On the day of its release, the community noticed that with the same 1M context, LongCat 2.0 ($0.30/$1.20) was about 10 times cheaper than Kimi K3 ($3.00/$15.00) for input and about 12 times cheaper for output. It was then "the cheapest 1M+ context model" on OpenRouter—but the post also warned that the listing page showed no quality data: "aggressive pricing could be a land-grab, or it could be genuinely efficient."
OpenRouter comparison during the same window (release date 2026-07-20):
Meituan LongCat 2.0: $0.30/M in, $1.20/M out, 1048k context
Kimi K3: $3.00/M in, $15.00/M out (same 1M context)
Muse Spark 1.1: $1.25/M in, $4.25/M out
Thinking Machines Inkling: $1.00/M in, $4.05/M out
Conclusion: LongCat 2.0 was about 10x cheaper than Kimi K3 for input and about 12x cheaper for output; it was, "by a wide margin," the cheapest 1M+ context model on the list.
Concern: "The listing itself shows no quality information—no eval scores, no parameter count, no benchmark numbers… This kind of aggressive pricing could be a move to grab market share, or it could genuinely be an efficient model."
Community comments: One user noted that DeepSeek V4 Flash was significantly cheaper (but did not have a 1M context); another comment joked that "only one of the two can reliably run its own model" (referring to Kimi).
The pricing data is verifiable (the OpenRouter history page; see the 2026-08-18 snapshot collected in Evaluation 03: LongCat was still listed at $0.30/$1.20, with an actual input transaction price of $0.03872/M); use the OpenRouter live page for the Kimi K3 price.
The post explicitly identifies the absence of quality data as a limitation. This is consistent with BenchLM's "no shared benchmark" (Evaluation 11) and AlphaSignal's verification (Evaluation 04): the price comparison holds, while a quality comparison is missing.
Applicability: For budget-sensitive use cases that need a 1M context, LongCat 2.0 is a candidate for the current pricing sweet spot. But "10 times cheaper" does not mean "10 times better value"; it needs to be evaluated on the actual task (see Evaluations 05–09).
LongCat 2.0