Which AI Image Model Should You Use?

Eight image models share one prompt box here. Switching models is a dropdown, not a new account. The useful question is not which model is best overall — none of them is — but which one to spend credits on for the picture in front of you.

Short version: draft on a cheaper model, finish on a heavier one when text, density, photoreal materials or production layouts actually matter.

Credit ladder at a glance

Every successful image costs the credits shown on that model. One image per run. Failed generations are not charged.

• GPT Image 2 — 8 credits. Up to 8 reference images. 1K / 2K / 4K. Literal layouts and clean product shots.
• GPT Image 2.5 Flare — 10 credits. Up to 8 references. 1K / 2K / 4K. Faster GPT-family iteration and edits.
• GPT Image 2.5 Sunburst — 10 credits. Up to 8 references. 1K / 2K / 4K. More precise GPT-family materials, grids and multi-turn edits.
• Nano Banana 2 — 10 credits. Up to 8 references. 1K / 2K / 4K. Fast everyday aesthetic work and light edits.
• Qwen Image 3.0 — 10 credits. Up to 3 references. 1K / 2K. Multilingual dense layouts and typography-aware design.
• FLUX.2 — 15 credits. Up to 8 references. 1K / 2K. Photoreal materials and reference-guided photography (replaces FLUX Schnell).
• Nano Banana Pro — 15 credits. Up to 8 references. 1K / 2K / 4K. Legible on-image text, crowded scenes and fine detail.
• Seedream 5.0 Pro — 20 credits. Up to 8 references. 1K / 1.5K / 2K. Production multilingual boards and multi-reference campaigns.

All eight support text-to-image and image-to-image. Providers behind the scenes are dual-routed (APIMart / KIE); you only see one credit balance and the model picker.

GPT Image 2 — the literal-minded starter

OpenAI's model reads a specific brief and renders what the brief says. Three objects means three. "Leave room for a headline" leaves room. Use it for covers, diagrams, labelled charts and clean e-commerce shots most models over-decorate — anywhere structure matters more than photoreal atmosphere.

Eight credits makes it the cheapest full GPT layout pass when you do not need 2.5-tier speed or precision.

GPT Image 2.5 Flare & Sunburst — faster / sharper GPT

Same family, two lanes at 10 credits:

• Flare when you want quicker day-to-day generation and edits with up to eight references.
• Sunburst when materials, grids or multi-turn precision matter more than raw speed.

Stay on GPT Image 2 if you only need a lower-cost layout pass.

Nano Banana 2 — everyday aesthetic work

Google's lighter Nano Banana lane: strong everyday looks, outfit and scene swaps, light restyles. Ten credits. Escalate to Nano Banana Pro when posters need exact wording, scenes get extremely crowded, or fine engraving has to survive.

Qwen Image 3.0 — multilingual layout brain

Best when the brief is a dense infographic, flow diagram or multilingual poster and the hierarchy has to stay readable. Cap is three reference images — lower than the product's usual eight — and resolution stops at 2K. For photographic night markets with perfect headline text, prefer Nano Banana Pro; for Swiss-style editorial covers, GPT Image 2 or Flare often suffice.

FLUX.2 — photoreal drafts and reference edits

Black Forest Labs' FLUX.2 is the photoreal workhorse here: believable fabric, skin, glass and street texture, with up to eight references for identity or product lock. Output is 1K or 2K. Aspect options are the FLUX intersection set (including auto, without ultra-wide 21:9 / 9:21).

It replaced FLUX Schnell. There is no 1-credit draft model and no four-images-per-run batch anymore — each FLUX.2 render is one image at 15 credits. Use it to explore photoreal direction, then hand hard text or production boards to Nano Banana Pro or Seedream.

Nano Banana Pro — the hard-shots model

Google DeepMind's heavier Gemini Pro image model survives what breaks lighter ones: legible text (including non-Latin scripts), dense compositions that stay coherent, and fine detail like engraving, small print and distant faces. Subject consistency across scenes is why it pairs well with reference images for character series.

Fifteen credits, one image, up to eight references, up to 4K.

Seedream 5.0 Pro — production multilingual boards

ByteDance's production model for campaign layouts, material studies and multi-reference edits when the brief is a finished board, not a single mood shot. Twenty credits. Resolutions include 1.5K between 1K and 2K. Prefer FLUX.2 for photoreal exploration with fewer layout constraints; prefer Nano Banana Pro when on-image text density is the main failure mode.

A workflow that wastes fewer credits

1. Start on GPT Image 2 or Nano Banana 2. Eight or ten credits to test composition and wording — not to finish the hero shot.
2. Keep the prompt that worked, not every picture. The closest frame tells you which clauses mattered.
3. Re-run on the model that matches the remaining risk. Layout fidelity → GPT 2.5 Flare/Sunburst. Photoreal materials → FLUX.2. Exact text / dense scenes → Nano Banana Pro. Multilingual production boards → Seedream 5.0 Pro. Dense multilingual diagrams with ≤3 refs → Qwen.
4. Iterate with references. Feed the result back into image-to-image; say what should change, not the whole scene again. Most models accept up to eight references; Qwen stops at three.

A handful of cheap iterations plus one or two heavy finishes usually costs less than burning Seedream or Nano Banana Pro on every exploratory click.

Things that stay true across models

• Pick aspect ratio and resolution before generating — each model exposes only the ratios and resolutions it actually supports.
• One shared credit balance. No separate vendor subscription, no API key to paste, no GPU.
• A generation that fails is not charged.
• Results land in your library with the prompt and the model that made them.

New accounts start with free credits — enough to try GPT Image 2 and sample a mid-tier model before you commit a heavier finish. Compare the models or just start with a prompt.