# MAI-Image 2.5 Pro Edit — Atlas Cloud API

> Microsoft AI's highest-fidelity image-to-image editing model, making surgical, instruction-driven edits to existing images while preserving composition, material realism, and subject identity.

This is the machine-readable API reference for **MAI-Image 2.5 Pro Edit** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `microsoft/mai-image-2.5-pro/edit`
- **Built by**: MICROSOFT
- **Modality**: Image
- **Model page**: https://www.atlascloud.ai/models/microsoft/mai-image-2.5-pro/edit
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.131 per image
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

export ATLASCLOUD_API_KEY="your-api-key"
```

Then ask in plain English; the agent calls `atlas_generate_image` with `model: "microsoft/mai-image-2.5-pro/edit"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate image microsoft/mai-image-2.5-pro/edit -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `microsoft/mai-image-2.5-pro/edit`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"microsoft/mai-image-2.5-pro/edit"`

- **`prompt`** (`string`, _required_):
  Text prompt describing the edit to perform on the image. Maximum context length: 32,000 tokens.

- **`image`** (`string`, _required_):
  The reference image to edit. Can be a URL or a base64-encoded image string. Must be in JPEG or PNG format.

- **`enable_base64_output`** (`boolean`, _optional_):
  If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.
  - Default: `false`

- **`enable_sync_mode`** (`boolean`, _optional_):
  If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "microsoft/mai-image-2.5-pro/edit",
  "prompt": "",
  "image": ""
}
```


**Full Example**:

```json
{
  "model": "microsoft/mai-image-2.5-pro/edit",
  "prompt": "",
  "image": "",
  "enable_base64_output": false,
  "enable_sync_mode": false
}
```


### Output Schema

The API returns the following output format:


- **`code`** (`integer`, _optional_):
  HTTP status code of the response.

- **`message`** (`string`, _optional_):
  Human-readable message; non-empty on failure.

- **`data`** (`object`, _optional_):
  - Properties:
    - **`id`** (`string`, _optional_):
      Unique identifier for the prediction.

    - **`model`** (`string`, _optional_):
      Model ID used for the prediction.

    - **`outputs`** (`array[string]`, _optional_):
      Array of URLs to the generated content. Null when status is not completed.

    - **`urls`** (`object`, _optional_):
      Object containing related API endpoints.
      - Properties:
        - **`get`** (`string`, _optional_):
          URL to poll for the prediction result.


    - **`status`** (`string`, _optional_):
      Status of the task: created, processing, completed, timeout, or failed.

    - **`created_at`** (`string`, _optional_):
      ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

    - **`error`** (`string`, _optional_):
      Error message if the task failed, empty string otherwise.

    - **`error_code`** (`integer`, _optional_):
      Error code if the task failed.

    - **`executionTime`** (`number`, _optional_):
      Total execution time in milliseconds.

    - **`timings`** (`object`, _optional_):
      Detailed timing breakdown.
      - Properties:
        - **`inference`** (`number`, _optional_):
          Inference time in milliseconds.





**Example Response**:

```json
{
  "code": 0,
  "message": "",
  "data": {
    "id": "",
    "model": "",
    "outputs": [
      ""
    ],
    "urls": {
      "get": ""
    },
    "status": "",
    "created_at": "",
    "error": "",
    "error_code": 0,
    "executionTime": 0,
    "timings": {
      "inference": 0
    }
  }
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "microsoft/mai-image-2.5-pro/edit",
  "prompt": "",
  "image": "",
  "enable_base64_output": false,
  "enable_sync_mode": false
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/microsoft/mai-image-2.5-pro/edit)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

#### MAI-Image-2.5-Pro Edit (Image-to-Image)

**MAI-Image-2.5-Pro Edit** is Microsoft AI's highest-fidelity image model applied to image-to-image editing — precise, controllable changes to an existing image driven by a natural language instruction. It uses a diffusion-based generative approach that progressively refines the image, producing strong alignment between the instruction and the result while preserving the original composition and layout. Compared with the standard MAI-Image-2.5, the Pro tier is tuned for **robust object consistency, stronger visual reasoning, and deeper world knowledge**, so edits hold up in visually dense scenes and across repeated iterations on the same asset.

Released in public preview on **July 23, 2026** (model version `2026-06-19`), it is the premium tier of the MAI-Image-2.5 family, alongside the balanced base model and the cost-efficient Flash variant.

#### Key Capabilities

- **Surgical object editing** — Remove, replace, recolor, or reposition a specific element without disturbing the rest of the image.
- **Object consistency across complex scenes** — Objects retain the same identity, materials, proportions, markings, and orientation throughout a visually dense composition, so an edit in one region doesn't destabilize another.
- **Character consistency across views and moments** — A person or character stays recognizably the same across poses, camera angles, expressions, clothing, and lighting conditions.
- **Material and physical-property accuracy** — Edited materials keep behaving according to their real-world properties, including reflection, translucency, weight, texture, and deformation.
- **Spatial and geometric reasoning** — Inserted or moved objects sit coherently in three-dimensional space, with credible scale, perspective, occlusion, and structural relationships.
- **Inpainting & attribute changes** — Fill in missing regions, remove unwanted content, and change attributes of existing subjects seamlessly.
- **In-image

_(Description truncated. Full text on the model page.)_

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/microsoft/mai-image-2.5-pro/edit · Docs: https://www.atlascloud.ai/docs