Image generation moved from novelty to commodity quickly, and raw quality has converged to the point where "which looks best" is no longer the useful question. The differences that still matter are elsewhere: how much control you get over a specific result, whether the tool can render legible text, how it handles editing an image you already have, and what you're permitted to do with the output.
Models, plans and licensing terms in this category change frequently. Verify current details with the vendor before relying on any of it for commercial work.
Three categories, not a ranked list
Hosted, art-focused tools
Dedicated image services built around generation, typified by Midjourney, and including services from Adobe and Stability AI. These optimise for aesthetic output and usually have the strongest communities around prompting technique.
- Strength: strong default aesthetics, less effort needed to get something attractive
- Strength: active parameter and style systems, plus reference-image conditioning
- Limitation: often a separate surface from where you actually work; some are chat- or Discord-centric
- Limitation: you're dependent on the vendor for access, history and pricing
Generators inside assistants
Image generation folded into general-purpose assistants — OpenAI's models inside ChatGPT, Google's Imagen family inside Gemini, and similar integrations.
- Strength: you're already there. Iterating in conversation, and combining image generation with reasoning about the image, is genuinely useful.
- Strength: conversational editing — "make the background warmer", "remove the second person" — without learning separate tooling
- Strength: one subscription covering several kinds of work
- Limitation: usually fewer fine-grained controls than a dedicated tool
- Limitation: generation quality and speed are subject to the assistant's plan limits and current model
Open-weight and local models
Weights you can run yourself — the Stable Diffusion lineage, Flux from Black Forest Labs, and a large ecosystem built on both.
- Strength: free at the point of use, no content filter by default, private, and unlimited iteration
- Strength: fine-tuning, LoRAs, ControlNet and inpainting give structural control that hosted tools often can't match
- Strength: your images and prompts never leave your machine
- Limitation: hardware requirements, setup time, and a much steeper learning curve
- Limitation: you're responsible for keeping it working
What actually differs
- Prompt adherence vs. aesthetics. Some models follow your description precisely but produce flatter images; others produce beautiful images that only loosely match what you asked for. Which you prefer depends entirely on the job.
- Text rendering. Historically the weakest capability across the board, and improving unevenly. If you need legible words in an image — a poster, a menu, a sign — test this specifically before committing.
- Editing vs. generating. Changing one element of an existing image is a different capability from making a new one. Inpainting, background removal and object removal vary more between tools than raw generation does.
- Consistency. Keeping a character, product or style stable across a set of images is genuinely hard and a common reason projects stall. Reference-image and style-consistency features are the differentiator here.
- Resolution and aspect control. Practical constraints if output is going into a real layout.
- Licensing and provenance. Commercial-use terms differ substantially between providers and plan tiers. This is the one to read before using output in anything published or sold.
Where they still fail
- Hands and fingers — much improved, still unreliable
- Legible text — improving, still the most common reason a generated image is unusable
- Physical and spatial logic — reflections, shadows, object continuity across a scene
- Quantity — asking for "five people" often returns an indeterminate number
- Brand and likeness — reproducing a specific real person or trademarked material is a legal question, not a technical one
- Factual illustration — a generated diagram of an anatomical structure or a mechanism may look authoritative and be wrong. Never use generated imagery where accuracy is the point.
Which to pick
- You want something attractive with minimal effort: a hosted art-focused tool.
- You want to iterate in conversation while doing other work: the generator in the assistant you already use.
- You need precise structural control, privacy, or high volume: run a local open-weight model.
- You need consistent output across a set: test the style-consistency features first — this is where projects fail.
- Commercial or published use: read the licensing terms for your specific plan before you start, not after.
Getting better results
- Describe subject, then style, then composition — in that order, and be concrete
- Use reference images rather than trying to describe a look in words
- Generate several and pick — selection beats prompt engineering for most people
- Iterate on one variable at a time so you learn what actually moved the result
- Don't put text in the prompt if you can add it later in a real layout tool
- Keep the prompt with any image you might want to reproduce or vary later
Bottom line
Hosted art tools give the best default aesthetics, generators inside assistants win on convenience and conversational editing, and local open-weight models win on control, privacy and volume. Quality differences between them are now smaller than the differences in editing capability, text rendering and licensing terms — so choose on those, verify the licence before commercial use, and never let a plausible-looking generated illustration stand in for an accurate one.