AI Image Model Comparison in 2026: Five Prompts, One Scorecard, and How to Answer "What Is the Best AI Image Generator" for Your Own Work

Ask ten people "what is the best AI image generator" and you'll get ten brand names and ten cherry-picked samples. One lucky render proves nothing. A model that nails a logo on try one can botch the same logo on tries two through four, and the sample you see in a roundup is almost always try one.

My name is Artem and I run the Writingmate blog. I spend a ridiculous amount of time sending identical prompts to different models and comparing what comes back, because the only way I trust a model is when I've watched it fail on my kind of work. This post gives you the exact method: five prompts, a four-point scorecard, a retry-adjusted cost formula and a verdict matrix you fill in yourself.

One honest note up front. Image model lineups change every few weeks, so I'm not going to hand you a "winner" table that's stale by Friday. Instead you get a kit that produces a winner for your briefs in about 30 minutes. If you only want job-by-job picks, our job-based image generator guide covers that. This one is the evidence-gathering method behind it.

Why single-sample rankings mislead

Three problems show up in almost every public comparison:

• One attempt per model. Image generation is stochastic. The same prompt can give you a keeper and a disaster back to back.
• Easy prompts. A sunset over mountains is handled well by nearly everything. The differences appear on text, hands, product geometry and multi-element layouts.
• No cost lens. A model that needs four retries per usable image is four times its sticker price, whatever the pricing page says.

The video above makes a typical claim of this sort: that Midjourney is weak at spelling while Ideogram is strong on text. That fits what academic benchmarks keep reporting too. The GPT-4o image generation study notes that earlier models handled short text reasonably but long text stayed limited, and OneIG-Bench shows text accuracy varying widely between model families.

Paraphrasing the Midjourney vs FLUX vs Ideogram video: its creator argues text accuracy is where the three tools separate most, with Ideogram far ahead on spelling. These are the video's claims, not our measurements, which is exactly why you should rerun the test yourself. — YouTube test

Community threads such as r/StableDiffusion on Reddit and model-maker accounts like Ideogram and Black Forest Labs on X are worth following for release news. Treat their samples as best-case, though.

How I compare: the setup

The rules are simple, and they matter more than which models you pick:

1. Same workspace, same prompt, same aspect ratio. In a multi-model workspace like Writingmate's text-to-image page you send one prompt to several models and review the outputs together. No tab switching, no re-typing, no subtle prompt drift.
2. Three attempts per model per prompt. If you only get one sample, you're measuring luck.
3. Pick three to five models. Include one you already use, one cheap option and one or two you've heard good things about. Browse the model list for what's currently available.
4. Score immediately, blind if you can. Hide the model name while scoring. Brand bias is real.

The five-prompt kit (copy and adapt)

Each prompt targets a different failure mode. Swap the bracketed details for your real brand, product or topic.

#	Prompt type	What it stress-tests	Starter prompt
1	Logo with text	Spelling, letter spacing, clean vector look	Minimal flat logo for a coffee roaster named "[BRAND]", exact text "[BRAND]" in bold sans-serif, centered, white background, no extra words.
2	Product shot	Geometry, lighting, material realism	Studio photo of a [PRODUCT] on a light grey seamless backdrop, soft side light, shallow depth of field, sharp label, 4:5.
3	Portrait	Skin, hands, eyes, natural light	Candid portrait of a [AGE] [PROFESSION] at a window, natural light, hands visible holding a mug, 85mm look.
4	Infographic	Multi-text layout, hierarchy, numbers	Clean infographic titled "[TITLE]" with three labeled steps "1 Plan", "2 Build", "3 Ship", icons, consistent palette, 16:9.
5	Stylized illustration	Style control, composition, consistency	Flat editorial illustration of [SCENE], limited four-color palette, bold shapes, generous negative space.

Why these five? Logo and infographic prompts expose text failures. The product shot exposes physical errors, such as a bottle with the wrong label or a shoe with three tongues. The portrait is the classic "uncanny" detector, and the illustration tells you whether a model can follow style direction rather than defaulting to its house look.

The four-point scorecard

For each image, score four things from 0 to 5:

• Text accuracy. Every character correct? Deduct a point per error. Zero if the text is unreadable. For prompts without text, mark N/A.
• Prompt adherence. Did it include every element you asked for, in the layout you asked for, and nothing you didn't?
• Realism or polish. For photos, does it survive zooming to 100%? For illustration, is the style clean and intentional?
• Retries needed. Not scored 0 to 5. Count how many of your three attempts were shippable (0, 1, 2 or 3).

Use a sheet like this for each model:

Prompt	Text accuracy	Adherence	Realism / polish	Usable out of 3
Logo with text	__ / 5	__ / 5	__ / 5	__ / 3
Product shot	N/A or __	__ / 5	__ / 5	__ / 3
Portrait	N/A	__ / 5	__ / 5	__ / 3
Infographic	__ / 5	__ / 5	__ / 5	__ / 3
Stylized illustration	N/A	__ / 5	__ / 5	__ / 3

I leave the cells blank here on purpose. Your brief and your tolerance for errors decide the score, and I'd rather you trust your own eyes than my screenshot.

Cost per usable image: the math that changes rankings

Sticker price per generation is the wrong number. The right one is:

Cost per usable image = price per generation ÷ usable rate

where usable rate is shippable images divided by total attempts across your five prompts. Here's an illustrative example with made-up numbers, just to show the effect:

Model type	Price per image (example)	Usable rate (example)	Cost per usable image
Budget model	$0.02	25% (4 attempts per keeper)	$0.08
Mid-priced model	$0.04	60%	$0.067
Premium model	$0.08	90%	$0.089

See what happened? The cheapest model isn't cheapest once you count retries, and the premium one is closer in cost than its price suggests. Plug in your real prices and your real usable rates from the scorecard. Also add your own time: if a retry costs you two minutes of reviewing and re-prompting, that's often more expensive than the credits.

If you're deciding how to pay for all this, our AI subscription cost math walks through one plan versus stacked plans, and the Writingmate pricing page shows what image generation costs inside a single plan.

The verdict matrix: turn scores into a decision

Once the sheets are filled in, don't average everything into one number. Averages hide what you care about. Build a matrix instead: your job types as rows, your models as columns, and the best performer per row marked.

If your work is mostly...	Weight these scores	Deciding question
Logos, posters, social cards with text	Text accuracy, usable out of 3	Can I ship without fixing spelling by hand?
E-commerce and ads	Adherence, realism	Does the product look like my actual product?
People and lifestyle	Realism, usable out of 3	Do hands and eyes survive zooming in?
Explainers and slides	Text accuracy, adherence	Are all labels and numbers right?
Editorial and brand art	Adherence, style polish	Does it follow my palette and style?

It's completely normal for two or three different models to win different rows. That's the real answer to "what is the best AI image generator": the best one per job. Using one model for everything is usually a convenience choice, not a quality choice.

Tips that raised my usable rate

• Keep on-image text short. A few words is safe. Whole sentences are where models start inventing letters.
• Put text in quotes and say "exact text". It nudges models to copy rather than paraphrase.
• Name the layout. "Title top center, three columns below" beats "make it organized".
• Don't stack styles. "Photoreal watercolor cyberpunk" gets you mush. Choose one.
• Fix text outside the model. If an image is 95% right but a word is wrong, patch it in a design tool. Don't burn ten retries chasing perfection.
• Re-run your kit monthly. New models arrive constantly. A 30-minute rerun keeps your matrix honest.

You can also lean on a chat model to write sharper image prompts before you spend generation credits. Ask it to expand your brief into three prompt variants, then test all three. That alone often lifts adherence scores.

Where this method breaks down

Be realistic about what a 15-image-per-model test can and can't say:

• It's a sample, not a benchmark. Large academic suites like OneIG-Bench cover far more prompts.
• Your scoring is subjective. Having a colleague score blind improves reliability a lot.
• Providers update models without notice. A result from today may drift.
• Licensing and commercial-use terms differ by provider. Check them before publishing anything for clients.

Still, a small test built on your own briefs beats a large test built on someone else's. That's the whole argument.

Run it in one workspace

The reason I do this inside Writingmate is plain: one prompt, several image models, results next to each other, and no juggling subscriptions to find out which one suits you. Once you've got your matrix, you can keep using the winners for each job without paying for the losers.

So here's the bottom line. Don't pick an image model from a ranking. Pick five prompts that look like your real work, run three attempts each, score what you see, do the retry math, and fill in the matrix. You'll likely end up with two or three models you actually trust, and a clear reason for each.

See you in the next one!

Artem