Nano Banana Pro 4K Guide: Resolution, Settings & Watermark-Free Output (2026)

Nano Banana Pro generates native 4K images at 4096x4096 pixels (16 megapixels) through Google's Gemini 3 Pro Image model (model ID: gemini-3-pro-image-preview). Official API pricing stands at $0.24 per 4K image as of February 2026, with the Batch API cutting that cost to $0.12. Third-party providers offer all resolutions at approximately $0.05 per image. Free Gemini app users are limited to roughly 1K resolution (1024x1024) with SynthID digital watermarks embedded in the output—API access or third-party platforms deliver watermark-free 4K output with full resolution control.

TL;DR

• Native 4K: Nano Banana Pro supports up to 4096x4096 (16MP) natively—no upscaling required
• Three resolution tiers: 1K (1024px, free tier), 2K (2048px, $0.134), 4K (4096px, $0.24)
• Cheapest 4K path: Third-party API providers at ~$0.05/image (79% savings vs official)
• Watermark-free: API and third-party access bypasses SynthID entirely
• Best settings: CFG 5-7, denoise 0.35-0.45, sRGB color space, JPEG quality 90-92
• Batch processing: Official Batch API offers 50% discount for non-urgent workloads

What Is Nano Banana Pro and Why Does 4K Matter?

Nano Banana Pro is the community nickname for Google DeepMind's Gemini 3 Pro Image model, one of the most capable AI image generators available as of early 2026. The model launched under its official identifier gemini-3-pro-image-preview and earned its playful "banana" moniker from Google's own naming convention, which includes a banana emoji on the official pricing page at ai.google.dev. Unlike many AI image generators that top out at 1024x1024 or require external upscaling pipelines, Nano Banana Pro natively generates images at resolutions up to 4096x4096 pixels—a genuine 16-megapixel output that represents a significant leap for the field.

The distinction between native 4K generation and upscaled 4K matters enormously for anyone producing professional-quality imagery. When an AI model generates at 4K natively, every pixel in the output comes from the model's understanding of the prompt, resulting in coherent fine details, accurate textures, and consistent lighting across the entire image. Upscaling, by contrast, takes a lower-resolution generation and interpolates additional pixels using algorithms that can introduce artifacts, soften details, and create visual inconsistencies that trained eyes notice immediately. For applications like print design (where 300 DPI at large formats demands genuine pixel density), product photography, architectural visualization, and commercial artwork, native 4K generation eliminates the quality ceiling that has limited AI-generated imagery in professional workflows.

Understanding Nano Banana Pro's 4K capabilities matters because this model represents the current state of the art for resolution in consumer-accessible AI image generation. If you want to see how Nano Banana Pro compares to Flux for image generation, the resolution advantage is one of the most significant differentiators. The model supports five aspect ratios (1:1, 16:9, 9:16, 21:9, and 4:5), accepts up to eight reference images for style guidance, and generates output in under ten seconds for standard resolutions. These specifications make it relevant for everyone from social media creators needing quick 1K thumbnails to design agencies requiring print-ready 4K assets.

The timing of Nano Banana Pro's 4K capability is significant in a broader context. As of early 2026, the competitive landscape includes Flux (which tops out at approximately 2K without external upscaling), DALL-E 3 (limited to 1024x1024 natively), and Midjourney (which offers up to 2048 pixels on its longest side). Nano Banana Pro's native 4K generation represents a genuine step function in resolution availability, and Google's decision to make this capability accessible through a standard API at transparent per-image pricing—rather than gating it behind enterprise agreements—democratizes high-resolution AI image generation in a way that previous models have not. Whether you are evaluating AI image generators for a new project or upgrading from a model with lower resolution limits, understanding Nano Banana Pro's 4K workflow is directly relevant to making an informed decision.

Resolution Tiers Decoded: 1K vs 2K vs 4K

Nano Banana Pro organizes its output into three distinct resolution tiers, and understanding the differences between them is essential for choosing the right balance of quality, speed, and cost for your specific use case. The tiers are not simply "small, medium, large"—each one maps to meaningfully different technical capabilities and practical applications. According to the official Google AI pricing page (last updated February 19, 2026), the model produces images consuming different numbers of output tokens depending on resolution, which directly determines the cost per image.

The first tier generates images at approximately 1024x1024 pixels (roughly 1 megapixel), consuming around 560 output tokens per image. At the standard output rate of $120 per million tokens, this works out to approximately $0.067 per image. This resolution is perfectly adequate for social media posts, web thumbnails, blog illustrations, and any context where images display at 800 pixels wide or smaller. Free Gemini app users are limited to roughly this resolution tier, making it the entry point for casual users exploring AI image generation. For a detailed breakdown of free versus Pro tier capabilities, the resolution limitation is one of the most impactful differences between the free and paid experience.

The second tier targets approximately 2048x2048 pixels (4 megapixels), consuming 1,120 output tokens per image at a cost of $0.134. This resolution hits a practical sweet spot for many professional use cases: it provides enough pixel density for mid-size print outputs (roughly 7 inches at 300 DPI), high-quality website hero images, presentation graphics, and social media content where platform algorithms favor higher-resolution uploads. The jump from 1K to 2K quadruples the pixel count while only doubling the cost, making it the most cost-efficient resolution for users who need more than basic web quality.

The third tier reaches up to 4096x4096 pixels (16 megapixels), consuming 2,000 output tokens at $0.24 per image. This native 4K output is designed for scenarios demanding genuine high resolution: large-format print (13+ inches at 300 DPI), commercial photography replacement, detailed architectural renders, and any workflow where downstream cropping or zooming requires maximum pixel density. The price jump from 2K to 4K is roughly 79% more expensive, reflecting both the increased computational demand and the significantly larger output. An often-overlooked benefit of 4K generation is the cropping headroom it provides—you can generate a 4K landscape and crop to a perfectly framed portrait composition while retaining more than enough resolution for web or print use, something impossible with 1K or 2K originals.

How to Generate 4K Images Step by Step

Generating 4K images with Nano Banana Pro requires choosing the right platform, because not every access method supports the full resolution range. The path you select determines not only your maximum resolution but also your level of control over settings, pricing, watermark status, and workflow integration. There are four primary platforms for accessing Nano Banana Pro's image generation capabilities, each suited to different user profiles and requirements.

The Gemini App (gemini.google.com) provides the simplest access path but offers the least control over resolution and settings. Free users generate images at roughly 1K resolution with SynthID watermarks applied automatically. Google AI Plus subscribers ($19.99/month as of February 2026) gain access to higher-quality generations, though the exact resolution ceiling in the app is not directly configurable—the app selects resolution based on the prompt and internal heuristics. If you simply need quick images and do not require precise resolution control or watermark-free output, the Gemini app is the most accessible starting point. However, for guaranteed 4K output, you need API access.

Google AI Studio (aistudio.google.com) serves as the intermediate option between the consumer app and raw API calls. It provides a web interface for testing prompts with the gemini-3-pro-image-preview model, lets you configure parameters like temperature and output format, and displays token usage so you can estimate costs before committing to production workflows. To generate 4K images in AI Studio, you can include resolution parameters in your request configuration. For our complete guide to getting your Nano Banana Pro API key, you will need a Google Cloud project or AI Studio account as the first step.

Direct API access through the Gemini API gives you full control over every parameter. Here is the essential API call structure for generating a 4K image:

python
import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-3-pro-image-preview")

response = model.generate_content(
    "A professional product photograph of a ceramic vase on a marble surface, "
    "studio lighting, high detail, 4K resolution",
    generation_config={
        "response_modalities": ["image", "text"],
        "image_generation_config": {
            "number_of_images": 1,
        }
    }
)


for part in response.candidates[0].content.parts:
    if hasattr(part, "inline_data"):
        with open("output_4k.png", "wb") as f:
            f.write(part.inline_data.data)

The API approach costs $0.24 per 4K image at standard rates, or $0.12 with the Batch API (a 50% discount for jobs that can tolerate up to 24-hour processing windows). For high-volume production workflows, the Batch API's savings add up quickly—generating 1,000 4K images costs $240 at standard rates versus $120 through batching.

Third-party API providers like laozhang.ai offer access to Nano Banana Pro through unified API endpoints, typically at significantly lower per-image costs (around $0.05 per image regardless of resolution). These providers handle the Google Cloud infrastructure, billing management, and API key rotation on your behalf, making them particularly attractive for developers who want to integrate Nano Banana Pro into applications without managing Google Cloud accounts directly. The trade-off is trusting a third party with your API traffic, though established providers with documented uptime records mitigate this concern. An additional advantage of third-party providers is that they typically offer OpenAI-compatible API endpoints, meaning you can switch between image generation models (Nano Banana Pro, Flux, DALL-E) by changing a single model parameter rather than rewriting your integration code—a valuable property for teams evaluating multiple models or building model-agnostic applications.

Native 4K vs Upscaling: A Practical Decision Guide

The question of whether to generate at native 4K or generate at a lower resolution and upscale is one of the most common decisions Nano Banana Pro users face, and the answer depends on your specific output requirements, budget constraints, and quality expectations. Both approaches have legitimate use cases, and understanding the technical differences helps you make an informed choice rather than defaulting to the most expensive option.

Native 4K generation through Nano Banana Pro produces images where every pixel originates from the model's diffusion process. This means fine details—hair strands, fabric textures, text in images, architectural elements, skin pores—are all generated with the model's full understanding of the prompt and spatial relationships. The result is coherent detail at every zoom level, with no interpolation artifacts or hallucinated textures. The cost is $0.24 per image through the standard API (or $0.12 via Batch API), and generation time extends to approximately 60-65 seconds for 4K output compared to under 10 seconds for standard resolution.

Upscaling takes a 1K or 2K generation and enlarges it using either Nano Banana Pro's own in-app upscale feature or external tools like Real-ESRGAN, Topaz Gigapixel, or Magnific AI. The advantage is cost: generating at 2K ($0.134) and upscaling to 4K costs significantly less than native 4K generation, especially if you use free upscaling tools. Testing by WaveSpeedAI (January 2026) found that in-app 2x upscaling produces acceptable results for most use cases, though they noted that 4x upscaling tends to produce output that looks "punchy but fake"—oversaturated details and artificially enhanced textures that reveal the upscaling process upon close inspection.

The practical decision comes down to three factors. First, consider your output destination: if images will display at web resolution (under 2000 pixels wide) or be viewed on mobile screens, the difference between native 4K and upscaled 4K is negligible—save the money and upscale from 2K. Second, consider the image content: photographs of people, architectural details with straight lines, and images containing readable text benefit most from native 4K because these elements show upscaling artifacts most visibly. Abstract art, landscapes, and painterly styles are more forgiving of upscaling. Third, consider volume: if you are generating hundreds of images, the cost difference between $0.24 and $0.134 per image compounds significantly, making the upscale path economically compelling even with a small quality trade-off.

A hybrid strategy that many production teams adopt is generating initial concept explorations at 1K resolution (fast and cheap at ~$0.067 per image), then regenerating approved concepts at native 4K for the final output. This approach lets you iterate rapidly on composition, style, and prompt refinement without incurring 4K costs on images you will ultimately discard, while still delivering maximum quality for the final deliverable. Combined with the Batch API for the 4K regeneration step, this workflow can reduce overall costs by 60-70% compared to generating everything at native 4K from the start.

Complete 4K Pricing Breakdown and Cost Optimization

Understanding the full pricing landscape for Nano Banana Pro 4K generation is critical for anyone planning to use this model at scale, because the cost difference between the most expensive and cheapest legitimate access paths represents a 79% savings opportunity. All pricing data in this section comes from the official Google AI pricing page at ai.google.dev/pricing (last updated February 19, 2026) and verified third-party provider documentation.

The official pricing structure works through a token-based system. Nano Banana Pro (Gemini 3 Pro Image) charges $120 per million output tokens for image generation. A single 4K image consumes approximately 2,000 output tokens, which calculates to $0.24 per image. For 2K images, consumption drops to 1,120 tokens at $0.134 per image, and 1K images consume roughly 560 tokens at approximately $0.067 per image. Image input (when using reference images) costs $0.0011 per image at 560 tokens each with an input rate of $2 per million tokens—essentially negligible even when providing the maximum eight reference images.

Google's Batch API represents the first significant optimization opportunity. Available for workloads that do not require real-time generation, the Batch API applies a flat 50% discount on output token costs. This reduces the 4K image price from $0.24 to $0.12, the 2K price from $0.134 to $0.067, and the 1K price to approximately $0.034. Batch jobs may take up to 24 hours to complete, though in practice most finish within a few hours. For any workflow where images are not needed in real time—batch content generation, dataset creation, marketing asset preparation—the Batch API should be the default choice.

Third-party API providers offer the most aggressive pricing. Platforms like laozhang.ai provide access to Nano Banana Pro at approximately $0.05 per image regardless of resolution tier, representing a 79% savings compared to official 4K pricing and a 63% savings compared to official 2K pricing. This flat-rate pricing model eliminates the resolution-based cost escalation entirely, which is particularly valuable for 4K generation where the official per-image cost is highest. These providers aggregate demand across many users, negotiate volume pricing, and pass a portion of the savings to individual customers.

For cost optimization at scale, consider a tiered approach: use the Batch API for large production runs where you control timing, third-party providers for development and testing where you need fast iteration at low cost, and the standard API only for real-time user-facing generation where latency matters. A team generating 500 4K images per month would spend $120 via official API, $60 via Batch API, or approximately $25 via third-party providers—the annual difference between the most and least expensive paths is $1,140 versus $300.

Watermark-Free 4K Output: Understanding SynthID and Your Options

One of the most frequently asked questions about Nano Banana Pro concerns watermarks—specifically whether generated images carry visible or invisible markings, and how to obtain clean output for commercial use. The answer requires understanding SynthID, Google's proprietary digital watermarking system, and how different access paths interact with it.

SynthID is an invisible digital watermark developed by Google DeepMind that embeds imperceptible signals into AI-generated images at the pixel level. Unlike traditional visible watermarks (semi-transparent logos or text overlays), SynthID does not alter the visual appearance of the image in any way detectable by the human eye. It works by making subtle modifications to the pixel values that are statistically detectable by specialized algorithms but invisible to viewers, even at extreme zoom levels or after common image processing operations like compression, cropping, and color adjustment. Google applies SynthID to images generated through consumer-facing interfaces—primarily the Gemini app—as part of their responsible AI deployment framework.

The critical distinction for users is this: SynthID is applied at the platform level, not at the model level. When you generate images through the Gemini app (both free and paid tiers), SynthID watermarks are embedded in the output. However, when you access Nano Banana Pro through the Gemini API directly, the model generates clean output without SynthID applied. Third-party platforms that access the model through the API also deliver watermark-free output, since SynthID is not part of the model's generation process itself. This means that for any user accessing Nano Banana Pro through API-based workflows, watermarks are simply not a concern.

For users who need watermark-free output but are currently using the Gemini app, the migration path involves obtaining an API key and switching to API-based generation. This is not a workaround or a hack—Google explicitly provides API access without SynthID as a feature for developers and businesses. The practical consideration is that API access requires a billing account and charges per image rather than offering a subscription-based unlimited model, but for users generating fewer than approximately 83 images per month at 4K resolution, the per-image API cost ($0.24 x 83 = ~$19.99) is comparable to the Google AI Plus subscription price anyway. At higher volumes, third-party providers at $0.05 per image make watermark-free 4K generation economically accessible to virtually any budget.

It is worth emphasizing what SynthID is not: it is not a usage restriction, a quality limitation, or a legal encumbrance. Images containing SynthID are fully usable for commercial purposes under Google's terms of service. The watermark exists purely as a provenance signal—a way for detection tools to identify AI-generated content. If you are using images in contexts where AI provenance detection might matter (certain stock photography platforms, journalistic contexts, regulated industries), the presence or absence of SynthID could be relevant. For most commercial and creative applications, it is a non-issue regardless of whether it is present.

Optimal Settings for Maximum 4K Quality

Generating a 4K image from Nano Banana Pro is straightforward, but generating a 4K image that maximizes the model's quality potential requires understanding which parameters matter most and how they interact. The settings recommendations in this section are drawn from published testing results (particularly WaveSpeedAI's January 2026 parameter analysis) and corroborated against community consensus from multiple practitioners working with the model.

The most impactful parameter for image quality is the CFG (Classifier-Free Guidance) scale, which controls how closely the generation adheres to your prompt versus allowing the model creative latitude. Conventional wisdom from earlier diffusion models suggested high CFG values (8-12) for maximum prompt adherence, but testing with Nano Banana Pro consistently shows that lower values of 5-7 produce superior results. At CFG 5-7, the model balances prompt fidelity with natural-looking output—colors appear more realistic, lighting transitions are smoother, and fine textures avoid the over-sharpened, artificial quality that higher CFG values introduce. Setting CFG above 8 with Nano Banana Pro tends to produce images that look "hyper-processed," with exaggerated contrast and oversaturated colors that reveal the image as AI-generated.

The denoise or variation strength parameter deserves equal attention, particularly when using image-to-image workflows or in-model upscaling. Testing indicates that a denoise range of 0.35-0.45 preserves the essential composition and content of the source while allowing the model to enhance details and add coherent fine structure. Values below 0.35 produce output too similar to the input (defeating the purpose of enhancement), while values above 0.45 begin introducing unwanted changes to composition, color palette, and subject identity. For 4K generation specifically, staying within this range ensures that the additional pixels contain meaningful detail rather than noise or hallucinated content.

Color space configuration matters more than most users realize, especially when generating images destined for specific output contexts. Nano Banana Pro generates in sRGB color space by default, which is the correct choice for any digital display, web publishing, or standard printing workflow. Some users attempt to force Display P3 or Adobe RGB output, but this typically introduces color shifts rather than expanding the gamut in any useful way—the model's training data is overwhelmingly sRGB, and attempting to force wider color spaces produces unpredictable results. For the vast majority of users, leaving color space at sRGB and handling any necessary conversions in post-processing is the optimal approach.

Export format selection is the final quality lever. For photographic content, JPEG at quality 90-92 provides an excellent balance between file size and visual fidelity—at these settings, compression artifacts are invisible even at 100% zoom on a 4K display. Going above 92 increases file size substantially with diminishing perceptual returns. For graphics, illustrations, or any image containing transparency or hard edges (logos, UI mockups, text overlays), PNG is the correct choice despite the larger file sizes, as JPEG compression introduces visible ringing artifacts around sharp edges. For web-optimized delivery, WebP at quality 85-90 provides better compression than JPEG at equivalent visual quality and supports transparency like PNG.

One frequently overlooked aspect of 4K image quality is prompt engineering itself. At 4K resolution, the model has sixteen times more pixels to fill compared to 1K, which means prompt specificity matters more—vague prompts produce more visible inconsistencies at higher resolutions because there is simply more visual space for the model to fill with uncertain content. Effective 4K prompts include specific material descriptions ("brushed stainless steel with fingerprint smudges"), precise lighting conditions ("warm golden hour sidelight from the upper left"), and compositional direction ("centered subject with shallow depth of field, background bokeh"). Including a single clear focal point rather than multiple competing subjects also improves 4K consistency dramatically, as the model can allocate its full resolution budget to rendering one subject with maximum coherence rather than splitting attention across competing elements.

Troubleshooting Common 4K Generation Issues

Even with optimal settings, 4K image generation with Nano Banana Pro occasionally encounters issues that can frustrate users who do not understand their root causes. The most common problems fall into a few well-understood categories, and most have straightforward solutions that do not require changing your fundamental approach.

The most frequent issue users report is receiving lower-resolution output than expected when requesting 4K images. This typically happens because the resolution parameter was not correctly specified in the API call, or because the user is generating through the Gemini app where resolution selection is handled by the platform rather than the user. The fix is to verify your API configuration explicitly includes 4K resolution parameters and to check the output image dimensions with a simple file inspection tool. If the API returns a 2K image when you requested 4K, the cause is usually a malformed request—double-check your image_generation_config parameters and ensure your account has sufficient quota allocation for 4K generation.

Generation timeouts represent the second most common issue, particularly for 4K output. Standard resolution images generate in under 10 seconds, but 4K generation can take 60-65 seconds or more depending on server load. If your API client has a default timeout of 30 seconds (common in many HTTP libraries), the request will fail before the image completes generating. The solution is straightforward: increase your client timeout to at least 120 seconds for 4K requests. For production applications, implementing an asynchronous generation pattern with polling or webhooks is more robust than extending synchronous timeouts. For a comprehensive error code reference, consult the dedicated troubleshooting guide that covers all common API error responses.

Quality inconsistency across generations can be disconcerting, where the same prompt produces excellent results one attempt and mediocre results the next. This is inherent to the stochastic nature of diffusion models and is not a bug—it is a fundamental property of how these models work. The practical solution is to generate multiple variants (3-5) for any important image and select the best result, which is standard practice in professional AI image workflows. At $0.24 per 4K image (or $0.05 via third-party providers), generating five variants for selection costs $1.20 or $0.25 respectively—a trivial expense for commercial quality assurance.

Unexpected artifacts in 4K output—particularly along image edges, in areas of high detail density, or in faces at certain angles—usually indicate either an overly complex prompt or CFG values that are too high. Simplifying the prompt to focus on one primary subject with clear compositional guidance, and keeping CFG in the 5-7 range, resolves most artifact issues. If specific content types consistently produce artifacts (a known challenge with hands and text), using image-to-image workflows with a clean reference image can guide the model past its weaker generation modes.

Rate limiting and quota exhaustion can interrupt 4K generation workflows, particularly for users on free-tier API plans or during peak usage periods. Google applies rate limits to the Gemini API that vary by account tier and region. If you encounter HTTP 429 (Too Many Requests) errors, implement exponential backoff in your client code—start with a 1-second delay, doubling on each retry up to a maximum of 64 seconds. For sustained high-volume generation, request a quota increase through the Google Cloud Console or consider distributing requests across multiple API keys. Third-party providers typically handle rate limiting on the backend, abstracting this complexity away from the end user, which is another practical advantage of the third-party approach for production workloads.

Putting It All Together: Your 4K Workflow

Bringing together everything covered in this guide, here is a practical decision framework for implementing Nano Banana Pro 4K generation in your workflow. Your optimal path depends on three factors: your volume requirements, your quality bar, and your budget constraints.

For low-volume users generating fewer than 50 images per month, the direct API through Google AI Studio is the simplest approach. Set up your API key, configure requests for your target resolution, use CFG 5-7 with sRGB color space, and generate 3-5 variants of each important image for quality selection. At 50 4K images per month, the standard API costs approximately $12—less than a single month of most creative software subscriptions. Generate at native 4K for print or high-quality web content, and at 2K with upscaling for everything else.

For medium-volume users (50-500 images per month), the Batch API becomes essential. Queue your generation jobs during off-peak hours, accept the batch processing latency, and pocket the 50% savings that bring your 4K cost down to $0.12 per image. At 500 images, that is $60 per month versus $120 on the standard API. Supplement with a third-party provider like laozhang.ai at $0.05 per image for development testing and rapid iteration, and reserve the official Batch API for final production runs where you want a direct relationship with Google's infrastructure.

For high-volume users (500+ images per month), third-party providers at $0.05 per image deliver the most compelling economics. At 1,000 4K images, the monthly cost is approximately $50 versus $240 on the official API—a savings of $2,280 annually. The key is establishing a reliable provider relationship and implementing quality verification in your pipeline to catch any generation anomalies.

Regardless of volume, remember these core principles: generate at native 4K only when the output genuinely requires it (print, commercial, detailed crops), use the 2K tier plus upscaling for web and social media content where the cost savings compound without visible quality loss, keep CFG at 5-7, export in the right format for your use case (JPEG 90-92 for photos, PNG for graphics), and always generate multiple variants when quality matters. Nano Banana Pro's 4K capability is genuinely impressive—the difference between average and excellent results comes down to understanding when and how to use it effectively.

Frequently Asked Questions

Is Nano Banana Pro 4K output truly native or upscaled? Nano Banana Pro generates native 4K images at up to 4096x4096 pixels (16 megapixels). The model produces these pixels directly through its diffusion process—they are not upscaled from a lower resolution. This is confirmed by the token consumption pattern: 4K images consume 2,000 output tokens versus 1,120 for 2K, indicating the model is doing proportionally more computational work at higher resolutions (source: ai.google.dev/pricing, February 2026).

Can free Gemini users generate 4K images? No. Free Gemini app users are limited to approximately 1K resolution (around 1024x1024 pixels). Accessing 2K and 4K generation requires either a Google AI Plus subscription ($19.99/month) with limited resolution control, or direct API/third-party access where you can specify exact resolution parameters. The API path provides the most reliable 4K generation.

How much does it cost to generate 100 4K images? Through the official API: $24.00 (100 x $0.24). Through the Batch API: $12.00 (100 x $0.12). Through third-party providers: approximately $5.00 (100 x $0.05). The most cost-effective approach for non-urgent workloads is the Batch API; for the lowest absolute cost, third-party providers offer the best rates.

Does SynthID watermark affect image quality? No. SynthID is an invisible digital watermark that makes imperceptible modifications to pixel values. It does not reduce resolution, add visible marks, or degrade image quality in any way detectable by the human eye. Its sole purpose is enabling algorithmic detection of AI-generated content for provenance tracking.

What is the best prompt strategy for high-quality 4K output? Focus on one primary subject with clear compositional guidance. Include specific lighting and material descriptions. Use CFG 5-7 (not higher). Generate 3-5 variants and select the best. Avoid overly complex multi-subject prompts at 4K, as the model handles single-subject compositions more consistently at maximum resolution.