OpenAI

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DALL-E 2

Name: DALL-E 2
Brand: OpenAI
Price: 0.016 USD
Availability: InStock

AI Image Editing Model

Image Deprecated

OpenAI's legacy image generation model supporting generations, edits with masks (inpainting), and variations

Try Model

1024 x 1024

Max Resolution

Max Images per Request

Supported Modes

Text to Image Image Edit

Deprecated

Details

Model ID

dall-e-2

Creator

OpenAI

Family

dall-e

Released

April 2022

Sunset

May 12, 2026

Ready to integrate?

Access dall-e-2 via our unified API.

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Available at 2 providers

Starting from

$0.016 /image via OpenAI · +1 more

Popular formats

256×256

~$0.016

512×512

~$0.018

1024×1024

~$0.020

Prices shown are in USD

See all providers

Providers & Pricing (2)

DALL-E 2 is available from 2 providers, with per-image pricing starting at $0.016 through OpenAI.

OpenAI

Text to Image Image Edit

openai/dall-e-2

Provider Model ID: dall-e-2

Output

Image 1024x1024

$0.020 per image

Image 256x256

$0.016 per image

Image 512x512

$0.018 per image

Pricing Notes (3)

• Deprecated model - will stop being supported on May 12, 2026
• Pricing is per image, varying by size
• Supports generations, edits with masks (inpainting), and variations

View official pricing

Replicate

Text to Image

replicate/dall-e-2

Provider Model ID: openai/dall-e-2

$0.020 /image

View pricing details

DALL-E 2 API OpenAI-compatible

Lumenfall provides an OpenAI-compatible API to generate images, create variations, and perform mask-based inpainting using the DALL-E 2 model. Developers can programmatically produce 1024x1024 visuals and execute image-to-image edits through a single unified endpoint.

Base URL

https://api.lumenfall.ai/openai/v1

Model

dall-e-2

Code Examples

Text to Image

/v1/images/generations

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/generations \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -H "Content-Type: application/json" \

  -d '{

    "model": "dall-e-2",

    "prompt": "",

    "size": "1024x1024"

}'

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.generate({

  model: 'dall-e-2',

  prompt: '',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.generate(

    model="dall-e-2",

    prompt="",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Image Edit

/v1/images/edits

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/edits \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -F "model=dall-e-2" \

  -F "[email protected]" \

  -F "prompt=Add a starry night sky to this image" \

  -F "size=1024x1024"

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

import fs from 'fs';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.edit({

  model: 'dall-e-2',

  image: fs.createReadStream('source.png'),

  prompt: 'Add a starry night sky to this image',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.edit(

    model="dall-e-2",

    image=open("source.png", "rb"),

    prompt="Add a starry night sky to this image",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Parameter Reference

Required Supported Not available

Core Parameters

Parameter	Type	Description	Modes
`prompt`	string	Required. Text prompt for image generation	T2I Edit

Size & Layout

Parameter	Type	Description	Modes
`size`	string	Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9") WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.	T2I Edit
`aspect_ratio`	string	Aspect ratio of the output image (e.g. "16:9", "1:1") Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.	T2I Edit
`resolution`	string	Output resolution tier (e.g. "1K", "4K") Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.	T2I Edit

size

Exact pixel dimensions

"1920x1080"

aspect_ratio

Shape only, default scale

"16:9"

resolution

Scale tier, preserves shape

"1K"

Priority when combined

size › aspect_ratio + resolution › aspect_ratio › resolution

size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.

How matching works

Shape matching – we pick the closest supported ratio. Ask for 7:1 on a model with 4:1 and 8:1, you get 8:1.

Scale matching – providers use different tier formats: K tiers (0.5K 1K 2K 4K) or megapixel tiers (0.25 1). If the exact tier isn't available, you get the nearest one.

Dimension clamping – if a model has pixel limits, we clamp dimensions to fit and keep the aspect ratio intact.

Media Inputs

Parameter	Type	Description	Modes
`image`	file	Required. Input image(s) to edit Supports PNG, JPEG, WebP.	T2I Edit

Output & Format

Parameter	Type	Description	Modes
`response_format`	string	How to return the image `url` `b64_json` Default: `"url"`	T2I Edit
`output_format`	string	Output image format `png` `jpeg` `gif` `webp` `avif` Gateway converts to requested format if provider doesn't support it natively.	T2I Edit
`output_compression`	integer	Compression level for lossy formats (JPEG, WebP, AVIF)	T2I Edit
`n`	integer	Number of images to generate Default: `1` Gateway generates multiple images in parallel even if provider only supports 1.	T2I Edit

Parameter Normalization

How we handle parameters across different providers

Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:

Behavior	What happens	Example
`passthrough`	Sent as-is to the provider	style, quality
`renamed`	Same value, mapped to the field name the provider expects	prompt
`converted`	Transformed to the provider's native format	size
`emulated`	Works even if the provider has no concept of it	n, response_format

Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.

Full API Reference Authentication, endpoints, and more

Gallery

View all 4 images

DALL-E 2 FAQ

How much does DALL-E 2 cost?

DALL-E 2 starts at $0.016 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.

How do I use DALL-E 2 via API?

You can use DALL-E 2 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "dall-e-2". Code examples are available in Python, JavaScript, and cURL.

Which providers offer DALL-E 2?

DALL-E 2 is available through OpenAI and Replicate on Lumenfall. Lumenfall automatically routes requests to the best available provider.

What is the maximum resolution for DALL-E 2?

DALL-E 2 supports images up to 1024x1024 resolution.

Overview

DALL-E 2 is a legacy text-to-image diffusion model developed by OpenAI that generates images from natural language descriptions. While succeeded by newer iterations, it remains a stable benchmark for image synthesis, offering a distinct feature set that includes image-to-image variations and mask-based inpainting. It is particularly known for its ability to combine disparate concepts and objects in a coherent, albeit often stylized, visual manner.

Strengths

Image Inpainting: The model excels at modifying existing images through masking, allowing users to replace specific elements or extend backgrounds while maintaining the original image’s context and lighting.
Concept Blending: It demonstrates a strong capability for semantic synthesis, such as placing a 3D-rendered character in a real-world setting or applying specific artistic styles (e.g., “in the style of Van Gogh”) to original subjects.
Compositional Understanding: DALL-E 2 handles spatial relationships and object attributes with reasonable accuracy, ensuring that adjectives are generally applied to the correct nouns within a prompt.
Variation Generation: It can ingest an existing image and output multiple visual permutations that retain the original’s core theme and color palette without being exact copies.

Limitations

Low Resolution: Native output is limited to 1024x1024 pixels, which often lacks the fine-grained texture and sharp detail found in more modern models like DALL-E 3 or Midjourney.
Text Rendering: The model struggle significantly with rendering legible text; characters often appear as nonsensical glyphs or blurred artifacts.
Photorealism Constraints: Compared to newer latent diffusion models, DALL-E 2 often produces images with a “plastic” or overly smooth aesthetic, struggling with complex human anatomy like hands or eyes.

Technical Background

DALL-E 2 is built on a CLIP-guided diffusion architecture, specifically a process OpenAI refers to as “unCLIP.” It uses the CLIP (Contrastive Language-Image Pre-training) latent space to translate text embeddings into image embeddings, which a decoder then converts into a visual representation. This approach prioritizes the relationship between visual concepts and their linguistic descriptions over raw pixel-mapping.

Best For

DALL-E 2 is best suited for rapid prototyping, creating stylized illustrations, and performing basic image editing tasks like inpainting or outpainting where high-fidelity photorealism isn’t the primary requirement. It is a cost-effective choice for developers who need consistent, programmatic image variations.

This model is available for testing and integration through Lumenfall’s unified API and interactive playground, allowing you to compare its outputs directly against more recent generative models.

Try DALL-E 2 in Playground

Generate images with custom prompts — no API key needed.

Open Playground