Ming-Image 0.1 Design
A solid pick for self-hosting and full data control.
Overview
Ming-Image 0.1 Design is a 6-billion-parameter text-to-image model released on 22 September 2026 by inclusionAI, Ant Group's AI lab. It targets a specific niche: visuals where text matters, such as UI mockups, landing pages, infographics and posters. It composes the whole page, headings and blocks included, where most image generators still garble lettering. It can output transparent-background (RGBA) images when the prompt starts with one of the vendor's recommended phrases, which gives cut-out elements ready for layout work. Generation goes up to 2048 pixels. The vendor recommends a prompt-rewriting step with a language model (Ling-3.0-flash-VL or a Qwen) and suggests vLLM-Omni for serving. Weights are open under MIT and cleared for commercial use. The Ming-Image GitHub repository also ships agent skills, including one that rebuilds a generated slide as an editable PowerPoint. The model is also offered for free through OpenRouter, but this page treats it as self-hosted: routing through a third-party API changes where your data goes. It has no score yet in LMArena's text-to-image arena.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Posters and social visuals with text
- UI mockups and landing pages
- Cut-out graphic elements for layout work
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/ming-image-0-1-design)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
4/8 documented · 2/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 3.4 GB | 6 GB | Any recent PC/Mac |
| Q8 · balanced | 6.4 GB | 9 GB | 16GB PC / M1+ Mac / 8GB GPU |
| FP16 · max quality | 12 GB | 15 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve inclusionAI/Ming-Image-0.1-Design \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |