microsoft/mai-image-2.5-flash/text-to-image

text-to-image

MAI-Image 2.5 Flash Text-to-Image API by MICROSOFT

microsoft/mai-image-2.5-flash/text-to-image

Text-to-image

Microsoft's fast, cost-optimized text-to-image generation model, creating high-quality images at lower cost using the same diffusion-based architecture as MAI-Image-2.5.

INPUT

Loading parameter configuration...

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.03 per run. For $10 you can run this model approximately 333 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parameters

Code Example
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "microsoft/mai-image-2.5-flash/text-to-image",
    "prompt": "A beautiful landscape with mountains and lake",
    "width": 512,
    "height": 512,
    "steps": 20,
    "guidance_scale": 7.5,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateImage

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "microsoft/mai-image-2.5-flash/text-to-image",
    "input": {
        "prompt": "A beautiful landscape with mountains and lake"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Response

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 0Required: 0Optional: 0

No parameters available.

Example Request Body

{
  "model": "microsoft/mai-image-2.5-flash/text-to-image"
}

Output Schema

The API returns a prediction response with the generated output URLs.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 300+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

Schema not available

No examples available

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MAI-Image-2.5-Flash Text-to-Image

MAI-Image-2.5-Flash is Microsoft's fast, cost-optimized text-to-image generation model, designed to create high-quality, visually rich images from natural language prompts at significantly lower cost than the standard MAI-Image-2.5. It uses the same diffusion-based generative approach, enabling strong alignment between the input text and the generated output, while being optimized for speed and throughput. Released on June 2, 2026.

Key Capabilities

Photorealistic image synthesis — Generates realistic imagery with consistent visual structure, accurate lighting, depth, and texture, suitable for concept visualization and professional content creation.
High-fidelity portraits — Produces expressive, natural-looking portraits with accurate facial structure, lighting, and skin texture.
Accurate text rendering — Improved rendering of legible text within generated images, including labels, posters, packaging, and signage.
Visual reasoning — Reasons across objects, scene structure, lighting, scale, and spatial positioning to produce consistent outputs even from ambiguous or complex prompts.
Product, branding & commercial design — Well suited for product imagery, marketing visuals, brand assets, and commercial creative workflows.
Creative concept visualization — Translates abstract textual descriptions into visually coherent and imaginative outputs.

Flash vs. Standard

Feature	MAI-Image-2.5	MAI-Image-2.5-Flash
Output image cost	$0.05 / image	$0.03 / image
Speed	Standard	Faster
Quality	Maximum fidelity	High quality, optimized
Best for	Premium production	High-volume, cost-sensitive

Flash is the recommended choice for high-volume workflows, rapid prototyping, and scenarios where speed and cost efficiency take priority over absolute maximum fidelity.

Pricing

Pricing is based on two components: the input text tokens in the prompt, and a fixed per-image output fee.

SKU	Description	Unit Price
`sku_input_1m_token`	Price per 1M input (prompt) tokens	$5.00
`sku_output_image`	Fixed fee per generated image	$0.03

Pricing Formula

cost = countTokens(prompt) / 1,000,000 × $5.00 + $0.03

For most prompts (a few hundred tokens), the token cost is negligible and the effective cost is approximately $0.03 per image — 40% lower than the standard MAI-Image-2.5.

Examples

Prompt Length	Token Count	Token Cost	Image Fee	Total
Short (e.g., 50 tokens)	50	~$0.000250	$0.03	~$0.0303
Medium (e.g., 500 tokens)	500	~$0.002500	$0.03	~$0.0325
Long (e.g., 2,000 tokens)	2,000	~$0.010000	$0.03	~$0.0400

For detailed pricing configuration, see models/microsoft/mai/price/microsoft-mai-image-2.5-flash-text-to-image.json.

Best Use Cases

High-volume generation — Batch generation of marketing assets, product variants, or dataset images.
Rapid prototyping — Quickly iterate on visual concepts before committing to full-quality renders.
Real-time applications — Interactive generation in products where response time matters.
Social media content — Fast-turnaround visuals for campaigns and posts.
E-commerce at scale — Generate large product image catalogs cost-effectively.
Development & testing — Test prompts and pipelines without incurring full production costs.

Pro Tips

Be specific about lighting, perspective, and style (e.g., "soft golden-hour lighting", "top-down view", "photorealistic").
Mention the subject first, then environment, then style and mood.
For text in images, keep inscriptions short and clearly stated in the prompt.
Use aspect ratios suited to your use case (portrait for people, landscape for scenes).
For maximum quality, consider upgrading to MAI-Image-2.5 for final production outputs.

Technical Specifications

Spec	Value
Model Developer	Microsoft AI
Release Date	June 2, 2026
Input Format	Text prompt (natural language)
Output Format	PNG image (base64-encoded)
Max Prompt Length	32,000 tokens
Max Output Resolution	1,048,576 total pixels (e.g., 1024×1024)
Supported Languages	English (primary)

MAI-Image-2.5-Flash Edit — Same Flash model with image-to-image editing capability.
MAI-Image-2.5 Text-to-Image — Full-quality text-to-image variant.
MAI-Image-2.5 Edit — Full-quality image editing variant.

MAI-Image 2.5 Flash Text-to-Image API by MICROSOFT

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response

Atlas Cloud Skills

Supported Clients

Install

Setup API Key

Capabilities

MCP Server

Supported Clients

Install

Configuration

Available Tools

API Schema

Please log in to view request history

MAI-Image-2.5-Flash Text-to-Image

Key Capabilities

Flash vs. Standard

Pricing

Pricing Formula

Examples

Best Use Cases

Pro Tips

Technical Specifications

Related Models

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One API for All Media AI.

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INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files