Stable Diffusion 3.5 ranks #20 of 20 in our ai image models testing. the ecosystem that started it all, twenty-one months without a successor.
48/100
still the widest tooling ecosystem in open image models, and now a full generation behind — there is no stable diffusion 4, whatever you have read.
why people look for an alternative
−no new image model in twenty-one months
−quality now a full generation behind
−stability has pivoted to audio and brand tooling
stay with Stable Diffusion 3.5 if widest tooling and workflow support of any image model is the thing you care about most — nothing below beats it on that.
#2 in ai image models · the independent lab that reasons about layout before it draws
90/100
verdictsecond on both public leaderboards and the only model here with genuine 16-megapixel native output — the strongest thing built outside a hyperscaler.
Reve 2.1 vs Stable Diffusion 3.5
Stable Diffusion 3.5
Reve 2.1
price
free below $1M annual revenue
~$0.04 / image on fal's reve endpoint
free tier
yes
yes
access
open weights + api
closed api + web app
max resolution
1–2K typical
4096×4096 native (16mp)
text rendering
weak by 2026 standards
strong, incl. non-latin
editing
everything, via the ecosystem
instruction edit + 8-image remix
license
community licence, $1M revenue cap
commercial via api terms
switch fordense compositions, posters, and anything that needs to stay coherent at very large sizes
pros
+#2 on both artificial analysis and lmarena
+4096×4096 native output, the highest here by a wide margin
#3 in ai image models · google's gemini 3.1 flash image — the best default for production work
89/100
verdictnot the top of any leaderboard, and still the model we'd default to — the balance of price, speed, resolution and character consistency is better than anything above it.
Nano Banana 2 vs Stable Diffusion 3.5
Stable Diffusion 3.5
Nano Banana 2
price
free below $1M annual revenue
$0.067 / image at 1K direct from google
free tier
yes
yes
access
open weights + api
closed api (gemini, vertex) + apps
max resolution
1–2K typical
512px to 4K
text rendering
weak by 2026 standards
strong, incl. in-image translation
editing
everything, via the ecosystem
excellent targeted editing
license
community licence, $1M revenue cap
commercial; synthid watermarked
switch forhigh-volume commercial pipelines that need consistent characters and predictable cost
pros
+best price/quality/speed balance on the market
+five-character, fourteen-object consistency is class-leading
+#3 on the editing leaderboard, ahead of nano banana pro
+4K output, web grounding, and 50% batch pricing direct from google
cons
−clearly behind gpt image 2 on aesthetic preference
−the nano banana naming makes version choice genuinely confusing
−~19% markup if you route through fal instead of google
#5 in ai image models · microsoft's own model, strong scores, awkward to buy
84/100
verdictthird on artificial analysis and genuinely excellent at text and stylised art — held back almost entirely by how hard it is to buy if you aren't already on azure.
MAI-Image-2.5 vs Stable Diffusion 3.5
Stable Diffusion 3.5
MAI-Image-2.5
price
free below $1M annual revenue
token-billed: $47 / 1M image-output tokens
free tier
yes
no
access
open weights + api
closed api (foundry, openrouter)
max resolution
1–2K typical
not published
text rendering
weak by 2026 standards
excellent
editing
everything, via the ecosystem
control-with-preservation editing
license
community licence, $1M revenue cap
commercial via api terms
switch forteams already inside azure or microsoft foundry
pros
+#3 on artificial analysis text-to-image, #4 on editing
+large generational jump in text rendering and stylised art
+editing preserves the rest of the frame well
+already embedded in powerpoint and onedrive
cons
−token-only pricing with no published per-image cost
#6 in ai image models · google's gemini 3 pro image — the accuracy specialist, now overpriced
83/100
verdictstill the best model for grounded, factually accurate imagery — but its own cheaper stablemate now beats it at editing, which makes the price hard to defend.
Nano Banana Pro vs Stable Diffusion 3.5
Stable Diffusion 3.5
Nano Banana Pro
price
free below $1M annual revenue
$0.134 / image at 1K–2K, $0.24 at 4K
free tier
yes
yes
access
open weights + api
closed api (gemini, vertex) + apps
max resolution
1–2K typical
4K
text rendering
weak by 2026 standards
strong
editing
everything, via the ecosystem
strong, #5 on editing board
license
community licence, $1M revenue cap
commercial; synthid watermarked
switch forinfographics and anything where factual accuracy in the image matters more than cost
pros
+web grounding produces genuinely factual imagery
+best-in-family world knowledge for infographics
+native 4K output
+google reliability, sdks and batch pricing
cons
−beaten on editing by nano banana 2 at half the price
−slowest of the nano banana family
−$0.24 at 4K is expensive for what it now delivers
#10 in ai image models · the most completely open release in the category
77/100
verdictthe top open-weight model on artificial analysis, released with datasets and training recipes — if provenance matters to you, nothing else is close.
NVIDIA Cosmos3 vs Stable Diffusion 3.5
Stable Diffusion 3.5
NVIDIA Cosmos3
price
free below $1M annual revenue
free — open weights
free tier
yes
yes
access
open weights + api
open weights, self-hosted
max resolution
1–2K typical
model-dependent
text rendering
weak by 2026 standards
adequate
editing
everything, via the ecosystem
via community tooling
license
community licence, $1M revenue cap
OpenMDW-1.1 — commercial ok
switch forresearch, fine-tuning, and teams who need to see what the model was trained on
pros
+highest-ranked open-weight model on artificial analysis
+openmdw-1.1 licence permits commercial use
+datasets and training recipes published, not just weights
+agentic planning rather than straight denoising
cons
−heavier infrastructure requirement than hidream-o1
every tool on this page went through the same test as Stable Diffusion 3.5 — same tasks, same order, scored the same way. the comparison tables are the figures from that testing, not vendor spec sheets.