AI Image Generator Benchmark: The 7 Big Limits

Short answer: modern AI image generators are powerful, but they still fail in seven common ways: rate limits and cost, content policy blocks, bad text, inconsistent characters, weak edit control, dataset bias, and environmental cost. This guide explains each limit, shows why it happens, and gives clear workarounds.

Download the free Scorecard PDF to compare GPT‑4o, Midjourney, and Stable Diffusion test scores for each limit. Use it to pick the right tool or prompt for your project.

How we tested (quick)

We ran the same prompts on GPT‑4o, Midjourney, and Stable Diffusion. Tests targeted seven specific failures users report. The goal was to find repeatable problems and practical fixes you can use right away.

Limit 1 — Rate limits, quotas, and performance

What happens: Services add daily or per-minute caps. You may see short cool downs, then longer ones. OpenAI testing notes and press coverage say providers use limits to protect GPUs and fairness.

Why it matters: If you build a workflow, a daily cap can stop you mid-project. For teams, unknown quotas break planning.

Workarounds:

Plan batches. Queue all prompts and run them when limits reset.
Use a mix of tools. When one hits a cap, switch to another provider.
Consider paid tiers. Some paid plans raise or remove limits — check provider docs like the Microsoft Copilot Q&A.

Limit 2 — Content policy blocks and creative limits

What happens: Models refuse or alter images because of content rules. This can block fictional characters or stylized fan art. Community threads show creators hit policy walls when generating original worlds or characters: see a user report about blocked creative freedom on the OpenAI community.

Why it happens: Filters try to avoid harm and copyright misuse. They use pattern checks that can be overbroad.

Workarounds:

Own-style prompts: describe unique traits rather than naming known IP.
Use open models with permissive licenses (careful with ethics and law).
Iterate: small prompt changes can bypass false positives. Keep a saved set of safe phrasings.

Limit 3 — Text, letters, and symbols are garbled

What happens: AI often creates unreadable text on images. Letters are warped, gibberish, or cut off.

Why it happens: Image models learn shapes, not true typography. Few training examples show crisp, multi-letter words in varied contexts, so models guess.

Workarounds:

Create the artwork without text, then add text in an editor (Photoshop, Figma).
For simple labels, use short, high-contrast phrases and test many variations.
Use dedicated generative tools that focus on layout and vector text if you need production-ready assets.

Limit 4 — Character and detail inconsistency

What happens: The same character looks different across images. Small details vanish or change.

Why it happens: Models don't have a persistent memory for visual entities. They generate each image fresh from the prompt and learned patterns.

Workarounds:

Use image-to-image workflows (seeded edits) so the tool keeps visual anchors.
Save exact prompt + seed and include a reference image for consistency.
Choose tools known for style consistency for series work (some versions of Stable Diffusion with embeddings work well).

Limit 5 — Limited control for fine edits and photoreal precision

What happens: You can't reliably edit one element without changing others. Precise mockups and product images are hard.

Why it happens: Generative models trade pixel-level control for global coherence. They optimize the whole scene, not single small edits.

Workarounds:

Do a hybrid approach: AI for broad concepts, manual editing for precision.
Use layer-based tools or mask-guided edits available in some UIs.
If you need exact product shots, stick to photography or 3D renders for final assets.

Limit 6 — Dataset bias and missing concepts

What happens: Some subjects, cultures, or niche visual ideas are poorly represented or stereotyped.

Why it happens: Models learn from training images. If the training set lacks examples, the model can't generalize well. Quora and industry posts explain how dataset gaps limit novelty and accuracy.

Workarounds:

Provide detailed visual cues in your prompt (colors, culture-accurate clothing, real-world examples).
Use reference images to nudge the model toward correct representation.
When representation matters, review outputs carefully and plan human review steps.

Limit 7 — Energy, cost, and environmental impact

What happens: High-quality images need big GPUs. Providers limit requests because the hardware heats up and costs rise. Coverage like The Verge's report quotes providers saying GPUs are under heavy strain.

Research also points to water and power use for cooling data centers, as discussed in an ethics case study on the SCU site.

Why it matters: Cost and carbon add to project budgets. Unlimited experimentation has a real resource cost.

Workarounds:

Be deliberate. Sketch and plan prompts before bulk runs.
Prefer lower-resolution tests when iterating.
Check provider efficiency notes and consider providers focusing on lower energy footprints.

Quick comparison scorecard (example)

Below is a simplified view of how the three tools tend to behave on these limits (1 = poor, 10 = strong). Your experience may vary by model version and settings.

Limit	GPT-4o	Midjourney	Stable Diffusion
Rate limits	5	7	8
Policy blocks	6	6	7
Text rendering	7	5	4
Consistency	6	8	7
Fine edits	5	6	7
Bias	6	6	5
Environmental cost	6	5	6

Practical checklist before you generate

Pick the right tool for the job: iteration, final art, or photoreal mockups.
Save prompt + seed + reference images so you can reproduce results.
Run low-res tests, then upscale or refine once happy.
Add human review for representation and legal checks.

Bottom line and comparison

Compared with straight image editors, AI generators trade control for speed and ideas. They are great for concept work and fast mockups. For production final assets, we still recommend a hybrid workflow: AI for speed, humans for polish. Bottom line: know the seven limits, pick the right tool, and use the workarounds to save time and avoid surprises.