Meituan releases LongCat-2.0 open-source 1.6T MoE model

The most disruptive fact about LongCat-2.0 is not its size but its supply chain. Meituan says the full pretraining run and large-scale deployment were completed on a roughly 50,000-card cluster of domestic Chinese chips, making it the first trillion-parameter model trained end-to-end on local hardware ^[5]. The company processed more than 30 trillion tokens through that stack - upward of 35T across training and deployment validation - without top-end Nvidia GPUs ^[6]. For an industry that has treated access to restricted American silicon as the gating factor for frontier work, that is a direct counterexample. The Geopolitechs analysis frames the shift bluntly: the binding constraint has moved to whether domestic hardware plus systems engineering can carry a serious model, and 'frontier-scale AI work can emerge from places that American policy and foreign investors were not watching closely enough' ^[6]. It is worth holding one caveat firmly, though. Meituan has claimed full domestic-chip training but has not disclosed which chips, so the headline cannot be independently verified ^[6]. The community filled that vacuum with guesses - Huawei's Ascend line is the popular bet - but skeptics on r/LocalLLaMA pushed back that this looks like 'good enough' mature-process silicon rather than cutting-edge parity, pointing to interconnect speeds well below the InfiniBand fabrics Western labs run. The signal is real; the exact hardware story is still partly a black box.

Agents are expensive for a boring reason: they read the same codebase, system prompt, and tool definitions over and over, and every iteration round re-bills that context. LongCat-2.0 attacks this directly with a cache-hit-free mechanism - cached input-prefix reads are simply not charged ^[7]. Layer that on top of already-aggressive per-token pricing (standard rates around $0.75 per million input and $2.95 per million output tokens, with a promotional tier near $0.30 and $1.20, roughly 6-7x cheaper input and 10x cheaper output than GPT-5.5) and the economics of deep iterative exploration change shape ^[7]. Cost no longer compounds linearly with the number of interaction rounds, which is exactly the failure mode that makes long agent sessions painful today. Developers quoted via PANews called it a move that 'changes the economics of Agent costs' ^[7]. For teams running agents that grind through a large repository across dozens of turns, free cached reads plus a sub-frontier price tag is a more consequential lever than a point or two on a benchmark.

A 1-million-token context is the headline feature, and LongCat Sparse Attention (LSA) is the mechanism that makes it affordable. Standard attention scales quadratically with context length; LSA selects only the most relevant tokens, dropping that cost closer to linear ^[3]. The design stacks three orthogonal improvements - Streaming-aware Indexing, Cross-Layer Indexing, and Hierarchical Indexing - to decide which tokens actually matter at each layer ^[2]. On r/LocalLLaMA, practitioners read LSA as an evolution of DeepSeek Sparse Attention paired with a 3-step multi-token-prediction path for speculative decoding and roughly 97% MoE sparsity per token, which fits the picture of a giant model that only ever lights up about 48 billion of its 1.6 trillion parameters at a time ^[1]. The practical payoff is coherence over very long inputs - the same property that let it hold up as a coding agent chewing through entire repositories.

The benchmark sheet is where LongCat-2.0's ambition is easiest to read. It posts 59.5 on SWE-bench Pro, edging past GPT-5.5's 58.6, alongside 70.8 on Terminal-Bench 2.1 and 77.3 on SWE-bench Multilingual ^[3]. Those are agentic-coding scores, not trivia, and they line up with how the model behaved in the wild. Before the reveal, running as Owl Alpha on OpenRouter, it was moving roughly 10.1 trillion tokens a month - about 559 billion tokens a day - and growing 242% month over month, enough to top the platform's agent-usage charts ^[4]. The scale story underneath is equally stark: 1.6 trillion total parameters with roughly 48 billion active per token (a dynamic 33B-56B range) ^[1], trained on more than 30 trillion tokens ^[6]. Read together, the numbers describe a model that earned real production demand anonymously before anyone knew whose it was.

The most useful thing about an open release is that people run it immediately, and the r/LocalLLaMA reception was sharp and specific. The consensus was not 'best model ever' but something more calibrated: one commenter who had already pushed 3.6 billion tokens through it as Owl Alpha via the Hermes agent called it explicitly NOT a reasoning model, but strong at instruction-following, planning, and long-context coherence - the exact profile you want in a coding harness rather than a math olympiad. YouTube hands-on coverage echoed the 'three brains fused' framing, noting only a sliver of the network activates per token. The other loud thread was skepticism about the hardware claim: contrarians argued the domestic chips look like mature-process 'good enough' silicon rather than leading-edge parity, and that a 200Gbps RDMA fabric is slow next to the 800Gb InfiniBand interconnects frontier labs use. Practically, downloading the weights is a commitment - 3.55TB in BF16, 2.05TB in FP8 - so most of the excitement is flowing through the hosted OpenAI- and Anthropic-compatible endpoints rather than local runs ^[3].