LongCat-2.0 (Meituan) vs Nano Banana 2.1 (Gemini Nano Banana 2.1, formerly Nano Banana 2)

Nano Banana 2.1 (Gemini Nano Banana 2.1, formerly Nano Banana 2) is the clear winner: 8.9/10 (A-tier) versus 7.9/10 (B-tier). LongCat-2.0 (Meituan) isn't a bad tool, but on every category that drives the overall score, Nano Banana 2.1 (Gemini Nano Banana 2.1, formerly Nano Banana 2) comes out ahead. The tier gap is repeatable -- not methodology noise -- and the day-to-day experience reflects it.

Pricing-wise, both tools have a free tier (LongCat-2.0 (Meituan) starts $0, Nano Banana 2.1 (Gemini Nano Banana 2.1, formerly Nano Banana 2) starts $0), so you can test either without committing. Compare what each free tier actually unlocks -- usage caps, model access, and feature gates differ a lot more than the headline price suggests, especially as both vendors have tightened limits in 2026.

By use case: pick LongCat-2.0 (Meituan) when teams that want frontier-class open weights for audit, fine-tuning research, or api use at aggressive pricing -- especially agentic-coding workloads where its swe-bench pro showing and 1m context matter. Pick Nano Banana 2.1 (Gemini Nano Banana 2.1, formerly Nano Banana 2) when designers, marketers, and content creators who need readable text in images (social posts, ad creative, book covers, infographics, event flyers) and who are already using or willing to pay for gemini. The two tools aren't fighting for the same person -- they're aiming at adjacent jobs that occasionally overlap. If you're squarely in Nano Banana 2.1 (Gemini Nano Banana 2.1, formerly Nano Banana 2)'s lane, the tier-list ranking and the use-case fit point the same direction; if you're in LongCat-2.0 (Meituan)'s lane, the score gap matters less than the fit.

Bottom line: Nano Banana 2.1 (Gemini Nano Banana 2.1, formerly Nano Banana 2) is the better tool for most people right now. Pick LongCat-2.0 (Meituan) only when teams that want frontier-class open weights for audit, fine-tuning research, or api use at aggressive pricing -- especially agentic-coding workloads where its swe-bench pro showing and 1m context matter -- that's its lane, and inside that lane it still earns its place.