MobileReality/mdma

Markdown Document with Mounted Applications
Interactive documents from Markdown. Built for next gen-apps
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🤗 Model
Why MDMA?
AI conversations today are plain text — the user reads a response and manually acts on it. MDMA changes that. When an LLM knows the MDMA spec, it can respond with interactive components (forms, tables, approval gates) instead of just text. The conversation becomes actionable: the user fills out a form, approves a step, or reviews structured data — all inline, with a predictable schema that your app already knows how to render and process.
No custom UI per use case. No parsing free-form text. The AI generates structured, validated components and your frontend renders them instantly.
What is MDMA?
MDMA extends Markdown with interactive components defined in fenced mdma code blocks. A regular Markdown file becomes an interactive application:
mdma
type: form
id: intake-form
fields:
- name: patient-name
type: text
label: "Full Name"
required: true
sensitive: true
- name: email
type: email
label: "Email"
required: true
sensitive: true
- name: reason
type: textarea
label: "Reason for Visit"
required: true
onSubmit: submit-intake
mdma
type: button
id: submit-btn
text: "Submit Intake Form"
variant: primary
onAction: submit
Speed comparison
Same scenario, two models. GPT-5.5 and our own hosted MDMA-IL model.
Our model is available on Hugging Face: MobileReality/mdma-gemma4-26b-dsl-unsloth-v1
| GPT-5.5 | Our own hosted model |
| :---: | :---: |
| | |
MDMA_AUTHOR prompt matrix
Each cell shows the pass rate of the model-specialized MDMA_AUTHOR prompt variant on the listed eval suite.
✅ 100% on the suite.
🟡 Scoring between 80–99% on the suite.
🔴 Scoring below 80% on the suite.
| Variant | one-shot | one-shot with custom prompt | conversation | specific flow of conversation |
| :--- | :---: | :---: | :---: | :---: |
| **OpenAI** | | | | |
| gpt-6.1-sol | ✅ | ✅ | ✅ | ✅ |
| gpt-6-sol | ✅ | ✅ | ✅ | ✅ |
| gpt-5.6-sol | ✅ | ✅ | ✅ | ✅ |
| gpt-5.6-terra | ✅ | ✅ | ✅ | ✅ |
| gpt-5.6-luna | ✅ | ✅ | ✅ | ✅ |
| gpt-5.5 | ✅ | ✅ | ✅ | ✅ |
| gpt-5.4 | ✅ | ✅ † | ✅ † | ✅ † |
| gpt-5.4-mini | ✅ | ✅ | ✅ \* | ✅ \* |
| gpt-5.4-nano | ✅ | ✅ | ✅ \* | ✅ \* |
| gpt-5.2 | ✅ | ✅ | ✅ | ✅ |
| gpt-5.1 | ✅ | ✅ | ✅ | ✅ |
| gpt-5 \[i] | ✅ | ✅ | ✅ | ✅ |
| gpt-5-mini \[i] | ✅ | ✅ | ✅ \* | ✅ \* |
| gpt-5-nano \[i] | ✅ | ✅ | 🟡 \* | 🟡 \* |
| gpt-4.1 | ✅ | ✅ | ✅ | ✅ |
| gpt-4.1-mini | ✅ | ✅ | ✅ \* | ✅ \* |
| gpt-4.1-nano | ✅ | ✅ | ✅ \* | 🟡 \* |
| **Anthropic** | | | | |
| claude-opus-5.5 | ✅ | ✅ | ✅ | ✅ |
| claude-opus-5 | ✅ | ✅ | ✅ | ✅ |
| claude-sonnet-5.5 § | ✅ | ✅ | ✅ | ✅ |
| claude-sonnet-5 | ✅ | ✅ | ✅ | ✅ |
| claude-haiku-5.5 | ✅ | ✅ | ✅ \* | ✅ \* |
| claude-opus-4.8 | ✅ | ✅ | ✅ | ✅ |
| claude-opus-4.7 | ✅ | ✅ | ✅ | ✅ |
| claude-opus-4.6 | ✅ | ✅ | ✅ | ✅ |
| claude-sonnet-4.6 | ✅ | ✅ | ✅ | ✅ |
| claude-haiku-4.5 | ✅ | ✅ | ✅ \* | ✅ \* |
| claude-fable-5 | ✅ | ✅ | ✅ | ✅ |
| **Google** | | | | |
| gemini-3.8-flash | ✅ | ✅ | ✅ | ✅ |
| gemini-3.7-flash | ✅ | ✅ | ✅ | ✅ |
| gemini-3.5-flash | ✅ | ✅ | ✅ | ✅ |
| gemini-3.1-pro-preview | ✅ | ✅ | ✅ | 🟡 ‡ |
| gemini-3.1-pro-preview-customtools | ✅ | ✅ | ✅ | ✅ |
| gemini-3.1-flash-lite-preview | ✅ | ✅ | ✅ \* | ✅ \* |
| gemini-3-flash-preview | ✅ | ✅ | ✅ \* | ✅ \* |
| gemini-2.5-pro | ✅ | ✅ | ✅ | ✅ |
| gemini-2.5-flash | ✅ | ✅ | ✅ \* | ✅ \* |
| gemini-2.5-flash-lite | ✅ | ✅ | ✅ \* | ✅ \* |
| **xAI** | | | | |
| grok-4.3 \[i] | 🟡 | 🔴 | 🔴 | 🔴 |
| grok-4.20 | ✅ | ✅ | ✅ | ✅ |
| grok-4.7 | ✅ | ✅ | ✅ | ✅ |
| grok-4.6 | ✅ | ✅ | ✅ | ✅ |
| grok-4.5 | ✅ | ✅ | ✅ | ✅ |
| **Zhipu (z.ai)** | | | | |
| glm-4-plus | TBD | TBD | TBD | TBD |
| **Moonshot** | | | | |
| kimi-k2 | TBD | TBD | TBD | TBD |
| **Alibaba** | | | | |
| qwen3-max | TBD | TBD | TBD | TBD |
| **MiniMax** | | | | |
| minimax-m1 | TBD | TBD | TBD | TBD |
| **Other** | | | | |
| *model* | — | — | — | — |
> **Don't see your model?** Add a prompt variant under packages/prompt-pack/src/prompts/mdma-author/ / and open a PR — we'll run the eval suite and add it to this table.
† **gpt-5.4 intermittent duplication bug** — gpt-5.4 passes one-shot evals reliably but shows a non-deterministic output duplication in multi-turn, custom-prompt, and flow evals (~7–15% of runs). The model generates a complete, correct response and then immediately re-emits the entire output verbatim, causing [duplicate-ids] validation errors. This is a known model-level issue unrelated to the prompt variant. See the OpenAI community thread for details. If this affects your use case, prefer gpt-5.5 or gpt-5.2.