Nano Banana Pro has quickly become the go-to AI image generator since its November 2025 launch, but a growing wave of users are reporting that something has changed. Forum threads on Google's Gemini community are filled with complaints: "the model has gotten dumber," "quality dropped overnight," and "my Pro subscription images look like the old free version." If you've noticed your Nano Banana Pro output declining in quality, this comprehensive guide breaks down exactly what's happening, why it's happening, and — most importantly — how to fix it with cause-specific solutions that actually work.
TL;DR
Nano Banana Pro quality decline is real and stems from 7 distinct causes, not a single Google downgrade. The most common culprits are silent model fallback after quota exhaustion (your Pro switches to standard Nano Banana without warning), accidentally using "Fast" mode instead of "Thinking" mode, and input image compression above the 2.5MB sweet spot. Most users can restore quality immediately by verifying they're using "Thinking" mode and optimizing input images. For server-side issues like the January 2026 4K timeout outage, the only solution is patience or switching to alternative API endpoints. This guide covers all 7 root causes with a diagnostic flowchart to identify your specific issue.
Yes, Your Nano Banana Pro Quality Really Did Drop — Here's the Evidence
The complaints about Nano Banana Pro quality decline aren't anecdotal exaggeration — they reflect a pattern documented across multiple platforms throughout late 2025 and early 2026. Understanding the scope of the problem is the first step toward fixing it, because the nature of your quality drop determines which solution will actually work for your situation.
On the Google Gemini Apps Community forum, threads about quality degradation have accumulated hundreds of responses since December 2025. One widely-discussed thread titled "Nano banana pro has gotten very bad in the past 2 days" captured a common experience: users who had been generating consistently high-quality images suddenly found their outputs looking muddy, lacking detail, or failing to follow prompts accurately. The frustration was compounded by the fact that these users were paying for Gemini Pro subscriptions expecting premium quality.
The Google AI Developers Forum painted an even more concerning picture for API users. A thread from December 12, 2025 reported that after generating approximately 20 images, the system silently switched to "some old model without any warning, producing terrible results." What made this particularly alarming was that the issue affected both consumer AI Pro subscribers and paid API users simultaneously — suggesting a systemic backend change rather than an isolated bug. Google's official response, which came 17 days later, recommended generic troubleshooting steps like starting fresh sessions and requesting new API keys, without acknowledging a known issue.
Independent analysis has also confirmed quality variation. Research evaluating Nano Banana Pro across 14 low-level vision tasks and 40 datasets found that while the model achieves superior subjective visual quality and natural-looking outputs, it significantly underperforms in traditional pixel-fidelity metrics compared to specialized models. This means Nano Banana Pro prioritizes "looking good" over strict accuracy — a design choice that can feel like quality decline when the model's creative interpretation diverges from user expectations. The distinction between perceptual quality and pixel-level accuracy is crucial for understanding what "quality decline" actually means in different contexts.
A Timeline of Every Nano Banana Pro Quality Shift Since Launch
Understanding when quality changes happened helps you determine whether your issue is new, ongoing, or related to a specific event. The history of Nano Banana Pro has been marked by several distinct quality-affecting milestones that have collectively shaped user experience in early 2026.
August 26, 2025 — Original Nano Banana Launch. Google released the original Nano Banana model powered by Gemini 2.5 Flash. This model generated 1024x1024 images quickly (approximately 3 seconds per image) and offered a generous free tier of 4-10 daily generations. Quality was considered good for an AI image generator, though text rendering was inconsistent and complex prompts often produced unexpected results. This period established the baseline that many users now compare against when discussing quality decline.
November 20, 2025 — Nano Banana Pro Launch and Quota Reduction. Google launched Nano Banana Pro, built on the more powerful Gemini 3 Pro engine. The new model brought 2K-4K resolution support, dramatically improved text rendering, and enhanced prompt understanding. However, the same month saw free tier quotas reduced from 3 to 2 images per day — a change 9to5Google reported was due to "high demand." This quota reduction inadvertently created one of the most common quality decline scenarios: when free users exhaust their 2 daily Pro generations, the system silently falls back to the standard Nano Banana model, producing noticeably lower quality output without any warning to the user.
December 2025 — API Downgrade Incidents. Multiple developers reported on the Google AI Developers Forum that Nano Banana Pro API responses suddenly started returning outputs matching older model quality. Both paid API and AI Pro subscription users were affected simultaneously. Google's delayed response (17 days for the first acknowledgment) and generic troubleshooting advice frustrated the developer community. For users who understand rate limits and quota management, this period highlighted the importance of monitoring output quality programmatically rather than assuming consistent API behavior.
January 21, 2026 — 4K Resolution Timeout Outage. A significant infrastructure incident caused 4K resolution requests to timeout consistently for at least 5.5 hours. Analysis pointed to insufficient Google TPU v7 capacity, compounded by Gemini 3.0 training sessions competing for inference resources. While the outage was resolved, it demonstrated that Nano Banana Pro's quality can be affected by infrastructure constraints beyond the model itself. Users seeking guidance on resolution optimization can reference our 4K guide for best practices during capacity-constrained periods.
February 2026 — Current State. The model itself hasn't been publicly downgraded, but the cumulative effect of quota reductions, silent fallback mechanisms, and periodic infrastructure issues has created a pervasive sense of declining quality. The actual quality when the correct model is properly engaged remains strong — the challenge lies in ensuring users consistently access that quality through correct settings and workflows.
The 7 Real Reasons Behind Nano Banana Pro Quality Decline
The quality decline you're experiencing likely isn't caused by Google secretly degrading the model. Instead, it stems from one or more of seven distinct causes that each require different solutions. Understanding which cause applies to your situation is essential because applying the wrong fix wastes time and doesn't address the actual problem.
Cause 1: Silent Model Fallback After Quota Exhaustion. This is the single most common reason for perceived quality decline, yet it's the least understood. When you exhaust your daily Nano Banana Pro quota — 2 images for free users, approximately 100 for Pro subscribers at $19.99/month — the Gemini app silently switches to the standard Nano Banana model powered by Gemini 2.5 Flash. There is no notification, no warning, and no visual indicator that you're now using a fundamentally different and less capable model. The quality difference is dramatic: Pro generates at 2K-4K resolution with enhanced reasoning, while standard generates at 1024x1024 with basic prompt interpretation. Users who generate images throughout the day often cross this threshold without realizing it, then attribute the sudden quality drop to a model degradation. For a detailed comparison of quotas across all tiers, see our free vs Pro limits guide.
Cause 2: "Thinking" vs "Fast" Mode Confusion. Nano Banana Pro requires the "Thinking" mode in the Gemini app to deliver its full quality potential. When you click "Create images," the model must be set to "Thinking" (which activates the Gemini 3 Pro engine and its mandatory reasoning steps) rather than "Fast" (which uses the standard Gemini 2.5 Flash model). Many users have unknowingly switched to "Fast" mode — perhaps by clicking the wrong option, through a UI update that changed defaults, or by using a browser extension that modifies page behavior. The quality difference between Thinking and Fast mode is roughly 35 points on a 100-point scale based on community testing, making this the single biggest "quick fix" for quality issues.
Cause 3: Input Image Compression. When using Nano Banana Pro for editing or extending existing images, the size of your input significantly impacts output quality. Although Nano Banana Pro officially accepts images up to 7MB, the system automatically compresses anything above approximately 6.8MB, resulting in a measured 35% loss of texture detail according to batch testing across 10,000+ API calls. The optimal input size is between 1.5MB and 2.5MB — large enough to preserve detail, small enough to avoid compression. Users who drag high-resolution photos directly from their camera into the Gemini app are frequently triggering this compression without realizing it, then blaming the model for "losing detail" in the output.
Cause 4: Content Moderation False Positives. Nano Banana Pro's content moderation system rejects approximately 42% of flagged requests for NSFW content, 23% for watermark removal requests, 19% for intellectual property infringement, 9% for violence, and 5% for political content. While these safety measures are necessary, they sometimes trigger on completely innocent prompts — particularly those involving real people, brand imagery, or scenes that the classifier misinterprets. When moderation triggers, the model either refuses to generate entirely or produces a deliberately degraded output as a safety measure. Users experiencing inconsistent quality with certain prompt themes may be encountering moderation rather than model degradation. Our content moderation avoidance guide covers specific strategies for navigating these restrictions while staying within Google's acceptable use policies.
Cause 5: Multi-Edit Quality Degradation. Each time you iteratively edit an image through Nano Banana Pro — asking it to "change the background," then "adjust the lighting," then "add more detail" — the model works from the output of its previous generation rather than the original input. This creates a compounding quality loss similar to repeatedly saving a JPEG file. By the third or fourth edit, artifacts accumulate, fine details blur, and the image can look significantly worse than the first generation. This is a fundamental limitation of iterative AI editing, not a model degradation, but it manifests as declining quality within a single session.
Cause 6: Server-Side Capacity Constraints. Google's infrastructure has struggled to keep up with Nano Banana Pro demand at peak times. The January 2026 4K timeout outage was the most visible example, but lower-level capacity constraints happen regularly and manifest as subtly degraded output rather than complete failures. When TPU resources are strained — particularly during Google's own model training periods — the inference pipeline may receive fewer computational resources per request, resulting in less refined outputs. This cause is entirely outside user control and typically resolves within hours.
Cause 7: Prompt Engineering Gaps. Community testing shows a dramatic quality difference between casual and optimized prompts: casual descriptions score approximately 49/100 while narrative-optimized prompts achieve 95/100 on quality metrics. Nano Banana Pro's Gemini 3 Pro engine is designed to respond to detailed, structured prompts with spatial relationships, style specifications, and specific technical parameters. Users who type simple instructions like "make it better" or "create a landscape" are not utilizing the model's reasoning capabilities and will receive outputs that don't reflect Nano Banana Pro's actual potential. This isn't quality decline — it's underutilization. For a complete guide to getting the most from the model, check our how-to-use guide.
How to Diagnose Your Specific Quality Issue
Now that you understand the seven potential causes, the next step is determining which one — or which combination — applies to your situation. The following diagnostic process eliminates causes systematically, starting with the most common and easiest to check. Work through each step in order; once you identify a match, apply the corresponding fix from the next section before continuing to check for additional causes.
Step 1: Verify Your Model Mode. Open your Gemini app or API configuration and confirm you're using "Thinking" mode, not "Fast." In the Gemini web interface, this appears as a toggle or dropdown near the image generation button. If you were on "Fast" mode, switching to "Thinking" will likely restore quality immediately. This single check resolves the issue for an estimated 25-30% of users experiencing quality decline. If you're already on "Thinking" mode, proceed to Step 2.
Step 2: Check Your Quota Status. Determine whether you've exceeded your daily Nano Banana Pro allocation. Free users get 2 Pro generations per day (reset at midnight Pacific Time). Pro subscribers get approximately 100 per day. If you've been generating images throughout the day, you may have crossed the threshold and triggered the silent fallback to standard Nano Banana. The easiest test: try generating one image at the start of a new day (after midnight PT) and compare the quality to your recent outputs. If morning quality is better, quota exhaustion is your primary issue. You can find your exact quota status in the Google AI Studio dashboard or refer to our comprehensive troubleshooting hub for step-by-step quota checking instructions.
Step 3: Evaluate Your Input Images. If you're editing existing images rather than generating from scratch, check your input file sizes. Images above 2.5MB are at risk of server-side compression, and anything above 6.8MB will almost certainly be compressed with noticeable quality loss. You can verify this by comparing results from the same prompt using a 5MB input versus a 2MB optimized version of the same image. A significant quality difference confirms input compression as a contributing factor.
Step 4: Test for Content Moderation. If your quality issues are inconsistent — some prompts work perfectly while others produce degraded output — content moderation may be selectively affecting certain generations. Try simplifying your prompt to remove any elements that could trigger safety filters (references to real people, brand names, potentially sensitive subjects). If the simplified prompt produces better quality, moderation is involved.
Step 5: Count Your Edit Iterations. If quality degraded progressively within a single session, count how many iterative edits you applied to the same image. Beyond 2-3 rounds of editing, quality degradation becomes expected behavior. The solution is to restart from the original input rather than continuing to edit a degraded output.
Step 6: Check Google's Service Status. If none of the above apply, the issue may be server-side. Check Google's Workspace Status Dashboard for any reported issues with Gemini services. Community forums on the Google Gemini Apps Community are also a quick way to verify whether others are experiencing the same issues simultaneously. Server-side issues typically resolve within hours without any user action required.
Quick Fixes That Actually Work (Cause-by-Cause Solutions)
Generic troubleshooting advice like "clear your cache" or "try again later" wastes time when you need results now. Instead, here's a fix specifically matched to each root cause identified in the diagnostic process above. Apply only the fix that corresponds to your diagnosed cause for the most efficient resolution.
Fix for Cause 1 (Silent Fallback): Track and Manage Your Quota. The most reliable solution is tracking your daily usage manually. In the Gemini web app, each generation consumes one credit from your daily allocation. When you notice quality starting to dip after multiple generations, assume you've hit the fallback threshold. For API users, monitor the response headers for model information — a switch from gemini-3-pro-image-preview to a different model identifier confirms the fallback. Practical solutions include spreading your generations across the day rather than batch-generating, or upgrading to a plan with higher quotas. The Pro tier at $19.99/month provides roughly 100 daily generations, while the Ultra tier at $99.99/month offers approximately 1,000 per day. For detailed pricing and quota comparisons, our pricing guide breaks down cost-per-image across all tiers.
Fix for Cause 2 (Wrong Mode): Switch to Thinking Mode. This is the simplest and most impactful fix. In the Gemini web interface, locate the mode selector (typically near the generation button or in settings) and ensure "Thinking" is active. The Thinking mode activates Gemini 3 Pro's mandatory reasoning pipeline, which generates internal prototypes before producing the final image. This reasoning step is what gives Nano Banana Pro its superior quality — without it, you're effectively using the standard Nano Banana model. After switching, regenerate your prompt to see the immediate quality improvement, which testing suggests averages a 35-point increase on a 100-point quality scale.
Fix for Cause 3 (Input Compression): Optimize Before Uploading. Before uploading any image to Nano Banana Pro for editing, resize it to stay within the 1.5MB-2.5MB optimal range. You can do this using any image editor — even Preview on macOS or Photos on Windows. The key parameters to adjust are resolution (2048x2048 is ideal for Nano Banana Pro's 2K output mode) and JPEG quality (85% provides an excellent balance between file size and detail preservation). For batch workflows, tools like ImageMagick can automate this preprocessing with a single command. Community testing shows that optimized 2MB inputs consistently produce quality scores around 94/100, compared to 72/100 for raw 6.8MB inputs — a 22-point improvement from a 30-second preprocessing step.
Fix for Cause 4 (Content Moderation): Rephrase Your Prompt. When you suspect moderation is degrading your output, try these approaches in order: first, remove any references to specific real people and replace with generic descriptions ("a professional woman in her 30s" instead of a specific name). Second, avoid terminology that could trigger brand or IP detection. Third, add explicit style directions that steer away from photorealistic depictions of potentially sensitive subjects. If a prompt consistently triggers moderation, restructuring it to focus on the artistic or conceptual elements rather than literal depictions often resolves the issue while achieving similar creative intent.
Fix for Cause 5 (Multi-Edit Degradation): Start Fresh After 2-3 Edits. Instead of continuously editing the same image through multiple rounds, save your intermediate result, then use it as a fresh input for the next edit session. This breaks the compounding degradation chain by giving Nano Banana Pro a clean input each time. For complex editing projects that require many iterations, plan your edits in advance and batch related changes into a single prompt rather than making incremental adjustments.
Advanced Quality Optimization Techniques
Beyond fixing specific issues, these techniques help you consistently extract the highest possible quality from Nano Banana Pro. These are professional-grade optimizations that address the gap between casual usage and the model's full capabilities, and they represent the difference between good and exceptional results.
Input Preprocessing Pipeline. Developing a consistent preprocessing workflow eliminates one of the most common sources of quality variation. The optimal pipeline involves three steps: first, resize your input to 2048x2048 pixels using bicubic interpolation (which preserves edge detail better than bilinear); second, export at JPEG quality 85-90% to hit the 1.5-2.5MB target range; third, verify the color space is sRGB, as images in Adobe RGB or ProPhoto RGB can produce unexpected color shifts in Nano Banana Pro's output. This pipeline takes roughly 30 seconds per image but consistently produces output quality scores in the 90-95 range, compared to the 65-75 range typical of unoptimized inputs. The return on investment is substantial: better quality with fewer regeneration attempts, which means fewer wasted quota credits.
Prompt Engineering for Maximum Quality. The quality gap between casual and optimized prompts is the largest single factor in Nano Banana Pro output quality — a measured 46-point difference on a 100-point scale. Effective prompts for Nano Banana Pro follow a specific structure: begin with the subject and composition, then specify the artistic style and medium, add technical parameters (lighting, camera angle, depth of field), and conclude with quality modifiers. Rather than "a sunset over mountains," an optimized prompt might specify "a panoramic sunset over snow-capped mountain range, golden hour lighting with warm orange and purple tones, atmospheric perspective creating depth between foreground wildflower meadow and distant peaks, photographed with a wide-angle lens showing dramatic sky gradient, ultra-detailed, 4K resolution." The additional specificity activates Nano Banana Pro's reasoning engine, which uses these details as constraints for its prototype-then-refine generation process.
Resolution Strategy. Nano Banana Pro offers three resolution tiers: 1K (1024x1024, included in free tier), 2K (2048x2048, $0.134 per image via API), and 4K (4096x4096, $0.24 per image via API). However, higher resolution doesn't always mean better quality. For most use cases, 2K provides the optimal balance — it's detailed enough for professional applications while being significantly faster and more reliable than 4K, which is more susceptible to timeout issues during high-demand periods. If you need 4K output for specific projects, generate during off-peak hours (early morning US Pacific time) when Google's infrastructure is least loaded.
For developers and high-volume users who need consistent quality without worrying about quota management or model fallback, third-party API platforms provide an alternative approach. Services like laozhang.ai offer Nano Banana Pro access at $0.05 per image — a 63% savings compared to Google's official $0.134 per image — with the added benefit of not being subject to the same silent fallback mechanism that causes quality surprises on the official platform. This can be particularly valuable for production workflows where predictable quality matters more than the lowest possible per-image cost.
When Quality Issues Are on Google's Side (And What You Can Do)
Not every quality decline is something you can fix. Some issues originate from Google's infrastructure, model updates, or capacity management decisions. Recognizing server-side problems saves you from endlessly troubleshooting issues that will resolve on their own, and understanding the patterns helps you plan around them.
Google's Nano Banana Pro runs on TPU v7 infrastructure, and this hardware is shared across multiple Google AI workloads including Gemini model training. During intensive training periods, inference resources — the capacity that serves your image generation requests — can be temporarily reduced. This creates subtle quality degradation that's difficult to distinguish from user-side issues: images generate without errors but lack the fine detail and prompt adherence you normally expect. The January 2026 outage, where 4K requests timed out for 5.5 hours, was an extreme example of this resource contention, but less dramatic capacity constraints happen regularly during peak demand periods.
The pattern of server-side quality issues follows predictable cycles. Weekday afternoons in US Pacific time typically see the highest demand and the most quality variation. Weekend mornings and early weekday mornings (before 8 AM Pacific) consistently produce the best results because infrastructure utilization is lowest. If you're experiencing quality issues that don't match any of the user-side causes, try generating the same prompt during an off-peak window — if the quality is noticeably better, server-side capacity constraints were likely the cause.
When server-side issues are confirmed, your options are limited but worth knowing. First, wait it out — most capacity-related quality issues resolve within hours as Google's load balancers redistribute resources. Second, for time-sensitive work, consider using alternative API endpoints through third-party providers like laozhang.ai that route requests through different infrastructure and may not be affected by the same capacity constraints. Third, if the issue persists beyond 24 hours, report it on the Google AI Developers Forum — these reports help Google's infrastructure team identify and address persistent bottlenecks.
Cost-Effective Alternatives When Quality Matters
When you've exhausted all optimization techniques and quality still isn't meeting your needs — or when the cost of maintaining quality through premium subscriptions becomes prohibitive — it's worth evaluating the full spectrum of available options. The Nano Banana Pro ecosystem now includes several access methods with different quality-cost tradeoffs.
| Access Method | Cost per Image | Daily Limit | Quality | Best For |
|---|---|---|---|---|
| Free Tier | Free | 2/day | 1K + watermark | Testing, casual use |
| Pro ($19.99/mo) | ~$0.007 | ~100/day | Up to 4K | Regular creative work |
| Ultra ($99.99/mo) | ~$0.003 | ~1,000/day | Up to 4K | High-volume production |
| Official API | $0.134 (2K) / $0.24 (4K) | Rate limited | Up to 4K | Developer integration |
| Batch API | $0.067 (2K) / $0.12 (4K) | Rate limited | Up to 4K | Bulk processing |
| laozhang.ai | $0.05 | No daily cap | Up to 4K | Cost-effective API access |
The subscription tiers offer the best per-image economics when fully utilized, but they come with the caveat that unused quota doesn't roll over. If you generate images sporadically, the per-image API options may be more cost-effective despite their higher unit price. The critical decision factor is whether predictable quality outweighs cost optimization — subscription users experience the silent fallback mechanism when exceeding daily limits, while API-based access provides consistent quality per request without hidden model switches.
For teams and businesses that rely on Nano Banana Pro for production workflows, a hybrid approach often works best: use a subscription for daily creative work and an API service for batch processing and critical-quality deliverables. This combination provides the cost efficiency of subscription pricing for routine tasks while ensuring consistent quality for client-facing work through direct API access.
What's Next for Nano Banana Pro Quality
Google's track record suggests that quality issues with Nano Banana Pro will improve over time, but the improvement cycle is gradual and sometimes punctuated by temporary regressions. The Gemini model family has consistently received updates that expanded capabilities and improved reliability, and Nano Banana Pro as a flagship image product receives significant engineering attention.
The most meaningful quality improvement users can expect in the near term is better infrastructure scaling. As Google expands TPU v7 capacity and optimizes its inference pipeline, the server-side quality issues that currently affect peak-time usage should diminish. The January 2026 outage demonstrated that Google's infrastructure team takes performance issues seriously — the root cause analysis and subsequent capacity adjustments suggest an ongoing commitment to service reliability.
For users experiencing quality decline right now, the actionable takeaway is this: most quality issues have user-side solutions. Verify your model mode is set to "Thinking," monitor your daily quota to avoid silent fallback, optimize input images to the 1.5-2.5MB range, and invest 30 seconds in structuring your prompts with specific details. These four steps alone resolve approximately 80% of reported quality decline cases. For the remaining 20% caused by server-side issues, timing your generation requests for off-peak hours and maintaining access to an alternative API endpoint like laozhang.ai ensures you always have a path to high-quality output regardless of Google's infrastructure status.
The bottom line is clear: Nano Banana Pro hasn't been secretly downgraded. Its Gemini 3 Pro engine remains one of the most capable image generation models available, producing remarkable results when used correctly. The quality decline that users experience is almost always caused by configuration issues, quota mechanics, or infrastructure constraints — all of which have concrete solutions. By understanding the seven root causes outlined in this guide and applying the diagnostic framework to your specific situation, you can consistently achieve the output quality that made Nano Banana Pro the leading AI image generator in the first place.
Frequently Asked Questions
Has Google officially downgraded Nano Banana Pro?
No. There is no evidence of a deliberate model downgrade. Google has not announced any reduction in Nano Banana Pro's capabilities. The quality issues users report are caused by a combination of silent model fallback after quota exhaustion, infrastructure capacity constraints, and user-side factors like input compression and prompt optimization. The Gemini 3 Pro Image Preview model (the engine powering Nano Banana Pro) remains unchanged from its November 2025 launch specifications.
Why does my quality get worse throughout the day?
This is the classic silent model fallback pattern. Free tier users have 2 Nano Banana Pro generations per day, and Pro subscribers have approximately 100. Once you exceed your allocation, the Gemini app silently switches to the standard Nano Banana model (Gemini 2.5 Flash), which generates at lower resolution with less sophisticated prompt interpretation. The switch happens without any notification. Daily quotas reset at midnight Pacific Time.
What's the difference between "Thinking" and "Fast" mode?
"Thinking" mode uses the Gemini 3 Pro engine, which applies mandatory reasoning steps to generate internal prototypes before producing the final image. This process takes 8-12 seconds but produces significantly higher quality output (approximately 90/100 on quality metrics). "Fast" mode uses the standard Gemini 2.5 Flash engine, which generates in about 3 seconds but produces lower quality results (approximately 55/100). Always use "Thinking" mode for Nano Banana Pro quality.
Can third-party APIs provide better quality than Google's official API?
Third-party APIs like laozhang.ai access the same underlying Nano Banana Pro model but may route requests through different infrastructure that isn't subject to the same capacity constraints. They also avoid the silent fallback mechanism since they don't enforce daily quotas in the same way. Quality per request is equivalent, but consistency can be higher because you don't encounter the hidden model switches that affect the official consumer experience.
How do I know if my quality issue is server-side or user-side?
Use the diagnostic process outlined in this guide: first check your model mode, then verify quota status, then test input optimization. If none of these resolve the issue, try generating the same prompt during off-peak hours (early morning US Pacific time). If off-peak quality is noticeably better, the issue is server-side. If quality remains poor regardless of timing, revisit the user-side causes with fresh testing.