open-source

LongCat-2.0 tops OpenRouter, but the weights aren't out yet

LongCat-2.0 posts SWE-bench Pro 59.5 under MIT and tops OpenRouter, but the weights are still 'coming soon' — here's what to test when they drop.

Jordan Reyes 3 min read
open-source china coding-agents export-controls local-llama

Meituan announced LongCat-2.0, a 1.6T-parameter MoE with 33B–56B active per token, a 1M-token context, MIT license, and a SWE-bench Pro score of 59.5. The catch: the weights are listed as "coming soon." So right now you can benchmark the marketing but you can't run the model.

The headline number is real enough to care about. SWE-bench Pro 59.5 puts this in near-frontier territory for agentic coding, and the model had already been quietly leading OpenRouter under the codename "Owl Alpha" at roughly 10.1T tokens a month. That's usage, not a benchmark — people were actually routing real coding work through it before anyone knew what it was.

What actually ships?

Right now, nothing you can `git clone`. An MIT license on a 1.6T model is a big deal, but a license on vaporweights is just a press release. Until the checkpoint lands on Hugging Face, LongCat-2.0 is an API endpoint on OpenRouter and a promise.

The more interesting fact, per VentureBeat's report on the release, is that Meituan trained this from scratch on a 50,000-card cluster of domestic Chinese ASICs — no Nvidia. If that holds up, it's the clearest signal yet that the export-control moat is leaking. DeepSeek proved you could get frontier-ish results cheap; this pushes the story to "and without US silicon."

What I'd test the moment it drops

A 1.6T MoE with 33B active per token is a serving problem, not a laptop problem. Nobody is running this locally in any honest sense — the r/LocalLLaMA excitement is about the license and the geopolitics, not about fitting it on your 4090. You'll need real multi-GPU infra or a host.

Things I'd check on day one:

  • **Real 1M context, or marketing 1M?** Run a needle-in-haystack at 200K, 500K, 900K. Long-context claims fall apart in the back half constantly.
  • **Agentic loop behavior.** SWE-bench Pro is one harness. Point it at your own repo with your own tool schema and watch how many turns it wastes re-reading files it already has in context.
  • **Tool-call format fidelity.** Chinese-trained models sometimes drift on strict JSON tool schemas. One malformed call per 20 kills an agent run.
  • **Active-param cost.** 33B active is cheaper to serve than a 70B dense model, but at 1.6T total the memory footprint for the full expert set is brutal. Price out what a host actually charges.

Should you use it?

Not yet, because you can't. But put it on the shortlist. If the weights ship under MIT as claimed and the SWE-bench Pro 59.5 survives contact with a real agent harness, this becomes a legitimate self-hostable alternative to closed coding models — and the fact that it was trained on Chinese chips means the supply of these keeps growing regardless of what Washington does. Bookmark the repo, don't rewrite your stack around a "coming soon."

FAQ

Can I run LongCat-2.0 right now?

No. As of the announcement the weights are "coming soon." You can hit it via OpenRouter's API, where it ran under the codename Owl Alpha, but there's no downloadable checkpoint yet.

Is this actually runnable locally?

Not in any practical sense. It's a 1.6T-parameter MoE with 33B–56B active per token — you need serious multi-GPU infrastructure. The local-LLM interest is about the MIT license and the China angle, not home hardware.

Why does "trained on Chinese chips" matter?

Because it undercuts the premise of US export controls. If Meituan trained a near-frontier coding model on 50,000 domestic ASICs without Nvidia, the hardware bottleneck the controls rely on is weaker than assumed.

Is SWE-bench Pro 59.5 good?

It's near-frontier for agentic coding — competitive with strong closed models. But one benchmark isn't a workflow. Test it against your own repo and tool schema before trusting the number.