The SFT version is tuned for more organized text and layout results. A prompt can be ordinary language, a sequence of tags, or a JSON layout describing a scene and the items within it. Layout boxes guide generation; they are not pixel-perfect guarantees, so text and placement still need visual review.
What the official benchmarks show
OpenMOSS evaluated the models on different tasks. Its technical report puts GPT Image 2 ahead on end-to-end graphic design, while MOSS-Image scores higher on a separate layout-control test. These are OpenMOSS-run benchmarks, not results from the Vismuse form above.
| Evaluation | MOSS-Image | GPT Image 2 |
|---|---|---|
| RealLayoutArena: human preference for complete poster, infographic and UI results | 1,698.70 Elo | 1,844.35 Elo |
| 7Bench: object-placement Layout AUC | 0.7031 | 0.1554 |
RealLayoutArena used 61 short design requests and compared complete generation systems: MOSS-Image used its Layout PE planner, while GPT Image 2 used its own prompt-processing pipeline. The 7Bench test used 224 prompts with target object boxes and measured spatial placement without MOSS's planner. The report counted 22 GPT Image 2 safety-blocked requests as zero in the 7Bench score. The two scores answer different questions and should not be treated as an overall quality ranking.
MOSS-Image also accepts its own JSON layout format for controlling text and object positions. Its current backbone is text-to-image only; the report says it does not directly edit or condition on an input image. This page's GPT Image 2 form does not implement the MOSS-specific layout contract.
MOSS-Image use cases
- Bilingual graphics: ask for a sign, poster or product concept with specific Chinese and English words.
- Planned compositions: define the intended relationship between a title, subject and supporting objects.
- Illustration experiments: explore a visual style while keeping the brief and visible copy explicit.
Check every finished image for misspelled words, missing objects and shifted layout. The model announcement describes capabilities, not a guarantee that every requested line will render exactly.
MOSS-Image prompts and a GPT Image 2 alternative
OpenMOSS's user guide supports natural language, tags and structured JSON. For text rendering, it recommends writing intended line breaks directly in the prompt. English has the most training data, although Chinese is supported too.
For the GPT Image 2 form above, use a plain-language brief rather than MOSS-Image's model-specific JSON coordinates:
Create a single vertical café poster. Place the exact headline “SLOW MORNINGS” in large cream lettering at the top. Below it, show a ceramic coffee cup on a dark wooden table in soft window light. Leave generous space around the headline. Use a warm brown and muted blue palette. Do not add any other words, logos or watermarks.
For MOSS-Image itself, OpenMOSS documents a structured layout format with a scene summary and text or object items. Use its official user guide for the current schema and examples.
MOSS-Image API availability
OpenMOSS provides model weights and a demo. Its repository also announces availability on the MOSI API Platform, and fal lists a hosted MOSS-Image-SFT text-to-image API. Vismuse has not integrated either MOSS-Image API. The form on this page is an alternative for creating an image now; its fixed model label and credit quote appear before you submit.
When MOSS-Image is available here, this page will need an updated model label, examples made with the actual model, and a verified generation workflow. Until then, no output from this form should be described as a MOSS-Image result.
MOSS-Image questions
Does this form use MOSS-Image?
No. Vismuse has not connected MOSS-Image yet. The form on this page currently sends image requests to GPT Image 2, which is named in the form before submission.
What is MOSS-Image?
MOSS-Image is an open text-to-image model family from OpenMOSS. The Base and SFT releases support natural-language prompts and structured JSON layouts for text and object placement.
Can I use MOSS-Image on Vismuse today?
Not yet. You can read about the model here and use the clearly labeled GPT Image 2 alternative. OpenMOSS also links to its official demo and model weights.
Can I paste a MOSS-Image JSON layout into this form?
This form uses GPT Image 2, so MOSS-specific JSON coordinates are not supported as a layout contract. Describe the intended text, placement and composition in ordinary language instead.