How do I tell if low resolution in an AI model is an intentional design limit or a quality problem?

Check three signals: is the resolution cap documented in the model's specs and pricing tiers, is it consistent across every generation, and does it buy a compensating advantage in cost or speed? Documented, uniform, and traded for something is a design limit. Undocumented, fluctuating, and uncompensated is a quality problem.

Start with the model's published specs and pricing tiers — intentional limits are always written down. Google Omni Flash outputs 720p by default, upscales to 1080p at no cost, and charges the equivalent of a full generation for 4K: that tier structure is the resolution ceiling stated as a product decision. Nano Banana 2 Lite caps output at 1K resolution, and its documentation frames that explicitly as a design trade-off. If the resolution you're getting matches a documented tier, you're looking at design, not defect.

Next, check whether the cap buys something. Deliberate resolution limits are exchanged for cost or speed: Nano Banana 2 Lite's 1K ceiling comes with 3-cents-per-image pricing, 2.5x faster generation than the standard tier, and roughly one-quarter the cost of Nano Banana Pro — 1,000 images for $30. As one review put it, "the cheapest model is no longer the worst model": Lite outperforms Pro on text-to-image quality in Google's own benchmarks despite the lower resolution. If your low-resolution output comes with no offsetting speed or price advantage, treat it as a symptom, not a spec.

Then test consistency across generations. Design caps are uniform — every output lands at exactly the stated resolution. Defects fluctuate: in one 30-plus-output model test, stop-motion clips oscillated between 12 FPS and 8 FPS across generations, and that kind of run-to-run inconsistency is the signature of a quality problem rather than a documented constraint. Users report the same pattern with resolution — undocumented forced output sizes and inconsistent generation behavior get flagged in community threads as suspected backend bugs, not accepted as limits.

Finally, run the documented upscale path. If the platform's own upscale (Omni Flash's free 1080p step, or its paid 4K generation) resolves the softness cleanly, the base resolution was a default, not a flaw. If upscaling amplifies smearing or mushy detail, the underlying generation quality is the problem — pixel count and image quality are separate axes, which is why even native 4K output doesn't guarantee production-grade textures.

Once you've confirmed a cap is intentional, route around it rather than fighting it: use the capped, cheap tier for exploration and escalate only selected assets to a higher-resolution model like Nano Banana Pro. Inside invideo, which hosts all of these models, the invideo agent handles that routing per generation, so a design limit becomes a cost lever instead of a ceiling.

Watch some of these to see what works for you:

the cheapest model is no longer the worst model

— invideo's creative team