- Home
- Image model comparison
- FLUX.1 Kontext [max] vs. Stable Diffusion XL 1.0
FLUX.1 Kontext [max]vs.Stable Diffusion XL 1.0
Which one is better?
In our own test, FLUX.1 Kontext [max] leads. Neither is in the arena. Neither has an API price.
Test images
Same task, the first image of each. The task with the biggest difference is preselected.
![FLUX.1 Kontext [max]: Infographic, first image from the fixed test run](/_astro-img/23f4b014668efc78/1024.webp)

Result for “Infographic”:FLUX.1 Kontext [max] ahead
What the rating looks at: Numbers, labels, and visual organization.
“Ahead”: better in all three rating rounds. “Slightly ahead”: a higher value, but not in every round.
Test protocol of the images
Each test attempt uses the same archived prompt and one request. The first output of that attempt counts. Repeats after technical test failures are disclosed for the affected images. Files are normalized to 1,024 × 1,024 pixels without cropping. Provider resolution, randomness, quality tier and prompt processing can still differ. The images are a practical visual test, not a reconstruction of the arena configuration.
Character and detail
FLUX.1 Kontext [max]: fal, aspect ratio 1:1, n=1, seed=2413815648, Fal schema has no resolution field; official 1:1 example returns 1024x1024, estimated cost $0.080000, generated Nov 1, 2026, 9:03 PM. Source
Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=268378870826470633; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens, tokenizer_2 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:07 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6
FLUX.1 Kontext [max] image SHA 256: 11ac5858ed56d1db44139faebd83b9aa3e13fb9e8cadd6748d4bfc9a20a7b85b
Stable Diffusion XL 1.0 image SHA 256: 26d3c3d3e687614e24dd12191246bc1d96c1576d31f9211840a3e02b64d4d507
Infographic
FLUX.1 Kontext [max]: fal, aspect ratio 1:1, n=1, seed=372152929, Fal schema has no resolution field; official 1:1 example returns 1024x1024, estimated cost $0.080000, generated Nov 1, 2026, 9:03 PM. Source
Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=5455008934753613262; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:08 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f
FLUX.1 Kontext [max] image SHA 256: cf1d34943829e1361560aee05277bb096e19399f7d231ee114ba1e6d3a865787
Stable Diffusion XL 1.0 image SHA 256: ebfe8d1c223478c94ff1973ab2600cd4f696b40b8c3388ca40f26f52c83afdb2
Product photography
FLUX.1 Kontext [max]: fal, aspect ratio 1:1, n=1, seed=314349008, Fal schema has no resolution field; official 1:1 example returns 1024x1024, estimated cost $0.080000, generated Nov 1, 2026, 9:03 PM. Source
Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=490593220006868735; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens, tokenizer_2 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:06 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3
FLUX.1 Kontext [max] image SHA 256: 85f361575b24f97fc43e88b3fdc8e6c12fb1077d95f11d1704447222bac13f59
Stable Diffusion XL 1.0 image SHA 256: f59af9c84f6fbb3e4ca34bf87ebb0f7c088e8caa520fa4cdf815a96d1bffda83
Typography and layout
FLUX.1 Kontext [max]: fal, aspect ratio 1:1, n=1, seed=4136483547, Fal schema has no resolution field; official 1:1 example returns 1024x1024, estimated cost $0.080000, generated Nov 1, 2026, 9:02 PM. Source
Stable Diffusion XL 1.0: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/stable-diffusion-xl-base-1.0@462165984030d82259a11f4367a4eed129e94a7b; native 1024x1024, 1:1; one request, first output; steps=50, guidance=5.0, seed=253938565953989943; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:05 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419
FLUX.1 Kontext [max] image SHA 256: f0591cb9970c7608e7bf3a52c32c88301b622673e3c250fd9dd41d32e1673f1c
Stable Diffusion XL 1.0 image SHA 256: c81e5d0d70afcb53fcffb51a0d548041fd0b33df300aed14b8ecd354aaaaec4e
The numbers at a glance
FLUX.1 Kontext [max]
not in the arena
Stable Diffusion XL 1.0
not in the arena
FLUX.1 Kontext [max]
Rank 71 of 108
Stable Diffusion XL 1.0
Rank 100 of 108
FLUX.1 Kontext [max]
no API price
Stable Diffusion XL 1.0
no API price
Ranks among the 21 image models with an arena rating and the 108 with a GraduallyBench run; prices compared with 28 models with an API price.
FLUX.1 Kontext [max]
- Subscription
- No Black Forest Labs subscription includes this model. Through other providers from ≈ €12.72 a month (Leonardo.Ai Essential). 15 plans in Germany.
- Via API
- No API price known. Access: API.
- Weights
- unknown.
Stable Diffusion XL 1.0
- Subscription
- n/a No plan in Germany names exactly this model.
- Via API
- No API price known. Access: API, local.
- License
- Open weights.
Subscriptions only count plans that name exactly this model. Subscription prices include VAT, API prices are in dollars.
GraduallyBench from Nov 3, 2026 · Subscription database from Sep 29, 2026 (38 days old)
All data side by side
The better value is bold. For ratings only when the gap is clear.
| Kennzahl | FLUX.1 Kontext [max] | Stable Diffusion XL 1.0 |
|---|---|---|
| Ratings | ||
| Arena ratingfrom votes by users, higher is better | n/a | n/a |
| Arena rank | n/a | n/a |
| Arena votesmore votes, a safer rating | n/a | n/a |
| GraduallyBenchour own rating, 0 to 100 | 39 | 13 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 46 | 5 |
| Product photographyMaterials, reflections, and fine details | 29 | 13 |
| Character and detailAnatomy, clothing, and spatial consistency | 27 | 28 |
| InfographicNumbers, labels, and visual organization | 54 | 5 |
| API price | ||
| Per imagelower is better | n/a | n/a |
| 100 images | n/a | n/a |
| Profile | ||
| Released | n/a | n/a |
| Weightsopen means: runs locally | unknown | open |
| Lifecycle | Edit-first model line; API also supports image creation from text, but the main GraduallyBench tasks are not edit-based | Older open checkpoint; provider still lists it in its legacy model overview |
| Access | API | API, local |
| Parameters | not published | not published |
| Largest output | not consistently published | not consistently published |
| Plans in Germanynaming exactly this model | 15 | n/a |
API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.
Sources: FLUX.1 Kontext [max] model source, status source (verified Nov 1, 2026) · Stable Diffusion XL 1.0 model source, status source (verified Nov 1, 2026)
Arena ratings by category
From blind votes on LMArena. “Ahead” means the lead is clear.
No shared arena score in the stored snapshot
The stored benchmark database contains no measurement for FLUX.1 Kontext [max] and Stable Diffusion XL 1.0 from the same arena and category. Older model versions are not used as substitutes.
More comparisons
Compare FLUX.1 Kontext [max] and Stable Diffusion XL 1.0 with the models people compare them with most often.