Image Gen – IMG.LY Blog

Introducing IMG.LY AI Benchmarks

Jan — Mon, 03 Aug 2026 00:00:00 GMT

IMG.LY GenAI Benchmarks is live. 15 text-to-image models, 37 prompts taken from real design jobs, three fixed seeds each, 1,662 generated images, roughly 7,400 scores. Every prompt, every score, every generated image and the full methodology are public.

Why we ran it

Our customers embed image generation in products that ship physical, commercial output. Web-to-print storefronts, merch platforms, campaign tooling, template systems. When they asked us which model to use, the honest answer was a shrug and a link to a leaderboard that measures something else.

The existing arenas rank aesthetic preference by crowd vote. That’s a signal, sure, but not the one most relevant to these use cases. They want to know whether the image has a real alpha channel, or whether users got the hex they asked for and whether a word is spelled right. A 10,000-piece personalization run needs to hold the character across every variant. Those are the failures that cost you a reprint or a support ticket, and none of them show up in a preference ranking.

So we built the one that answers our customers’ question. Which model should you build on for production design work.

Why some of it is judged, not measured

Plenty of what matters here is measurable, and we measure it. Native output size, wall-clock latency and price per image come straight off the run. Transparency is a file-level check for a real alpha channel rather than a painted-on white rectangle. Color accuracy is the CIEDE2000 distance between the hex the prompt required and the color that came back.

The rest needs a judgment call. Whether an image satisfies “three blue cubes stacked on the left and one red sphere on the right” is a checklist, and something has to look at the picture and tick it off. Whether a banner leaves a clean area for a headline has a right answer, but not a computable one.

For those criteria a vision model does the judging, against a checklist written per prompt before the run. We added two rules to make the VLM’s judgment more transparent and control for arbitrariness. Every judgment records a written rationale beside the score, so you can read why an image lost a point and tell us we’re wrong. The criterion that really does need a person, design-readiness, the “would a professional ship this with under five minutes of cleanup” question, is surfaced to a human in the loop (that would be yours truly, the author).

The rest of the protocol is the boring part that makes the numbers worth anything. Identical prompts for every model, default parameters, fixed seeds, no per-model prompt tuning ever, a versioned suite, and published scores that are never quietly restated. Partner models get no special treatment, and the Gateway links stay well away from the rankings.

Why we split it by use case

Average our eight criteria with equal weight and Nano Banana 2 Lite leads at 3.87 out of 5, while last place, at 2.55, goes to Nano Banana Pro, the most expensive flagship in the set. That’s not a data error. Nano Banana Pro has the best brand-color fidelity in the field and renders every required string in the suite exactly. It also costs 28 times more per image than the cheapest model and takes 23 seconds. An unweighted average treats all of that as equally important, so it buries the model with the best output.

This is the chart everyone asks for, and its vertical axis is the number you should not rank on. Explore the live, interactive version, where every point links to that model’s scores.

Which is what leaderboards do. So we publish the same measurements per use case as well, with a weight matrix per job and the reasoning behind each matrix written out. Print files weight resolution and color heavily, because a 1024px generation is a 3.4-inch print and an out-of-gamut red is a reprint. Personalization at scale weights cost and adherence, because unit economics decide the run. Merch weights the alpha channel above everything else, because a sticker without a clean cutout isn’t a sticker.

Same numbers, different question, different answer. That’s why the section opens by asking what you’re building instead of handing you a ranked list.

The result that surprised us most

Reweighting moves models the length of the table.

GPT Image 1.5 sits twelfth of fifteen on the unweighted blend, dragged down by cost and latency. Reweight for merch and stickers, where transparency carries 35 percent of the matrix, and it wins outright at 3.72, nearly a full point clear of second place. It’s the only model in the run that reliably returns a genuine alpha channel. Thirteen of the fifteen return none at all, and a few paint a fake checkerboard into the pixels, which looks like transparency right up until it reaches a printer.

On a general leaderboard, the best model for one of our customers’ most common jobs reads as a mid-table also-ran. That’s the argument for the whole project.

The typography column surprised us a second time. Ideogram has the strongest public reputation for rendering text, and in our run it ranks twelfth of fifteen on measured text accuracy, behind several general-purpose flagships that now render every required string in the suite exactly. Route typographic work to a specialist on reputation alone and you can end up with worse type than the model you already call by default.

Two more capability gaps got written up as standalone findings. Brand color, where the best model manages 3.67 out of 5 and most of the field sits below 3. And consistency, where no model holds a described character across three scenes and the ceiling is 3.89.

What this means for an editor

We build editors, so our conclusion isn’t neutral. The data still points where it points.

Read the four findings together and they describe the same problem four times. The cut-out is missing, the color is close but not the color in the brand book, the headline is right until it isn’t, and the character drifts between scenes. None of these are bugs waiting on the next model release. They’re what generation is, a probabilistic first draft. Getting from that draft to something a customer can order takes a handful of deterministic corrections, and somebody has to be able to make them.

That’s an editor’s job. Background removal and edge cleanup close the transparency gap. Brand kits and exact color values close the color gap. Editable text layers turn a wrong character into a two-second fix instead of a regeneration. Reusable assets and templates carry identity that regeneration won’t. Our AI Editor gives your users those controls over whatever model produced the image, and the AI Gateway makes routing per job, which the data says you should be doing, a config change instead of another integration.

We’d rather make that case with numbers anyone can check, including the ones that make models we partner with look bad.

Caveats, and what’s next

This is a pilot dataset. Quality criteria are judged by a vision model, not yet by the blind expert panel, and design-readiness isn’t scored at all. The weight matrices are provisional. When the frozen suite lands, scores restate once and the suite version changes; after that, nothing gets restated without a clearly labeled new methodology version.

The roster will grow. Image editing and video are the obvious next modalities, and we plan to re-run the suite whenever a notable model ships.

Go pick a use case and see which model wins your job. If the numbers disagree with your own experience, the prompts and the raw images are all sitting there, and we’d like to hear about it.

The Best GenAI Models for Web-to-Print: A Buyer's Guide

Jan — Fri, 26 Jun 2026 00:00:00 GMT

No single generative model is “best” for web-to-print. The right pick depends on the specific job, and print has jobs that screen-first leaderboards don’t measure. A model that tops a general image-quality benchmark can still be useless for print if it can’t render legible text, can’t output vector, or shifts hard out of CMYK gamut.

This guide gives you the criteria that actually matter for print, a scoring rubric to apply them, and a model-by-model read on where each one wins. The goal is to route each task to the model that fits it, which is why a model-agnostic (“bring your own model”) integration matters more than any one provider’s marketing.

How to read this guide. The criteria and rubric are the durable part. The model roster moves monthly: treat the specific model notes as a snapshot, and re-run the rubric against current versions before you commit. The per-model benchmark scores come from IMG.LY’s GenAI benchmark suite, which is now live: a pilot dataset you can explore today, re-run whenever new models drop. The gated PDF report that packages the weighted per-use-case totals and the full test set is the piece we’re opening to early-access subscribers first (sign-up at the end of this guide).

Why print needs its own criteria

A web-to-print product sells a physical object. That changes what “good” means:

The output is measured at 300 DPI on a substrate, not at 72 PPI on a retina screen.
Color is CMYK and spot, not sRGB.
Logos and type must scale and stay crisp: vector, not pixels.
The customer is a non-designer, so the model has to be controllable enough to stay inside a template.
And because it’s a commercial product sold to a customer, the licensing of the output carries real legal exposure.

General benchmarks (LMArena-style human preference, aesthetic scores) miss most of this. The eleven criteria below are built around it.

The eleven evaluation criteria

1. Output type and format: raster vs. vector

Why it matters for print: Logos, icons, line art, and type need to scale to any size and print crisply. Raster can’t; vector can. A model that outputs (or can be cleanly traced to) native SVG / vector is uniquely valuable for the print-specific elements that screen apps don’t care about.

What to test: Can it produce vector natively? If not, how cleanly does its output vectorize? Does the vector survive into PDF/X as real paths?

2. Resolution and maximum dimensions

Why it matters: A ~1024px generation is ~3.4” at 300 DPI. Large-format and even A4 need far more. Native max resolution, and how well the model holds detail when upscaled, determines whether you can print the output at size without a quality penalty.

What to test: Native max output dimensions; detail retention at 4×/8× upscale; effective printable size at 300 DPI.

3. Visual quality and fidelity

Why it matters: The baseline. Realism, coherence, absence of artifacts, anatomical and structural correctness. Crucially, how it holds up under print’s unforgiving close inspection: banding, mushy detail, and plasticky textures show more on paper than on screen.

What to test: Side-by-side preference on print-representative prompts; artifact rate; detail at print scale.

4. Text and typography rendering

Why it matters: Print is full of words, and most models garble text inside images. For anything where text-in-image is unavoidable (a generated poster, a label motif), legibility and kerning are make-or-break. Best practice is still to set real type as a separate layer, but some jobs need it baked in. Our text-reliability finding measures exactly this. The top of the field has largely solved Latin headlines: three models rendered every required string in our suite exactly. Small type and non-Latin scripts have not been solved, and the model most famous for typography ranks twelfth of fifteen. One wrong character still means regenerating the whole image, which is the real argument for keeping type on its own layer. Compare every model on the same wordmark prompt to see the spread.

What to test: Legibility of short and long strings; correct spelling; kerning; multi-line; non-Latin scripts.

5. Prompt adherence and controllability

Why it matters: Non-designers need predictable results, and templates need the output to land where it’s told. Controllability spans prompt adherence, image-to-image / inpainting / outpainting, style and reference-image conditioning, and seed stability for repeatable results.

What to test: Does it honor constraints? Quality of edits (generative fill, expand, object removal)? Reference-image fidelity? Same-seed reproducibility?

6. Color accuracy and CMYK-readiness

Why it matters: Vivid RGB outputs clip hard in CMYK and shift on press. Models that stay closer to printable gamut, and outputs that convert predictably, mean fewer surprises and less soft-proofing friction. Even before the CMYK conversion, hitting an exact brand hex in RGB is hard: our brand-color finding measures color accuracy with CIEDE2000 against the required hex, and no model reliably lands the color in your brand book.

What to test: Gamut coverage vs. CMYK; shift magnitude after ICC conversion; behavior on brand-critical colors and near-whites/blacks.

7. Transparency and background handling

Why it matters: Merch, stickers, die-cuts, and product compositing need clean subjects on transparent backgrounds and accurate edges (hair, glass, fine detail). Native transparent output and clean cutout edges save a manual masking step. In the models themselves this is close to unsolved: our transparency finding shows 13 of 15 tested models emit no real alpha channel at all, which is exactly why a background-removal step belongs in the pipeline rather than a prayer that the model returns a clean cutout.

What to test: Native alpha output; edge quality on hard cases; cutout-path readiness.

8. Responsiveness / latency

Why it matters: Two very different bars. In an interactive editor, anything over a few seconds breaks flow. In a batch/VDP run, throughput and concurrency matter more than per-image speed. The right model differs by context.

What to test: P50/P95 latency at your resolution; throughput under concurrency; cold-start behavior.

9. Cost per generation

Why it matters: Unit economics. A few cents per image is invisible for interactive one-offs and brutal across a 100k-recipient VDP run. Price has to be weighed against quality for the specific job. You don’t pay flagship prices for a draft thumbnail.

What to test: Cost per image at your resolution/step settings; cost of edit vs. generate; volume pricing.

10. Consistency and repeatability

Why it matters: Brand work needs the same input to yield the same (or controllably similar) output, and product likenesses must not drift. Seed control, character and style consistency, and low variance separate “brand-safe” models from “slot-machine” ones. Our consistency finding puts numbers on the drift: no model holds a described character across scenes, so identity has to come from templates and editing, not from a regeneration lottery.

What to test: Variance across runs at fixed seed; character/style consistency across a set; drift on iterative edits.

11. Commercial licensing and IP safety

Why it matters: You’re selling the printed output. Commercial-use rights, training-data provenance, indemnification, and content/safety filtering are legal exposure, not fine print. A model that’s brilliant but legally murky is a non-starter for a product customers resell.

What to test: Commercial-use terms; indemnity; provenance/transparency; enterprise and data-handling terms; content-filter behavior.

The scoring rubric

Score each model 1–5 on each criterion, then weight by your use case. Suggested weights for the three most common web-to-print contexts:

Criterion	Interactive design tool	High-volume VDP / automation	Merch / POD
1. Vector / SVG output	High	Medium	Medium
2. Resolution / max size	High	High	High
3. Visual quality	High	Medium	High
4. Text rendering	Medium	High	Medium
5. Controllability / editing	High	High	High
6. CMYK-readiness	High	High	High
7. Transparency / cutout	Medium	Low	High
8. Responsiveness	High	Medium (throughput)	High
9. Cost per generation	Medium	High	Medium
10. Consistency	Medium	High	High
11. Licensing / IP	High	High	High

Scoring scale: 1 = unusable for print · 2 = weak · 3 = workable with mitigation · 4 = strong · 5 = best-in-class.

Drop your own numbers into this rubric. The measured scores in the results table later in this guide give you a starting hypothesis to test; the qualitative notes are not a substitute for your own runs on your prompts.

Models by job

New models ship faster than any ranking can keep up with, so this section is organized by job, the way you actually route requests. Models are drawn from the roster currently wired into CE.SDK’s AI plugins.

Job A: Text-to-image (generate a new asset)

Candidates: Recraft V3, Recraft 20B, Seedream V4, Nano Banana, Nano Banana Pro, GPT Image 1, Ideogram V3.

Model	Stand-out for print	Watch-outs	Benchmark
Recraft V3	The print specialist: native vector/SVG generation, strong text rendering, brand-style controls. Often the single most print-relevant text-to-image model.	Verify max raster resolution for large-format jobs.	3.29 / 5
Recraft 20B	Faster, cheaper Recraft tier. Good for interactive iteration before a final Recraft V3 render.	Quality step-down vs. V3.	not yet in the suite
Seedream V4	High visual fidelity and resolution; strong general-purpose hero imagery.	Text rendering and vector are not its strength.	3.80 / 5 (measured on Seedream 4.5)
Nano Banana / Pro	Fast, controllable, strong prompt adherence; Pro for higher quality. Good interactive default.	Confirm commercial-licensing terms for resale.	3.24 / 5 · Pro 2.55 / 5
GPT Image 1	Strong instruction-following and comparatively reliable in-image text; good for layout-aware generation.	Latency and cost on the higher side for interactive use.	3.07 / 5 (measured on GPT Image 1.5)
Ideogram V3	Built around typography and still a credible pick for poster and typographic art, but our measured text column no longer puts it on top.	Ranks twelfth of fifteen on text accuracy in our run.	2.97 / 5

Routing heuristic: logos, icons, and typographic art go to Recraft V3 (vector; the print files use case). Words-in-image goes to whichever flagship currently tops the measured text column, which in our July 2026 run means GPT Image 1.5, FLUX.2 [pro] or Nano Banana Pro rather than a typography specialist (the text-heavy designs use case). Photoreal hero imagery goes to Seedream V4 or Nano Banana Pro (the product ads use case). Fast interactive drafts go to Recraft 20B / Nano Banana.

Job B: Image editing (adapt a customer’s asset)

Candidates: Flux Pro Kontext, Flux Pro Kontext Max, Nano Banana Edit, Nano Banana Pro Edit, Qwen Image Edit, Gemini Flash Edit, Seedream V4 Edit, GPT Image 1, Ideogram V3 Remix.

Model	Stand-out for print	Watch-outs	Benchmark
Flux Pro Kontext / Max	Strong instruction-based editing with high subject preservation: change one thing without wrecking the rest. Good for generative fill/expand and targeted edits.	Max tier costs more; budget per edit.	editing suite planned
Nano Banana Edit / Pro Edit	Fast, controllable edits; good interactive default for object removal and swaps.	Verify edge quality on fine detail.	editing suite planned
Qwen Image Edit	Strong editing with good text handling during edits.	Validate commercial terms.	editing suite planned
Gemini Flash Edit	Low latency: the responsiveness pick for interactive editing.	Quality trade-off vs. heavier models.	editing suite planned
Seedream V4 Edit	High-fidelity edits matching its generation quality.	Heavier; weigh latency.	editing suite planned

The benchmark currently measures text-to-image only; the editing models above join the leaderboard when the frozen suite adds an editing track, so their cells read “editing suite planned” rather than a score.

Routing heuristic: precise “change X, keep everything else” goes to Flux Kontext. Fast interactive edits go to Gemini Flash Edit / Nano Banana Edit. Edits that must preserve in-image text go to Qwen Image Edit.

Job C: Specialized print operations

These aren’t single “models” so much as task pipelines, but they belong in the routing table:

Task	Approach / model	Note
Vectorize (raster → SVG)	Vectorize plugin / Recraft vector	The print-critical one: protects logos and PDF/X vector output.
Background removal	`@imgly/background-removal` (in-browser)	On-device, instant, privacy-preserving. Feeds cutout paths.
Image correction & DPI	Perfectly Clear plugin + DPI validation	Auto-corrects exposure, color, sharpness, and noise; validation flags undersized images. Prefer high-res native generation over upscaling.
Text generation / copy	Claude Sonnet 4.6, GPT-4.1 Nano	Headlines, rewrites, per-recipient VDP copy.
Text-to-speech / sound	ElevenLabs (where relevant)	Less common in print; relevant for multi-channel campaigns.

The quick-reference picks

If you skip the rubric and just want today’s defaults, this is the snapshot we’d start from. Treat it as the hypothesis your own benchmarks confirm or overturn, not a verdict.

Print job	Pick	Why
Logos, icons, line art, anything that must scale	Recraft V3	The only major model with native SVG/vector output. Everything else hands you pixels to trace. Vector survives into PDF/X as real paths and prints sharp at any size.
Legible text inside the image (posters, labels, packaging motifs)	GPT Image 1.5, FLUX.2 [pro], Nano Banana Pro	These three rendered every required string in our suite exactly. The typography specialists did not: Ideogram ranks twelfth of fifteen on the measured column. Still, bake text in only when you must: real type as a separate editor layer beats all of them.
Photoreal hero imagery at print size	Seedream 4.5	Resolution is the print bottleneck, and it is the one criterion where the field genuinely splits. At default parameters Seedream returns 2048px (about 6.8” at 300 DPI); most of the field returns 1024px, which is 3.4” and a bet on the upscaler.
Transparent cut-outs for stickers, die-cuts, merch	GPT Image 1.5, and a background-removal step regardless	It is the only model in our run that reliably emits a real alpha channel. Thirteen of fifteen emit none at all, so the cut-out has to be a pipeline step, not a prompt.
”Change X, keep everything else” edits	Flux Kontext Pro / Max	Best-in-class instruction-following edits with subject preservation, which is critical when the asset is the customer’s product photo and “the AI improved it” means a reprint. The Nano Banana family is the consistency pick for variants of one subject.
Interactive drafts (speed and cost)	Recraft 20B, Gemini Flash Edit, base Nano Banana	In an editor, a 20-second generation is a bounce. Draft cheap and fast, then re-render the final with the heavyweight. The customer never needs to know two models were involved.
Data sovereignty / self-hosting	Qwen-Image / Qwen Image Edit	Apache 2.0 open weights. For European operators and government buyers who won’t send customer uploads to a US API, “we can run it ourselves” beats a quality delta.
VDP copy and headlines	Claude Sonnet 4.6	Cheap enough per recipient at 100k-mailer volume, and strong at length-constrained rewriting: the “fit this text frame” problem.

Three of those rationales carry more weight than the rest.

Vector is the criterion that decides printability. Screen products never need vector, so general models never optimized for it, and Recraft sits almost alone on the one measure that determines whether a logo prints cleanly at size. If you adopt a single routing rule from this guide, make it this one.

Resolution and color are pipeline problems the model can only start. No model outputs CMYK; every one generates sRGB. “CMYK-readiness” is really about how hard a model’s palette clips on conversion, and your ICC and soft-proofing pipeline owns the rest. Same with DPI: pair every generation path with validation and a dedicated upscaler rather than trusting native output. You can swap the model whenever you want; the print-correctness layer around it has to stay put.

Transparency is not a model feature you can shop for. When we asked every model for a transparent background and measured the alpha channel that came back, thirteen of fifteen returned none, and a few painted a checkerboard into the pixels instead. If your product sells stickers, die-cuts, or anything composited onto a garment, budget for background removal and edge cleanup as a permanent pipeline stage. Choosing a different model does not remove that stage.

One roster note: Adobe Firefly isn’t wired into CE.SDK’s plugin roster today, but for licensing-sensitive enterprises its trained-on-licensed-content and indemnification story is the strongest answer to criterion 11. Include it in your own evaluation if criterion 11 dominates your weighting.

A worked decision: which model wins for your product?

The rubric resolves to a different answer per context. Three quick reads:

Interactive design tool (non-designers personalizing templates). Weight responsiveness, controllability, and vector. Likely stack: Recraft 20B / Nano Banana for fast generation, Recraft V3 for final vector, Gemini Flash Edit for interactive edits. The customer iterates fast and the final asset is print-clean. See the in-app UGC use case for how the benchmark weights this context.
High-volume VDP / automation (100k personalized mailers). Weight cost, throughput, consistency, and licensing. Likely stack: a cost-efficient generation model at high concurrency, Claude Sonnet 4.6 for per-recipient copy, seed-locked for repeatability, all run headless. Per-unit cost dominates the decision. See the personalization-at-scale use case for the weighted read.
Merch / POD (user art on physical products). Weight transparency/cutout, resolution, and quality. Likely stack: background removal plus cutout for subjects, upscaling for resolution, Seedream V4 / Nano Banana Pro for generated designs, with hard licensing checks because the output is resold. See the merch and stickers use case, where the transparency gap decides the stack.

In every case, the conclusion is the same: no one model wins all three. The advantage is in being able to route each task to the model that wins it, and to swap models as better ones appear, without re-architecting your editor.

What makes per-job routing practical: native plugins + the AI Gateway

A criteria-driven, route-per-job strategy only works if switching models is cheap. If wiring in a new model means a new API integration, new auth, new key management, new error handling, and new billing plumbing every time, you’ll quietly standardize on whatever you integrated first, and inherit its weaknesses on every job it loses.

CE.SDK is built to avoid that:

Every image model in this guide is natively supported as an AI plugin. Text-to-image and image editing run as drop-in providers inside the editor, not bespoke integrations you build and maintain. Background removal, vectorization, and image correction (Perfectly Clear) ship as their own plugins, and text generation routes through built-in Anthropic and OpenAI providers. Adopting Recraft for vector, Ideogram for text, and Flux Kontext for edits is configuration, not three separate engineering projects.
The AI Gateway gives you one unified surface to every model. Instead of stitching together a dozen provider APIs, key schemes, and rate-limit behaviors, you call models through a single managed layer. That’s what turns “route each task to the best model” from an architecture diagram into a one-line config change, and what lets you swap a model the day a better one ships, without touching your editor or your print pipeline.
Outputs land inside the print pipeline, not beside it. Whatever model produces the asset, it flows into locked templates with bleed, safe-area, and DPI constraints, then through CMYK / spot-color / PDF/X-3 export. The model is interchangeable; print correctness is not. Print-on-demand company The Print Bar and direct-mail platform PostBuddy both built on this pipeline, embedding CE.SDK rather than maintaining their own print editor.

So the “best model” question is no longer a one-time, high-stakes bet on a single provider; it’s an ongoing optimization. The benchmark scores below tell you which model to route each job to today, and the native-plugin and Gateway architecture is what lets you act on that answer and revisit it when the scores change next quarter.

The IMG.LY GenAI benchmark: measured results

IMG.LY’s GenAI benchmark now scores these models on print-representative prompts, and the results are live and explorable at img.ly/ai-benchmarks. The table below is the top-line read for the text-to-image models in this guide: the overall score out of 5, the four print-critical criteria (each out of 5), list price per image, and median (p50) latency. All numbers are from the pilot-0 dataset (July 2026); scores are sorted by overall.

Model	Overall	Text	Color	Transparency	Resolution	$ / image	p50
Nano Banana 2 Lite	3.87 / 5	4.9	2.9	0.0	3.0	$0.02	4.0s
Seedream 4.5	3.80 / 5	4.9	2.9	0.0	5.0	$0.048	12.4s
FLUX.2	3.64 / 5	4.9	2.8	0.0	2.0	$0.013	2.0s
FLUX.2 Turbo	3.61 / 5	4.9	2.7	0.0	2.0	$0.015	2.0s
Gemini 2.5 Flash Image	3.60 / 5	5.0	1.7	0.0	3.0	$0.039	7.4s
Recraft V3	3.29 / 5	4.0	1.9	2.0	3.0	$0.04	7.4s
Nano Banana 2	3.24 / 5	5.0	3.2	0.0	3.0	$0.10	13.2s
FLUX.2 Pro	3.23 / 5	5.0	2.3	0.0	2.0	$0.04	11.5s
Qwen-Image	3.21 / 5	4.8	2.0	0.0	2.0	$0.03	6.9s
GPT Image 1.5	3.07 / 5	5.0	3.4	4.2	3.0	$0.12	34.0s
Ideogram 3.0	2.97 / 5	4.7	2.8	0.0	3.0	$0.06	17.9s
Nano Banana Pro	2.55 / 5	5.0	3.7	0.0	3.0	$0.36	23.3s

Measured on the pilot-0 suite (July 2026): 37 prompts times 3 seeds per model, every image scored 0 to 5 per criterion by an automated vision judge. Two models carry newer versions than the guide names them by: Seedream V4 is measured as Seedream 4.5, and GPT Image 1 as GPT Image 1.5. The suite also covers Seedream 5.0 Lite, Luma Photon, and Stable Diffusion 1.5, which this guide’s roster does not name; their scores are on the rankings page. The overall column is an unweighted blend, so read it alongside the per-use-case weighting, not as a single leaderboard: the cheap fast model tops the raw average while the priciest lands last. Full methodology is at img.ly/ai-benchmarks/methodology, and every model, prompt, and generated image is explorable at img.ly/ai-benchmarks.

A few things the numbers make plain, and that the criteria above predicted. Transparency is close to unsolved: 13 of 15 tested models emit no real alpha channel at all (the transparency finding), and GPT Image 1.5 is the only one that scores meaningfully on it. Brand color is broadly weak: measured with CIEDE2000, no model reliably hits an exact hex (the brand-color finding). Text is the one column where the field mostly clears the bar: on our per-required-string measure several general-purpose flagships render every string in the suite exactly, while the model with the biggest reputation for text, Ideogram, actually lands near the bottom of the column, not the top (the text-reliability finding). The gaps open exactly where print work gets hard. Averaged across the field, the neon sign in Japanese scores 3.91 and the dense packaging label 4.40, against 4.91 for a greeting-card cover. Latin headlines are close to solved; small type and non-Latin scripts are not.

The same numbers, weighted for print

The overall column is the wrong lens for a print product, which is the whole reason the benchmark carries use-case weights. Reweighted for print files (resolution 35%, color 30%, text 15%, transparency 10%), Seedream 4.5 leads at 3.64, ahead of Seedream 5.0 Lite at 3.50 and GPT Image 1.5 at 3.45: native output size at default parameters does most of the work, and the two models that return 2048px start the job at twice the printable width of the 1024px field.

Reweight again for merch and stickers, where alpha carries 35%, and the order breaks completely: GPT Image 1.5 wins at 3.72, nearly a full point ahead of Recraft V3 at 2.78, because it is the only model that reliably returns a real alpha channel. Same measurements, different job, different answer. That inversion is the argument for routing per job rather than standardizing on one provider.

The gated report is coming to subscribers first. AI Image Models for Print Production: The Benchmark Report packages the weighted per-use-case totals, the full test set, and the print-specific analysis into one PDF. Subscribe to get it before it is public.

The criteria and the routing logic above stand on their own. The scored numbers are what the suite adds: a measured, repeatable answer for each cell, refreshed as models change. For why the benchmark is built this way, and what else the run turned up, see Introducing IMG.LY AI Benchmarks.

Bottom line

Pick criteria before you pick models. For web-to-print, the criteria that screen benchmarks ignore (vector output, print resolution, CMYK behavior, text legibility, licensing) are exactly the ones that decide whether a beautiful generation is a sellable print. Score the current models against those criteria for your context, route each job to the model that wins it, and run it all through CE.SDK’s native AI plugins and AI Gateway so re-routing tomorrow, after the next leaderboard reshuffle, is a config change rather than a rebuild.

Companion piece: How to Leverage Generative AI in Web-to-Print covers the use cases, integration patterns, and print-specific pitfalls behind these model choices.

Top 5 Generative AI APIs for Creative Apps in 2025: A Developer’s Guide (GPT-4o, Gemini, Firefly, and More)

Jan — Mon, 14 Apr 2025 07:57:51 GMT

If you’re working on creative tooling right now, anything from a lightweight design editor to a marketing automation suite, you’re probably already thinking about or actively working on bringing image generation into the mix. The tech is here, expectations are rising, and if your users can’t type a prompt and get a visual back in seconds, your app might feel like it’s lagging behind.

But choosing which AI model to integrate, and how, isn’t all that straightforward. There’s a growing ecosystem of APIs out there, and they don’t all behave the same way, some are designed for open-ended creativity, others for structured workflows. Some offer pixel-perfect fidelity with fine control, others lean toward rapid ideation. And Importantly in our content not all of them are equally accessible to developers.

This is a guide to help you make sense of it all. What models are available, how do they differ, and what should you consider when embedding them into your product. This isn’t supposed to be a hype piece or a leaderboard, just a clear-eyed look at what’s out there and what’s coming.

OpenAI GPT-4o

GPT-4o is OpenAI’s next-gen multimodal model, currently only available inside ChatGPT. It can take both text and images as input and is capable of generating image outputs in context.

The potential upside is significant. With GPT-4o, you may soon be able to create deeply interactive creative tools where users chat, sketch, and prompt all within a single UI. It’s likely to support richer input types and more natural iteration flows.

The main downside is availability. There’s no API yet, so you can’t build on it directly. It also remains to be seen how OpenAI will expose generation tools, whether through a dedicated endpoint or via the chat interface.

GPT-4o is right for you if you’re planning ahead and want to design for a future where multimodal interaction is the norm. It’s not something you can use today, but it should inform how you architect your UI and prompt handling.

OpenAI DALL·E 3

DALL·E 3 is OpenAI’s current image generation API, available via both the platform and ChatGPT. It translates text prompts into images and is known for interpreting prompts accurately and producing clean, useful visuals.

Its strengths are clarity, commercial readiness, and reliability. It’s easy to use and integrates well into frontend flows that involve text-to-image generation.

However, it lacks features like inpainting, style tuning, or detailed layout control. You also don’t get deep iteration features: each image is a new generation.

DALL·E 3 is a good fit if you want high-quality results from text prompts with minimal complexity. It’s especially useful for marketing visuals, content automation, and simple design tools.

Google Gemini (Imagen)

Gemini, powered by Google’s Imagen models, is available via fal.ai, Makersuite, Vertex AI. It supports not only text prompts, but also sketches and inpainting, making it one of the more flexible APIs for creative work.

Its big advantage is control. You can use sketches to guide composition and make visual edits to generated outputs. That makes it ideal for iterative design processes.

The downside is that it can be tricky to navigate Google’s ecosystem. Access and feature sets can change quickly, and the integration overhead is higher than OpenAI.

Gemini is right for you if your product needs image refinement, visual grounding, or sketch-to-image workflows. It fits e-commerce editors, mockup tools, and design collaboration features.

Adobe Firefly

Firefly is Adobe’s generative image model, integrated tightly into Creative Cloud. It stands out for its licensing model: images are trained on Adobe Stock, meaning they’re cleared for commercial use.

The biggest strength here is trust and integration. Designers already using Photoshop or Illustrator can use Firefly to generate content directly in their layers and work non-destructively.

The drawback is API access. There is no public endpoint for Firefly yet, and its features are embedded in Adobe’s own ecosystem.

Firefly is a strong option if you’re building for agencies, brand teams, or other users with high expectations around copyright and integration with existing Adobe workflows.

Stability AI (SDXL)

Stability AI offers an open-source model suite, with SDXL as the flagship for high-resolution image generation. It supports both text and image inputs and can be run locally or hosted via services like Replicate.

Its biggest advantage is flexibility. You can fine-tune models, build custom workflows, or even run inference offline. It’s ideal for teams that want full control.

The challenge is quality consistency. Compared to closed models like DALL·E, SDXL may require more tuning, and prompt engineering matters more. Hosting and scaling also require more effort.

SDXL is right for you if you need an open, customizable system that fits into a broader pipeline. It’s a solid choice for research tools, OSS projects, and privacy-conscious applications.

Midjourney

Midjourney is a proprietary model with a focus on aesthetic, stylized image generation. It runs exclusively via Discord and is popular for its distinctive look and community-driven prompts.

Its upside is the quality of its visuals, especially for stylized scenes or concept art. Designers often use it as an ideation tool.

The limitation is integration. There’s no API, no SDK, and limited ways to embed it in your own product beyond scraping or bots.

Midjourney is best used as an inspiration engine. If your workflow includes moodboarding or creative brainstorming, it can supplement (but not power) your product.

Hugging Face

Hugging Face is a hub for open models, offering hosted APIs for SDXL variants, Playground v2, and other creative generation tools.

The main benefit is diversity. You can try multiple models, experiment with variations, and deploy quickly using their hosted inference endpoints.

That said, it’s not always ready for production. Some models lack documentation or support, and you may need to piece together features.

Hugging Face is a great choice for experimental projects, prototyping, or if you want to stay vendor-neutral and build your own stack.

Runway Gen-2 and Leonardo.Ai

Runway and Leonardo are rising players at the edge of AI and media. Runway’s Gen-2 supports text-to-video and animated image generation, while Leonardo focuses on style-consistent 2D asset generation.

These platforms bring specialization. Runway is tailored to video and cinematic scenes, while Leonardo offers structured design features for asset creators.

They’re less open from a dev perspective. APIs are limited, and integration support is still maturing.

Use these tools if your use case leans into video, motion, or asset generation for games and content libraries. They’re best when you’re not looking to build your own editor, but to enhance creative capacity.

Quick Comparison

Model/API	Input	Output	Control	API Access	Best For
GPT-4o (OpenAI)	text, image (chat)	image (likely)	medium-high	not yet	assistants, multimodal UIs
DALL·E 3	text	image	medium	yes	content tools, illustrations
Gemini (Google)	text, sketch	image	high	yes	e-commerce, product mockups
Firefly (Adobe)	text	image, layers	very high	no	professional design tools
SDXL	text, image	image	high	yes	custom tools, OSS projects
Midjourney	text	image	very high	no	stylized inspiration
Hugging Face	text, image	image	medium-high	yes	experimentation, open models
Runway Gen-2	text	video/image	medium	yes	motion design, AI video
Leonardo.Ai	text	image	high	limited	game assets, style templates

Conclusion

If you’re building for creative users, especially those used to real-time feedback and control, then how you wrap these APIs into your workflow matters more than which model you use. It’s not just about generating images. It’s about how you let users prompt, refine, iterate, and remix inside your canvas.

That’s the opportunity here. Not just plugging in a model, but designing a loop where generation feels native to creation. The APIs are improving fast. The real challenge, and the real product value, is in how you build around them.

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