Krea 3 vs MAI Image 2.5 Pro: A Megapixel Is Not the Same as a Rank

One of these models has a measured third-party rank and cannot return a 2K image. The other is a day old, has no rank at all, and renders natively at 2K. MAI-Image-2.5-Pro, Microsoft AI's editing and generation model, sits mid-board on Artificial Analysis's image editing leaderboard at 1,117 Elo from 14,751 blind comparisons — eleventh by score on a board led by OpenAI's GPT Image 2.5 Sunburst at 1,198. Krea 3, released by Krea on 15 October 2026, publishes a flat $0.06 per image at 2K and under eight seconds end to end, and has no independent score of any kind. So the honest framing is a measured middle-of-the-table model against an unmeasured one, and if you ship product imagery for a living the resolution ceiling decides it before the Elo does. a single endpoint across 200-plus models with provider list price passed through at 0% markup, automatic failover when a provider degrades, and a routing DSL for pipelines that chain a generation step into an edit step. automatic failover when a provider degrades

The 1-megapixel ceiling is the whole argument

Microsoft documents MAI-Image-2.5-Pro's output as capped at 1,048,576 pixels — the equivalent of 1024×1024, about one megapixel. Microsoft's own marketing refers to 8K; the published cap does not support that reading, and the gap between the two is the first thing to understand about the model.

• MAI-Image-2.5-Pro output — capped around 1 megapixel. Whatever token budget you spend, the image comes back at roughly 1024×1024.

• Krea 3 output — native 2K, with 4K for print. Krea's launch page names 2K native explicitly and lists 4K for print, large screens and campaign assets.

• The pixel count difference is not cosmetic at 4× to 16×. A 2K edit carries roughly four times the pixels of a 1-megapixel output; 4K carries about sixteen times. For a hero image on a product page, a print insert or a screen larger than a phone, that is the difference between an asset and a placeholder.

• Krea also does something MAI-Image-2.5-Pro's documentation does not promise. Krea 3 finds the region it needs to change and renders only that, leaving the untouched pixels as they were. Krea measures this with a share-of-pixels-changed figure in its own Precise mode, which is a vendor measurement of a vendor configuration and should be labelled as one.

The comparison is not that one model is better. It is that the two are solving for different outputs. MAI-Image-2.5-Pro was built to put a well-composed, well-lit, correctly-lettered image in front of a consumer, where a megapixel is plenty. Krea 3 was built to put a 2K asset in a catalogue, where it is not.

Pricing: a flat rate against a token meter

The pricing structures are as different as the outputs, and the token meter is the one that will surprise you.

• Krea 3 — $0.03 per image at 1K, $0.06 at 2K. Per image, published, flat. Krea's FAQ says the launch price is the list price and that the company expects it to go down rather than up.

• MAI-Image-2.5-Pro — metered by tokens. Microsoft's preview rate card lists $5 per million text input tokens, $8 per million image input tokens and $106 per million image output tokens. Microsoft does not publish tokens-per-image.

• That converts to roughly $108.50 per 1,000 images at 1024×1024 using Artificial Analysis's token-to-image conversion, which is the figure to hold loosely because it is a derived number rather than a vendor-published one.

• Against its own siblings, MAI-Image-2.5-Pro is priced as the premium tier — around 2.3 times MAI-Image-2.5 at roughly $48 per 1,000 and about 5.4 times MAI-Image-2.5-Flash at roughly $20 per 1,000. And these are preview rates, which do not bind the general-availability price.

Take an editing volume that any e-commerce team would recognise — 100,000 edits — and the shapes diverge sharply. Krea 3 at 2K is $6,000 at list price. MAI-Image-2.5-Pro at the derived rate is in the region of $10,850 for edits that come back at about a quarter of the resolution. Token metering also means the bill moves with the size of the instructions and the number of reference images, which is a property a per-image price simply does not have.

What each vendor claims, and who checked it

This is where the two models are furthest apart, and it runs the opposite way to the resolution comparison.

MAI-Image-2.5-Pro has real third-party measurement, and the measurement puts it in the middle of the field rather than at the top of it. On Artificial Analysis's image editing leaderboard, read 16 October 2026, it sits at 1,117 Elo from 14,751 blind comparisons — eleventh by score, on a board led by OpenAI's GPT Image 2.5 Sunburst at 1,198. The same snapshot places it fourteenth by score in text-to-image at 1,113 Elo from 11,776 samples, against a text-to-image leader at 1,217. That is a 104-Elo gap, and a considerably less flattering position than the editing placement suggests. Krea 3 appears on neither board.

Microsoft's own numbers for it are vendor-reported and unreproduced: 96.8% text-rendering accuracy across Chinese, English, Japanese, Korean and Spanish; a claimed 27% improvement in prompt adherence over GPT-Image-1.5 on internal evaluations; 94% multi-image character consistency. Microsoft also cites figures like "up to 84% lower GPU cost" in specific PowerPoint and OneDrive scenarios, which is a statement about two Microsoft products rather than a general cost claim, and should not be read as one.

Krea 3 has no third-party measurement at all. It launched on 15 October 2026 and the launch page is the entirety of the public record: under eight seconds end to end at 2K, up to six reference images, native 2K with 4K for print, trainable LoRAs on a product, person or style. Krea's speed demo publishes its own methodology — median request-to-result time of a twelve-image batch including queue time, measured on Krea's own platform between 24 September and 1 October 2026 — and one comparison price on that page carries the note "a draft estimate pending validation." Naming your own method on the launch page is more than most labs do. It is still a vendor measuring itself, and it is not a substitute for 14,751 blind votes.

Consistency, training and where the two capabilities actually differ

Both models make a consistency promise, and they get there by different mechanisms.

• MAI-Image-2.5-Pro — multi-image character consistency at 94%, vendor-reported. The mechanism is prompt and reference driven. Microsoft states its MAI models are trained on clean, traceable data without distillation from third-party models, which is a provenance claim rather than a capability one.

• Krea 3 — LoRA fine-tuning on a product, person or style. Krea calls it the first editing model you can train this way; that is a claim about the field, and the checkable part is that Krea's API reference publishes a "Train a custom style (LoRA)" endpoint next to the Krea 3 beta endpoint. Training is a data-collection step with its own cost, not a parameter.

• Reference counts — Krea 3 up to six per edit; MAI-Image-2.5-Pro's spec sheet publishes a 32,000-token text input ceiling, a 131k context window and a 4,096-token output ceiling rather than a reference-image count.

• Interface — Krea 3 adds bounding boxes for element placement and multi-turn edits that hold across turns; MAI-Image-2.5-Pro's published capability set covers targeted object replacement, layout changes, in-image text updates and artefact cleanup.

The genuinely different capability is LoRA training. Everything else on that list is a variation on reference-conditioned editing, which both do. If your recurring problem is a specific SKU or a specific face that must not drift across hundreds of images, one of these two ships a mechanism for that and the other ships a percentage.

Availability, and the preview asterisk

MAI-Image-2.5-Pro is served on Microsoft Foundry and in the free MAI Playground, and it was announced on 23 July 2026 in public preview. Preview is a status, not a formality: it means the rate card can change, the endpoint shape can change, and the model can be withdrawn. Its sibling, MAI-Image-2.6, entered private preview on 19 August 2026, so the family is still moving under the model you would be integrating against.

Krea 3 is served on krea.ai, through the Krea API at a documented beta endpoint, and through a set of third-party distribution partners. Krea also offers self-hosting for enterprise customers on their own GPUs, with licence terms and hardware sizing to be discussed — which is a procurement conversation rather than a product feature.

Neither model is on OrcaRouter's catalogue. Our image line is OpenAI's GPT-Image models, xAI's Grok Imagine image model and Google's Imagen and Gemini image families; Microsoft's MAI-Image line and Krea's models are both absent. Where we do change the picture for a buyer running either of these is one layer up: a single endpoint across 200-plus models with provider list price passed through at 0% markup, automatic failover when a provider degrades, and a routing DSL for pipelines that chain a generation step into an edit step. A preview model from a large vendor is exactly the kind of dependency worth having a failover path behind, and a beta model from a small one is even more so.

Which one your workload wants

• Highest measured editing quality, output around a megapixel, enterprise procurement already inside Microsoft — MAI-Image-2.5-Pro. The 1,117 Elo is the strongest evidence either model has, and on consumer-facing or screen-only assets the resolution cap may never bind.

• 2K or 4K output, per-image budgeting, high volume — Krea 3. Four times the pixels for a little over half the derived per-image cost, with a published flat price and a stated intent to reduce it.

• A brand asset that has to stay identical across a campaign — Krea 3. LoRA training on the editing model is the only published mechanism on either side, and it is the capability Krea is leading with.

• A model you must be able to put a number next to in a procurement document — MAI-Image-2.5-Pro. Krea 3 has a price, a latency claim and a reference count. It does not have a rank.

The most useful thing you can do with this pair is stop treating them as substitutes. One is a measured quality leader with a resolution ceiling and a preview label; the other is an unmeasured throughput play with the resolution and the price a catalogue needs. Run the first on the hero shots and the second on the thousand variants, and the argument about which is better stops mattering.