OpenAI's previous generation image model with higher quality than DALL-E 2 and support for larger resolutions
Details
dall-e-3
Starting from
By quality
Prices shown are in USD
See all providersProviders & Pricing (2)
DALL-E 3 is available from 2 providers, with per-image pricing starting at $0.04 through OpenAI.
openai/dall-e-3
Output
Pricing Notes (3)
- • Deprecated model - will stop being supported on May 12, 2026
- • Pricing is per image, varying by quality (standard/hd) and size
- • Text-to-image generation only (no image editing)
replicate/dall-e-3
DALL-E 3 API OpenAI-compatible
Integrate DALL-E 3 into your application through Lumenfall’s OpenAI-compatible API to generate high-quality digital images and artwork from natural language descriptions.
https://api.lumenfall.ai/openai/v1
dall-e-3
Code Examples
Text to Image
/v1/images/generationscurl -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-3", "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-3', prompt: '', size: '1024x1024'});// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }console.log(response.data[0].url);from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.lumenfall.ai/openai/v1")response = client.images.generate( model="dall-e-3", prompt="", size="1024x1024")# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }print(response.data[0].url)Parameter Reference
Core Parameters
| Parameter | Type | Description | Modes |
|---|---|---|---|
prompt
|
string | Required. Text prompt for image generation |
T2I
|
style
|
string |
Image style
natural
vivid
|
T2I
|
Size & Layout
| Parameter | Type | Description | Modes |
|---|---|---|---|
size
|
string |
Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9")
1254x836
836x1254
1024x1024
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
|
aspect_ratio
|
string |
Aspect ratio of the output image (e.g. "16:9", "1:1")
2:3
1:1
3:2
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
|
resolution
|
string |
Output resolution tier (e.g. "1K", "4K")
1K
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
|
1K 3 sizes
| Output |
size
|
aspect_ratio
+
resolution
|
|
|---|---|---|---|
| 1024 × 1024 | "1024x1024" |
or |
"1:1"
+
"1K"
|
| 836 × 1254 | "836x1254" |
or |
"2:3"
+
"1K"
|
| 1254 × 836 | "1254x836" |
or |
"3:2"
+
"1K"
|
How these parameters work
size
Exact pixel dimensions
"1920x1080"
aspect_ratio
Shape only, default scale
"16:9"
resolution
Scale tier, preserves shape
"1K"
Priority when combined
size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.
How matching works
7:1 on a model with
4:1 and 8:1,
you get 8:1.
0.5K 1K 2K 4K)
or megapixel tiers (0.25 1).
If the exact tier isn't available, you get the nearest one.
Output & Format
| Parameter | Type | Description | Modes |
|---|---|---|---|
response_format
|
string |
How to return the image
url
b64_json
Default:
"url" |
T2I
|
output_format
|
string |
Output image format
png
jpeg
gif
webp
avif
Gateway converts to requested format if provider doesn't support it natively.
|
T2I
|
output_compression
|
integer | Compression level for lossy formats (JPEG, WebP, AVIF) |
T2I
|
n
|
integer |
Number of images to generate
Default:
1Gateway generates multiple images in parallel even if provider only supports 1.
|
T2I
|
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.
Gallery
View all 4 imagesDALL-E 3 FAQ
How much does DALL-E 3 cost?
DALL-E 3 starts at $0.04 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.
How do I use DALL-E 3 via API?
You can use DALL-E 3 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "dall-e-3". Code examples are available in Python, JavaScript, and cURL.
Which providers offer DALL-E 3?
DALL-E 3 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 3?
DALL-E 3 supports images up to 1792x1792 resolution.
Overview
DALL-E 3 is a text-to-image generation model developed by OpenAI that focuses on precise prompt adherence and complex scene composition. It is designed to interpret nuanced instructions without the need for complex “prompt engineering,” natively supporting various aspect ratios and higher resolutions than its predecessor, DALL-E 2. A defining characteristic of this model is its deep integration with large language models to refine user queries into detailed visual descriptions.
Strengths
- Prompt Adherence: The model excels at following complex, multi-part instructions, accurately placing specific objects in relation to one another as described in the text.
- Text Rendering: Unlike many earlier diffusion models, DALL-E 3 can reliably generate legible text, signs, and labels within images.
- Contextual Understanding: It handles nuanced requests involving specific artistic styles, historical periods, or lighting conditions with higher fidelity than previous iterations.
- Compositional Logic: It demonstrates a strong grasp of spatial reasoning, such as “a small blue cube sitting on top of a large red sphere,” reducing the frequency of floating or merged objects.
Limitations
- Photorealism Constraints: While capable of high-quality output, it may sometimes produce images with a “polished” or “rendered” aesthetic that lacks the organic imperfection found in models specifically tuned for hyper-realism.
- Human Anatomy: Like many generative models, it can occasionally struggle with the fine details of human hands, fingers, and complex joint positions in crowded or high-action scenes.
- Generation Speed: Due to the complexity of the model and its alignment process, generation times are generally slower compared to smaller or more optimized latent diffusion models.
Technical Background
DALL-E 3 is built upon a diffusion-based architecture that utilizes a highly descriptive captioning system. During training, OpenAI used a visual-language model to re-generate captions for the training dataset, resulting in a model that associates visual patterns with much more specific and detailed linguistic descriptions than models trained on raw alt-text. This bridge between the language and vision domains allows the model to process long, descriptive paragraphs of input text effectively.
Best For
DALL-E 3 is ideal for creative brainstorming, generating marketing assets with embedded text, and producing illustrative content where specific placement of elements is critical. It is well-suited for users who prefer natural language descriptions over technical parameter tuning. You can experiment with DALL-E 3 alongside other leading vision models through Lumenfall’s unified API and interactive playground to compare output styles and consistency.
Try DALL-E 3 in Playground
Generate images with custom prompts — no API key needed.