Model Differences: DALL-E 3, Midjourney, Flux, and Imagen

Same Prompt, Four Different Images — and That's Not a Bug

Copy the same detailed image prompt into DALL-E 3, Midjourney, Flux.1, and Imagen 3 and you will get four images that may share the subject but differ dramatically in style, rendering quality, composition choices, and how precisely they followed your instructions. This is not a sign that some models are failing — it is a sign that they have fundamentally different architectures, training approaches, and design goals. Understanding these differences lets you choose the right model for each task and adapt your prompt accordingly rather than fighting each model's tendencies.

DALL-E 3: The Natural Language Model

DALL-E 3, OpenAI's image generation model, is designed to follow natural language prompts with high fidelity. Unlike earlier image models that responded to keyword lists, DALL-E 3 processes full sentences and understands complex compositional instructions, relationships between objects, and nuanced scene descriptions.

Key characteristics:

• Natural language first: Write prompts as sentences or paragraphs, not keyword lists. The more naturally written the instruction, the better DALL-E 3 follows it.
• Text in images: DALL-E 3 is notably capable of generating legible text within images — one of the most distinctive technical capabilities among major models. To use it, put the exact text you want rendered in quotation marks within the prompt: "A coffee shop chalkboard sign that reads Today's Special: Oat Milk Latte in handwritten chalk lettering." Short phrases (one to five words) are most reliable. Longer text strings may have errors.
• Exclusions via natural language: "A street scene without any cars or people visible" is understood. "Avoid harsh shadows, avoid cluttered backgrounds" works for quality exclusions. No dedicated exclusion parameter — embed in natural language.
• Prompt rewriting: When accessed through ChatGPT, DALL-E 3 rewrites your prompt before generating. This can add detail you did not request or alter emphasis. Access via the API with system instructions to minimize rewriting if you need precise prompt adherence.

DALL-E 3 prompt example: "A woman in her 40s at a wooden desk writing in a journal. Late afternoon light through linen curtains, warm amber tones. Photographic style, medium close-up, 85mm lens. The journal page reads Day One in her handwriting. Quiet, introspective mood."

Midjourney: The Parameter-Driven Model

Midjourney is the most parameter-rich of the major image models, and its prompting style reflects a philosophy of precise visual control through structured modifiers. As of v6 and v7, Midjourney has significantly improved natural language understanding, but its parameter system remains the primary tool for fine-grained control.

Core parameters (placed at the end of the prompt, after the text description):

• --ar [width]:[height] — Sets the aspect ratio. Common values: --ar 1:1 (square), --ar 16:9 (widescreen), --ar 3:4 (portrait), --ar 4:5 (Instagram portrait). Example: a coastal town at sunset, watercolor style --ar 16:9
• --no [element, element] — Negative prompt. Comma-separated list of elements to exclude. Example: portrait of a woman, natural light --no jewelry, makeup, background people
• --style raw — Disables Midjourney's default aesthetic enhancement. Produces images that follow the prompt more literally and look less "Midjourney-stylized." Essential when you want documentary or neutral aesthetics rather than the platform's signature look.
• --v [version] — Specifies the model version. --v 6 and --v 6.1 are the current stable options; --v 7 adds Omni-Reference for character consistency (covered in Lesson 9). Use the latest stable version unless you are deliberately referencing an earlier aesthetic.
• --s [0-1000] (stylize) — Controls how strongly Midjourney's aesthetic training is applied. Low values (0–100) produce literal, muted results. High values (750–1000) produce highly stylized, opinionated images. Default is 100.
• --c [0-100] (chaos) — Controls variation between generations. Higher values produce more unexpected compositions. Low values are more predictable. Default is 0.

Midjourney prompt example: a weathered fishing boat in a Nordic fjord at dawn, mist on the water, film photography, Fujifilm Pro 400H palette, golden light --ar 3:2 --style raw --no people, modern elements, text --v 6.1

Flux.1: The Detail-First Model

Flux.1, developed by Black Forest Labs, is built for following detailed, descriptive prompts with high prompt adherence — meaning it tends to render elements you describe more accurately and comprehensively than models with stronger aesthetic opinions of their own. It excels at photorealistic and commercial imagery and handles complex compositional descriptions reliably.

Key characteristics:

• Natural language, not keyword lists: Flux.1 performs best with detailed, sentence-structured prompts. Its architecture is designed to process natural language instructions, and the more specific the description, the more precisely it is followed.
• Hierarchical layering works well: Describe the image in layers — foreground subject first, then mid-ground, then background elements. This matches how the model processes compositional information.
• No native negative prompts: Flux.1 does not support a dedicated negative prompt field in its base architecture. Use positive framing: instead of "no extra fingers," write "hands with correct anatomy, five distinct fingers, natural proportions." Some interface implementations add a negative field as a post-processing layer, but results are inconsistent.
• Text in images: Like DALL-E 3, Flux.1 supports text rendering. Use quotation marks around the exact text: "A vintage poster that says Explore More in bold Art Deco lettering."
• Variants: Flux.1 [pro] emphasizes quality and detail; Flux.1 [dev] is optimized for research and flexibility; Flux.1 [schnell] is faster but lower fidelity. For image prompting practice, Flux.1 [dev] via accessible interfaces offers the best combination of quality and availability.

Flux.1 prompt example: "A barista's hands pouring steamed milk into a ceramic cup in slow motion, creating a rosette latte art pattern. Close-up, 85mm macro equivalent, extremely sharp focus on the milk pour, soft bokeh on the cup rim and counter background. Commercial coffee photography, clean natural light from a window to the left, clean white and warm wood tones."

Imagen 3: The Photorealism Specialist

Google's Imagen 3 is particularly strong in photorealistic rendering and understands photography vocabulary — lens types, lighting setups, and compositional terms — with notable accuracy. Available through Google's Gemini products and Vertex AI, it handles natural language prompts and processes complex scene descriptions reliably.

Key characteristics:

• Photography vocabulary is well understood: Focal length, depth of field, lighting setups, and photographic style terms produce consistent and accurate results. Imagen 3 has been specifically tuned to respond well to the language of photography.
• Photorealism strength: For photorealistic output — people, products, architecture, nature — Imagen 3 is among the strongest available. It renders physically plausible images with accurate lighting, material, and perspective.
• Natural language exclusions: Like DALL-E 3, exclusions are handled through natural language. "A park scene without any visible buildings or vehicles" is understood reliably. Rephrase as positive conditions when natural language exclusions are inconsistent.
• Text in images: Imagen 3 added improved text rendering capabilities. Short text strings in quotation marks in the prompt are rendered more reliably than in earlier versions, though accuracy decreases with longer text.

Imagen 3 prompt example: "A family sitting around a dinner table, golden evening light through a window to the right. Documentary-style, natural expressions mid-conversation, no one looking at the camera. Full shot, 35mm equivalent, slight grain. Warm amber and terracotta palette."

Model Comparison: Choosing the Right Tool

Adapting the Same Prompt Across Models

DALL-E 3 version (full natural language): "A stone lighthouse on a rocky coast during a storm. Dramatic waves crashing against the base, storm clouds, lightning in the distance. Fine art photography style, long exposure effect, blue-grey palette. No people visible."

Midjourney version (natural language + parameters): "stone lighthouse on rocky coast, dramatic storm, crashing waves, lightning, fine art photography, long exposure, blue-grey palette --ar 16:9 --style raw --no people, tourists, text --v 6.1"

Flux.1 version (layered natural language): "A dramatic stone lighthouse stands on jagged black rocks in the foreground. Massive storm waves crash against the base, sending white foam high. Storm clouds fill the mid-ground sky with deep blue-grey tones. A single bolt of lightning strikes in the far background. Fine art long-exposure photography aesthetic, blue-grey desaturated palette, no people, cinematic scale."

The core visual idea is identical across all three. The syntax adapts: DALL-E 3 receives clean natural language with the exclusion embedded; Midjourney receives compressed language with parameters appended; Flux.1 receives layered natural language organized from foreground to background. The vocabulary is transferable. The structure adapts to each model's strengths. The image generation prompt library at OnePlaceForAI.com contains side-by-side examples of prompts adapted for different models — study these to see how vocabulary transfers while syntax adapts to each platform's particular strengths.