How much does DALL-E 3 cost via Railwail?

Per-call: €4.00. No monthly minimum, no subscription. Start with €5 free credits.

What is the context window of DALL-E 3?

DALL-E 3 supports a unknown context window — enough for typical AI workloads.

How fast is DALL-E 3?

Average response latency: 15.0s (p50 across recent Railwail traffic). See live p50/p95 metrics on /rankings.

Is DALL-E 3 better than Flux 1.1 Pro Ultra?

It depends on your use case. DALL-E 3 (OpenAI) and Flux 1.1 Pro Ultra (Black Forest Labs) are both strong choices in image generation. Compare them side-by-side at /compare/dall-e-3-vs-flux-pro-ultra.

DALL-E 3

Name: DALL-E 3
Brand: OpenAI
SKU: dall-e-3
Price: 4 EUR
Availability: InStock

OpenAI

Image Generation

OpenAI's latest image generation model. Excellent at following complex prompts with high fidelity.

Generate with DALL-E 3

Describe what you want and pick a size — the image renders inline.

Size

Result appears here

TL;DR·Last updated March 4, 2026

DALL-E 3 is image generation AI model from OpenAI, priced at €0.000 per 1M input tokens with a unknown context window.

Try DALL-E 3

Prompt

Aspect Ratio

Quality

Examples

See what DALL-E 3 can generate

Sample output

Fantasy Illustration

Prompt: "A majestic dragon perched atop a crumbling medieval castle tower, wings spread wide against a stormy sky with lightning, epic fantasy art style with rich detail and dramatic lighting"

Sample output

Product Mockup

Prompt: "A sleek wireless earbud case floating against a clean white background with soft shadows, product photography style, the case is matte black with a subtle LED indicator light"

Sample output

Surreal Art

Prompt: "A giant vintage pocket watch melting over the edge of a floating island in the sky, surrounded by clouds and tiny hot air balloons, Salvador Dali inspired surrealism with hyperrealistic rendering"

Pricing

Price per Generation

Per generation€4.00

API Integration

Use our OpenAI-compatible API to integrate DALL-E 3 into your application.

Install

npm install railwail

JavaScript / TypeScript

import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const images = await rw.run("dall-e-3", "A beautiful sunset over Tokyo");
console.log(images[0].url);

// Or use the image() method for full control
const res = await rw.image("dall-e-3", "A cat in space", {
  size: "1024x1024",
  n: 1,
});
console.log(res.data[0].url);

Specifications

Price

€4.00

Avg. latency

15.0s

Est. duration

15s

Developer

OpenAI

Deep dive — OpenAI's DALL-E 3

About OpenAI

Founded 2015 · San Francisco, USA

OpenAI was founded in December 2015 by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, Wojciech Zaremba and John Schulman as a non-profit research lab with a $1B funding pledge. Restructured as a capped-profit company in 2019, OpenAI introduced its DALL-E text-to-image family beginning with DALL-E (Jan 2021), DALL-E 2 (April 2022) and DALL-E 3 (Sept-Oct 2023). DALL-E 3 was developed in close collaboration with Microsoft and built into ChatGPT and Bing Image Creator. The DALL-E team includes Aditya Ramesh, Prafulla Dhariwal, Mark Chen and Gabriel Goh. OpenAI is led by CEO Sam Altman and is majority-funded by Microsoft (>$13B invested), with a 2025 valuation north of $150B.

Visit OpenAI →

Architecture

Latent diffusion model with LLM-based prompt rewriter (cascade)

DALL-E 3 is a closed-source latent diffusion text-to-image system built on top of a large pretrained image autoencoder. Its core innovation, described in the technical report 'Improving Image Generation with Better Captions' (Betker et al. 2023), is dataset recaptioning: a fine-tuned vision-language captioner was used to relabel large portions of the training corpus with rich, detailed synthetic captions that describe content, style and composition far more thoroughly than the noisy alt-text used by earlier models. DALL-E 3 is also tightly integrated with GPT-4: at inference, a prompt-rewriter LLM expands the user's short prompt into a longer, more descriptive caption before it is fed to the diffusion backbone, which substantially improves prompt adherence. The diffusion backbone uses a U-Net with cross-attention conditioning and is trained with the standard noise prediction objective; sampling typically uses 50 DDIM/DPM-Solver steps. Safety filters and a multi-stage moderation pipeline (input and output) are applied, and the model refuses requests for named living people, copyrighted characters and explicit content.

Parameters: Undisclosed (estimated multi-billion parameters in U-Net plus rewriter LLM)
Context: 4K tokens

What it can do

Excellent prompt adherence due to GPT-4-based prompt rewriting
High-quality, detailed images at 1024x1024, 1792x1024 and 1024x1792 resolutions
Strong typography and short-text rendering inside images
Coherent multi-object scenes with correct spatial relations
Style control via natural-language style descriptors
Built-in safety: refuses named real people, IP characters and unsafe content
Tight ChatGPT integration including iterative refinement chat
C2PA content credentials embedded in outputs
Best for: marketing creatives, blog illustrations, concept art, presentation graphics.

Training & License

Trained on a large, licensed and publicly available image-text corpus with extensive synthetic recaptioning by a fine-tuned vision-language model. Exact data sources are not disclosed. OpenAI emphasises filtering for explicit content, violent imagery and personally identifiable information.

License: Proprietary commercial license via OpenAI API, ChatGPT Plus/Team/Enterprise and Microsoft Bing/Copilot.

Known limitations