10 Best AI Image Generators in 2026

In this guide, I'll review the 10 best AI models for generating images in 2026, covering output quality, prompt adherence, speed, pricing, and what each model is good at, so you can pick the right one without burning credits on trial and error.

What Factors Should Be Considered When Evaluating AI Image Generators?

Output Quality and Realism

This is the first thing I looked at. Can the model generate images that actually hold up at full resolution, or do they fall apart when you zoom in? I paid attention to fine details like hands, text rendering, facial features, and background coherence. Some models nail the overall composition but produce obvious artifacts when you look closer.

Note: I'm going to compare all AI image generation models with the same prompt so that we can see the difference:

A cinematic dusk scene in a floating coastal megacity carved into limestone cliffs. Bioluminescent turquoise water illuminates wet stone below. In the foreground, a weathered archivist with cybernetic eyes repairs a glowing glass memory sphere projecting holographic stars, birds, and manuscripts. Renaissance-inspired robes mixed with futuristic materials, intricate embroidery visible. Behind them: vertical gardens, neon alien signage, steam vents, and slow-moving airships in misty clouds. Lighting blends warm candlelight, cool twilight, and neon reflections; teal-and-amber color palette. Hyper-realistic digital painting, shallow depth of field, 50mm lens, soft film grain, cinematic sci-fi concept art.

Prompt Adherence

A model can produce stunning images. But if it ignores half your prompt, it's not useful for production work. I tested each model with complex, multi-element prompts to see which ones could follow detailed instructions without dropping objects, changing colors, or misinterpreting spatial relationships. This matters more than most people think, especially if you're generating assets for specific campaigns or brand guidelines.

Speed and Cost Per Generation

Some models take 30 seconds. Others take 3. When you're running hundreds of generations a week, that gap adds up fast, both in time and credits. I compared cost-per-image across models, factoring in resolution and quality settings.

Supported Resolutions and Aspect Ratios

Not every model handles non-standard aspect ratios well. If you need vertical images for social media or ultra-wide banners, some models will stretch or crop awkwardly. I specifically tested 1:1, 16:9, 9:16, and 4:3 outputs for each model.

What Are The Best AI Image Generators in 2026?

The best AI image generators in 2026 are fal, Nano Banana 2, and FLUX.2 [pro].

Here's my shortlist of the 10 best models I reviewed:

fal

fal.ai (that's us) is the best place to generate AI images in 2026 because our platform gives you access to 1000+ generative AI models (including every model on this list) through a single API with pay-per-use pricing and no GPU management.

Full disclosure: Even though fal is our platform, I'll provide an unbiased perspective on why it's the best option for generating images in 2026.

Instead of signing up for separate accounts with Black Forest Labs, Google, OpenAI, ByteDance, and Ideogram, you integrate once with fal and get access to all of them. Same API key, same billing, same integration pattern. Swap one model endpoint string for another, and you're generating with a different model. No code changes beyond that.

But the real reason fal sits at #1 isn't just model access. It's speed. fal built its inference engine from scratch with custom CUDA kernels optimized for specific model architectures, rather than wrapping general-purpose frameworks like most competitors do. The result? Cold starts of 5-10 seconds versus 20-60+ seconds on other platforms, and FLUX models running up to 4x faster on fal than on competing platforms.

Here are the four things that make fal the best platform for AI image generation.

One API for Every Image Model You Need

Instead of juggling separate integrations with FLUX, Seedream, Recraft, Ideogram, GPT Image, and a dozen other providers, you integrate once with fal. The same API pattern works across all 1000+ models. Your auth, error handling, queue logic, and billing stay identical whether you're generating with FLUX.2 [pro] for photorealism, Recraft V3 for vector art, or Nano Banana 2 for fast semantic generation.

What this means in practice: you can ship a feature using FLUX.1 [schnell] for fast drafts, let users upgrade to FLUX.2 [pro] for final output, and add Recraft V3 for typography-heavy assets, all without touching your integration code.

Generated using Nano Banana 2 on fal.

When a new model drops, fal typically has it available on day one. fal's relationship with Black Forest Labs, for example, means FLUX.2 [pro] launched on fal on release day. And Google's Nano Banana 2 is already live on the platform.

Six lines of code to get started:

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/flux/dev", {
  input: {
    prompt: "Photo of a rhino wearing a tailored suit at a bar",
  },
});

Every model also has a playground where you can test it in your browser before writing any code.

Speed That Actually Matters for Production

Our engineering team writes custom CUDA kernels for specific model architectures and uses techniques like epilogue fusion to eliminate unnecessary memory transfers between GPU operations.

The numbers aren't just marketing. A FLUX.1 [schnell] image generates in roughly 1-2 seconds on fal.

The infrastructure handles autoscaling automatically: regional GPU routing sends requests to the nearest available cluster, a custom CDN delivers generated content with minimal latency, and the system expands from zero to thousands of GPUs based on demand without any configuration on your side.

For teams running real-time features like live previews, interactive editors, or streaming generation, this gap is the difference between usable and unusable.

Pay-Per-Use Pricing With No Idle Costs

fal charges per output rather than requiring you to reserve GPU capacity. You don't pay when your app is idle. You don't estimate capacity in advance.

For image generation specifically, pricing starts at $0.003 per megapixel for FLUX.1 [schnell] and goes up to $0.15 per image for premium models like Nano Banana Pro.

The range means you can pick the right model for each task and only pay for what you actually generate.

No hidden fees for API calls, storage, or CDN delivery. You pay for generation and computing: end of the story.

Pricing

fal uses pay-as-you-go pricing with no subscriptions or minimum commitments.

Here's a snapshot of image generation costs:

• FLUX.1 [schnell]: $0.003/megapixel (fastest, lowest quality).
• FLUX.2 [dev] Turbo: ~$0.008/image.
• FLUX.1 [dev]: $0.025/megapixel.
• FLUX.2 [pro]: $0.03/megapixel.
• Seedream V4.5: $0.04/image.
• Recraft V3: $0.04/image (raster), $0.08/image (vector).
• Nano Banana 2: $0.08/image.
• Nano Banana Pro: $0.15/image.

Pros & Cons

Pros:

• Access to 1000+ models through a single API, including every model on this list.
• Fastest inference engine on the market with custom CUDA kernels and 5-10 second cold starts.
• Pay-per-use pricing with no idle costs, subscriptions, or minimum commitments.
• SOC 2 compliant and ready for enterprise procurement processes.

Cons:

• Per-generation pricing can feel expensive for casual, low-volume use compared to running models locally.
• No IP indemnity for generated content. If your use case requires legal coverage for outputs, you'll need to build that layer yourself.

Nano Banana 2

Best for: Teams that need fast, vibrant image generation with strong text rendering, character consistency, and semantic understanding at a mid-range price point.

Similar to: Nano Banana Pro, GPT Image 1.5.

Nano Banana 2 is Google's newest image generation model, built on the Gemini 3.1 Flash Image architecture. It's the faster, more affordable sibling to Nano Banana Pro (which runs on the heavier Gemini 3 Pro backbone), and it lands at a sweet spot between speed and quality that most models on this list don't hit.

What makes it different from traditional diffusion models is how it generates images. Instead of treating your prompt as a bag of weighted tokens, it reasons about composition, lighting, and spatial relationships before rendering, the same way a multimodal language model processes text. The result is images that feel more intentional and less like a lucky roll of the dice.

Performance

Generated using Nano Banana 2 on fal, an AI model from Google DeepMind.

• Output quality: Vibrant, high-fidelity output with rich color and punchy contrast straight out of the box. The Flash architecture trades some of the compositional depth you'd get from Nano Banana Pro's full reasoning pipeline for noticeably faster generation.
• Prompt adherence: Strong semantic understanding inherited from the Gemini foundation.
• Speed and cost: From $0.06 per image on 512x512 resolution, $0.08 per image at 1K resolution, with 2K at 1.5x ($0.12) and 4K at 2x ($0.16).
• Control and editing capabilities: Supports up to 14 reference images for editing workflows, maintains character consistency for up to 5 people across generations without fine-tuning (great for storyboarding and campaign work).
• Supported resolutions: 512x512 (0.75x rate), 1K (standard), 2K (1.5x rate), and 4K (2x rate), with full aspect ratio support including 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, and 9:16.

How to Run Nano Banana 2 on fal

You can run Nano Banana 2 through fal's API or test it in the playground.

Same integration pattern as every other model on fal. If you've already integrated FLUX or Seedream, switching to Nano Banana 2 is a one-line endpoint change. All outputs include SynthID digital watermarking, and commercial use is enabled through fal.

Optional web search grounding is available through the enable_web_search parameter, which adds $0.015 per generation for factually current visuals.

Pricing

Here's how much it costs to run Nano Banana 2 on fal:

• $0.06 per image at 512x512 resolution (0.75x rate).
• $0.08 per image at 1K resolution (standard).
• $0.12 per image at 2K resolution (1.5x rate).
• $0.16 per image at 4K resolution (2x rate).
• Web search grounding adds $0.015 per generation if enabled.

Pros & Cons

Pros:

• Reasoning-guided generation that understands creative intent, not just keywords.
• Character consistency for up to 5 people across generations without fine-tuning.
• Flash-tier speed with vibrant, high-contrast output that rarely needs post-processing.

Cons:

• Nano Banana Pro remains the pick for highly complex compositional prompts where full reasoning depth matters most.
• At $0.08/image for 1K resolution, it's more expensive than FLUX.2 [pro] ($0.03/MP) and Seedream ($0.04/image) for teams that don't need the semantic reasoning advantages.

FLUX.2 [pro]

Best for: Production teams that need consistent, studio-grade output without tweaking inference parameters.

Similar to: FLUX 1.1 [pro] Ultra, Seedream V4.5.

FLUX.2 [pro] is Black Forest Labs' latest flagship, launched on fal on day one. It removes the guesswork from image generation: no inference steps to configure, no guidance scales to adjust, just a zero-configuration pipeline that delivers predictable quality whether you're generating one image or ten thousand.

Performance

Generated using FLUX.2 [pro] on fal.

• Output quality: Studio-grade photorealism with strong detail in skin textures, lighting, reflections, and fabric folds, consistently close to professional photography output in my testing.
• Prompt adherence: Top marks in my testing. I gave it the prompt with 5-6 specific elements (subject, pose, background, lighting, object placement), and it followed them more consistently than any other model I tested.
• Speed and cost: $0.03 per megapixel on fal, which works out to $0.03 for a standard 1024x1024 image, competitive with most models on this list and cheaper than DALL-E 3's $0.04-$0.08 per image through OpenAI's API.
• Control and editing capabilities: Supports text-to-image and image-to-image editing with natural language instructions (no masks or layers needed), multi-reference editing with up to 9 source images in one generation, and LoRA fine-tuning through fal's training pipeline for custom brand styles and visual identities.
• Supported resolutions: Flexible resolution options, including Custom, Square HD, Square, Portrait 3:4, Portrait 9:16, Landscape 4:3, and Landscape 16:9.

How to Run FLUX.2 [pro] on fal

You can run FLUX.2 [pro] through fal's API or test it in the playground without writing any code. fal handles the GPU infrastructure, so you don't need to manage servers or worry about scaling. Cold starts clock in at 5-10 seconds on fal versus 20-60 seconds on other alternatives on the market. The API docs and integration guide are available at fal.ai.

Pricing

Here's how much it costs to run FLUX.2 [pro] on fal:

• $0.03 for the first megapixel of output, plus $0.015 per extra megapixel.
• A standard 1024x1024 image costs $0.03.
• A 1920x1080 image costs roughly $0.045.

Pros & Cons

Pros:

• Professional-grade outputs without tuning steps or guidance parameters.
• Multi-reference editing with up to 9 source images in one generation.
• Strong prompt adherence on complex, multi-element prompts.
• Predictable results across batch generations.

Cons:

• Newer model, so community and third-party resources are still growing compared to FLUX.1 or Stable Diffusion ecosystems.
• Quality-first approach means it's not the fastest option if raw speed is your top priority.

Seedream V4.5

Best for: Teams that need photorealistic output with strong prompt adherence and built-in editing in a single model.

Similar to: FLUX.2 [pro], Nano Banana Pro.

Seedream V4.5 is ByteDance's latest image generation model, and it integrates both generation and editing into a single, unified architecture. What sets Seedream apart from most models on this list is how well it handles complex prompts while keeping costs low.

Performance

Generated using Seedream V4.5 on fal.

• Output quality: Photorealistic output that competes directly with FLUX.2 [pro] in detail and coherence, with natural skin textures, well-handled lighting, and few obvious AI artifacts.
• Prompt adherence: Strong. I tested it with multi-element prompts, and it consistently followed 4-5 out of 6 specified elements on the first try, placing it in the top tier of models I reviewed.
• Speed and cost: $0.04 per image on fal at standard resolution, which is $0.01 more than FLUX.2 [pro]'s starting price point. It took 30.51 seconds to run since I tried it with its Auto 4K image size.
• Control and editing capabilities: The unified architecture lets you generate an image and refine it within the same model without switching tools, supporting image editing with multi-image inputs.
• Supported resolutions: Multiple resolution options available, including Custom, Square HD, Square, Portrait 3:4, Portrait 9:16, Landscape 4:3, Landscape 16:9, Auto 2K, and Auto 4K.

How to Run Seedream V4.5 on fal

You can run Seedream V4.5 through fal's API or test it in the playground at fal.

fal handles all GPU infrastructure and scaling. Just send a request, get an image back, pay per use. The API uses the same integration pattern as every other model on fal, so if you've used one, you've used them all.

Pricing

Here's how much it costs to run Seedream V4.5 on fal:

• $0.04 per image on fal at standard resolution.

Pros & Cons

Pros:

• Strong photorealism with consistent quality across generations.
• Unified generation and editing in one model.
• Competitive pricing at $0.04/image on fal.

Cons:

• Higher image sizes may take longer to generate: as much as 30+ seconds, which can also be on the heavier side (e.g., 12 MB).
• FLUX.2 [pro] remains the stronger pick for multi-reference compositing workflows.

Recraft V3

Best for: Designers and marketers who need accurate text rendering, vector art, and brand-consistent imagery.

Similar to: Ideogram V3, FLUX.1 Kontext [pro].

Recraft V3 achieved the #1 position on Artificial Analysis's Text-to-Image Arena benchmark in October 2024 with an ELO of 1172. It's available on fal with the same API, pricing model, and fast inference as the rest of the catalog.

What stands out about it is that you can set the style of the image, such as realistic, digital illustration, and vector illustration.

Performance

Generated using Recraft V3 on fal.

• Output quality: Excellent for design-oriented output like posters, logos, illustrations, and brand assets.
• Prompt adherence: Very good, especially for structured design briefs with specific typography, color, and composition requirements.
• Speed and cost: $0.04 per image on fal for raster styles, $0.08 for vector, which is a slight premium over FLUX.2 [pro] and Seedream, but justified by the specialized typography and vector capabilities.
• Control and editing capabilities: Supports style presets (e.g., realistic_image, digital_illustration, vector_illustration), brand color palette control through a colors parameter, and size options covering all common social media and print formats.
• Supported resolutions: Square, square HD, portrait 4:3, portrait 16:9, landscape 4:3, and landscape 16:9.

How to Run Recraft V3 on fal

You can run Recraft V3 through fal's API or test it in the playground at fal.

The same API key and integration pattern work across all 1,000+ models on fal. No separate account or billing relationship needed.

Pricing

Here's how much it'd cost to run Recraft V3 on fal:

• $0.04 per image for raster styles on fal, which means roughly 25 raster generations per $1.00.
• $0.08 per image for vector styles on fal.

Pros & Cons

Pros:

• Best-in-class text rendering for typography-heavy use cases.
• Vector art generation, a rare capability among AI models.
• Brand color palette control for consistent brand assets.

Cons:

• Built for design and typography rather than the photorealism that FLUX.2 [pro] and Seedream specialize in.
• Vector generation costs 2x the standard rate.

Nano Banana Pro (Gemini 3 Pro Image)

Best for: Creative teams that need semantic accuracy, character consistency, and advanced text rendering, with a budget for premium output.

Similar to: GPT Image 1.5, DALL-E 3.

Nano Banana Pro is Google's image generation model built on the Gemini 3 Pro architecture. Instead of matching keywords to visual patterns like traditional diffusion models, it uses multimodal reasoning to interpret your creative intent holistically, which makes it a fundamentally different tool from everything else on this list.

Performance

Generated using Nano Banana Pro on fal.

• Output quality: Premium-tier output quality, with strong photorealism, accurate compositions, and a level of contextual understanding (like interpreting '1960s aesthetic' as grain, color palette, and composition changes, not just a filter) that sets it apart from other models on this list.
• Prompt adherence: The semantic understanding here stood out in my testing, with the Gemini Pro backbone giving it a clear edge on prompts that require reasoning about relationships between concepts rather than treating them as weighted tokens.
• Speed and cost: $0.15 per image on fal at standard resolution, making it 5x the starting cost of FLUX.2 [pro] and the most expensive model on this list, with generation speed not publicly benchmarked by Google.
• Control and editing capabilities: Supports multi-image blending with up to 14 reference images, maintains character consistency for up to 5 people across generations without fine-tuning, and includes batch processing for up to 4 variations per request.
• Supported resolutions: 1K, 2K, and 4K options through the API, with 4K charged at 2x the standard rate, and full aspect ratio support including 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, and 9:16.

How to Run Nano Banana Pro on fal

You can run Nano Banana Pro through fal's API or test it in the playground at fal. Our platform handles the infrastructure and billing. All outputs include SynthID digital watermarking. Commercial use is enabled through fal.

Pricing

Here's how much it'd cost you to run Nano Banana Pro on fal:

• $0.15 per image at standard resolution on fal.
• 4K outputs charged at 2x ($0.30/image).

Pros & Cons

Pros:

• Really good semantic understanding.
• Character consistency for up to 5 people without fine-tuning.
• Industry-leading text rendering in multiple languages.

Cons:

• Most expensive option at $0.15/image.
• The full reasoning pipeline prioritizes output quality over generation speed.

Ideogram V3

Best for: Anyone creating marketing materials, posters, logos, or social media graphics where text accuracy is critical.

Similar to: Recraft V3, GPT Image 1.5.

Ideogram built its reputation on getting text right in images. V3 takes that further with improved realism and better overall image quality, while keeping the near-perfect typography accuracy that made earlier versions popular.

Performance

Generated using Ideogram V3 on fal.

• Output quality: Solid overall quality with a big jump from V2, including better lighting, more natural compositions, and less of the "AI look".
• Prompt adherence: Excellent for typography-focused prompts, with near-perfect spelling accuracy on multi-word phrases, brand slogans, and product labels in my testing.
• Speed and cost: $0.03-$0.09 per image on fal, depending on quality tier, with the base tier being competitive and the premium tier costing 3x the base price.
• Control and editing capabilities: Focuses primarily on text-to-image generation with strong typography control, with multi-reference editing, LoRA training, and compositing being areas where FLUX.2 [pro] has a deeper feature set.
• Supported resolutions: Standard resolution options with multiple aspect ratios available, also accessible through Ideogram's own platform alongside fal.

How to Run Ideogram V3 on fal

Ideogram V3 is available through fal's API and playground at fal.

You can also access it through Ideogram's own platform, starting at $20/month (Plus plan), but the fal route gives you pay-per-use pricing with no subscription.

Pricing

Here's how much it'd cost to run Ideogram V3 on fal: $0.03-$0.09 per image, depending on quality tier:

• TURBO: $0.03.
• BALANCED: $0.06.
• QUALITY: $0.09.

Pros & Cons

Pros:

• Near-perfect text rendering in generated images.
• Big quality improvement over V2 across the board.
• Affordable base pricing at $0.03/image.

Cons:

• Photorealism takes a back seat to typography, which is where Ideogram puts its focus.
• Premium quality tier is 3x the base cost.

GPT Image 1.5

Best for: Versatile image generation with strong prompt following and multi-quality tiers for different budgets.

Similar to: Nano Banana Pro, DALL-E 3.

GPT Image 1.5 is OpenAI's latest flagship image model. The three-quality-tier system (low, medium, high) gives you real control over the cost-quality tradeoff per generation, which is something most other models on this list don't offer.

Performance

Generated using GPT Image 1.5 on fal.

• Output quality: Ranges from surprisingly usable at the low tier (good enough for drafts and social media) to competing with top models at the high tier for detail and coherence.
• Prompt adherence: Solid, benefiting from OpenAI's language understanding foundation, so conversational prompts work better here than with models that require more structured prompt engineering.
• Speed and cost: From $0.009/image (low quality, 1024x1024) to $0.034/image (medium quality) on fal, making the low tier one of the cheapest options on this list for high-volume draft work.
• Control and editing capabilities: Supports text-to-image and image-to-image editing, with LoRA training, vector generation, and multi-reference compositing being areas where FLUX, Recraft, and FLUX.2 [pro] each go deeper.
• Supported resolutions: 1024x1024, 1024x1536, and 1536x1024 with pricing that varies by resolution and quality tier.

How to Run GPT Image 1.5 on fal

GPT Image 1.5 is available through fal's API and playground at fal.

Pricing on fal is token-based, matching OpenAI's pricing structure but accessed through fal's unified API. You don't need a separate OpenAI account or API key when running it through fal.

Pricing

Here's how billing is calculated when you run GPT Image 1.5 on fal:

• Low quality: $0.009 for 1024x1024 or $0.013 for any other size per image.
• Medium quality: $0.034 for 1024x1024, $0.051 for 1024x1536 and $0.050 for 1536x1024 per image.
• High quality: $0.133 for 1024x1024, $0.200 for 1024x1536 or $0.199 for 1536x1024 per image.

Pros & Cons

Pros:

• Three quality tiers let you control cost per image precisely.
• Strong natural language prompt understanding.
• Low-tier pricing is excellent for high-volume draft work.

Cons:

• No LoRA fine-tuning or custom training options.
• Focused on versatile generation rather than specialized editing, vector, or multi-reference workflows.

FLUX 1.1 [pro] Ultra

Best for: Teams that need high-resolution output up to 2K without running a separate upscaling step.

Similar to: FLUX.2 [pro], FLUX 1.1 [pro].

FLUX 1.1 [pro] Ultra is the high-resolution variant of Black Forest Labs' FLUX 1.1 [pro]. It produces output up to 2K resolution natively, which means you skip the separate upscaling model that most other generators on this list require for larger formats.

Performance

Generated using FLUX 1.1 [pro] Ultra on fal.

• Output quality: Strong photorealism with rich colors and details that hold up at larger sizes, giving output a polished, production-ready feel without post-processing.
• Prompt adherence: Good for straightforward and moderately complex prompts, with FLUX.2 [pro] being the stronger choice for highly complex, multi-element prompts.
• Speed and cost: $0.06 per image on fal, a slight premium over FLUX.2 [pro]'s $0.03/megapixel, but the native high-res output can save you the cost of a separate upscaling step.
• Control and editing capabilities: Part of fal's FLUX ecosystem with LoRA support, with the zero-configuration pipeline and multi-reference editing being features that FLUX.2 [pro] introduced later in the lineup.
• Supported resolutions: Up to 2K resolution natively with improved photorealism at larger sizes, the highest native resolution among FLUX models without upscaling.

How to Run FLUX 1.1 [pro] Ultra on fal

Available through fal's API and playground at fal.

Same integration pattern as all FLUX models on fal. Swap the model endpoint string, and you're generating at higher resolutions immediately, no code changes beyond that.

Pricing

It costs $0.06 per image to use FLUX 1.1 [pro] Ultra on fal.

Pros & Cons

Pros:

• Native 2K resolution without needing a separate upscaler.
• Strong photorealism with polished output at large sizes.
• Part of fal's FLUX ecosystem with LoRA support.

Cons:

• FLUX.2 [pro] has since taken the lead on editing versatility and zero-config quality.
• More expensive than many other models.

Qwen Image Max

Best for: Teams that need strong text rendering and precise image editing from an LLM-based architecture at a budget-friendly price point.

Similar to: GPT Image 1.5, Ideogram V3.

Qwen Image Max is an image generation foundation model from the Qwen series (Alibaba) that takes a fundamentally different approach from most models on this list. It's built on an autoregressive LLM architecture rather than a diffusion model, which gives it strong advantages in complex text rendering and precise image editing.

Performance

• Output quality: Good overall quality with particular strength in text-heavy compositions, with FLUX.2 [pro] and Seedream V4.5 being the stronger picks for raw photorealism in portrait and product photography.
• Prompt adherence: Strong, especially for prompts that involve text elements, labels, or precise spatial descriptions, benefiting from the LLM architecture's natural language understanding.
• Speed and cost: $0.075 per image, making it more expensive than FLUX.2 [pro] ($0.03/MP) and Recraft V3 ($0.04/image).
• Control and editing capabilities: Supports LoRA fine-tuning for custom styles and subjects, includes a turbo mode for faster generation, and handles image editing tasks with the same text-instruction approach that makes LLM-based models feel intuitive to use.
• Supported resolutions: Includes Custom, Square HD, Square, Portrait 3:4, Portrait 9:16, Landscape 4:3, and Landscape 16:9.

How to Run Qwen Image Max on fal

Available through fal's API and playground at fal. Same integration pattern as every other model on fal.

LoRA training is supported, so you can fine-tune it for specific visual styles or brand identities through fal's training pipeline. The queue system handles long-running requests with webhook support for async workflows.

Pricing

To run Qwen Image Max on fal, you'd need to spend $0.075 per image.

Pros & Cons

Pros:

• LLM-based architecture delivers strong text rendering and natural language editing.
• Webp output format option.
• LoRA fine-tuning support for custom training.

Cons:

• Photorealism is an area where diffusion-based models like FLUX.2 [pro] and Seedream still have the edge.
• Smaller community and fewer third-party resources compared to FLUX or Stable Diffusion ecosystems.

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editing

new

kling-video/v3/pro/motion-control

video-to-video

Transfer movements from a reference video to any character image. Cost-effective mode for motion transfer, perfect for portraits and simple animations.

stylized

transform

editing

new

ltx-2/extend-video

video-to-video

Extends videos with audio using LTX-2

new

ltx-2/audio-to-video

audio-to-video

Generate video from audio using LTX-2

stylized

transform

lipsync

new

qwen-image-2/pro/text-to-image

text-to-image

Qwen-Image-2.0 is a next-generation foundational unified generation-and-editing model

realism

typography

new

qwen-image-2/text-to-image

text-to-image

Qwen-Image-2.0 is a next-generation foundational unified generation-and-editing model

realism

typography

new

qwen-image-2/pro/edit

image-to-image

Qwen-Image-2.0 is a next-generation foundational unified generation-and-editing model

stylized

transform

new

qwen-image-2/edit

image-to-image

Qwen-Image-2.0 is a next-generation foundational unified generation-and-editing model

stylized

transform

new

depth-anything-video

video-to-video

Generates depth maps from video using Video Depth Anything (CVPR 2025). Produces per-frame depth estimation with temporal consistency across frames. Supports 3 model sizes (Small, Base, Large), 5 colormaps including grayscale, side-by-side comparison with the original video, and raw depth export as .npz. Useful for 3D reconstruction, video effects, compositing, and scene understanding.

video to video

motion

edit

new

bria/fibo-bbq-preview/generate

text-to-image

A preview to the next level of control of Text-to-Image models.

new

trellis-2/retexture

image-to-3d

Generate 3D models from your images using Trellis 2. A native 3D generative model enabling versatile and high-quality 3D asset creation.

image-to-3d

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