Muse Spark (Meta) vs LongCat-2.0 (Meituan)

Muse Spark (Meta) edges out LongCat-2.0 (Meituan) by 0.9 points (8.8 vs 7.9) -- a A-tier vs B-tier split that's narrow but real. Not a blowout; both belong on a shortlist. The score gap shows up most clearly in the categories that matter for Muse Spark (Meta)'s strengths, so if those categories are your priority, the lead translates.

Pricing-wise, both tools have a free tier (Muse Spark (Meta) starts $0, LongCat-2.0 (Meituan) 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 Muse Spark (Meta) when anyone who wants frontier-level ai for free. 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. The two tools aren't fighting for the same person -- they're aiming at adjacent jobs that occasionally overlap. If you're squarely in Muse Spark (Meta)'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: Muse Spark (Meta) is the safer default for most readers, but LongCat-2.0 (Meituan) is competitive enough that the tie-breaker is your specific workload, not the spec sheet.