China AI Open Models are AGGRESSIVELY Accelerating — Superbash Learn

Superbash Learn

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Key takeaways

Switch at least one non-trivial workload to GLM-5.2 this week and benchmark it against Opus 4.8 before committing further to a single frontier API.
Buy the Chinese monthly pass bundling GLM-5.2, DeepSeek, Kimi, MiniMax, Mimo, and Qwen instead of running six separate accounts, and stop rotating API keys manually.
Architect a mixture-of-agents pipeline that uses GLM-5.2 as the reasoning node with Kimi and MiniMax as supporting endpoints, the way Tone B Studio did.
Set up WeChat Pay or Alipay before Long Cat 2 access closes further — it requires domestic payment rails and is a 1.6T-parameter, ~48B-active, 1M-context model trained on 35T tokens.
Treat the 50,000-domestic-accelerator training claim on Long Cat 2 as a signal to plan for hardware-diversified inference, not just model-diversified inference.
Avoid building any new product that depends on a single frontier API without an open-weight fallback — API rate limits are reversible, model weights are not.
Track Meituan's Owl Alpha on OpenRouter as a proxy read on Long Cat 2 quality, since both come from the same lab.

Video summary

Companion notes

GLM-5.2 has become the default Chinese open model, and a new bundled access pass plus a 1.6T-parameter domestic-training model (Long Cat 2) prove the stack is shifting from experiment to utility.

GLM-5.2 as the current default

The creator states that GLM-5.2 is "still the hottest open model in discussion," with many builders preferring it to Opus 4.8. Usage is "abnormally large" from the China side, signaling that an open model is now a default reference point rather than a curiosity.

Bundled API access — one pass, six models

Chinese providers are productizing access with a monthly pass bundling GLM-5.2 alongside DeepSeek, Kimi, MiniMax, Mimo, and Qwen. The explicit goal is "reducing friction around API keys and provider churn." If you currently juggle five dashboards, this is the cheapest way to test the field without rotating credentials weekly.

Mixture-of-agents on open weights

Tone B Studio tested a mixture-of-agents setup using GLM-5.2 together with Kimi and MiniMax — treating GLM as "a reasoning node in a larger ensemble rather than a single endpoint." This is the architecture worth copying if you need specialized routing across Chinese models.

Long Cat 2 — near-frontier training on domestic silicon

Long Cat 2 dropped on the day of recording. The specs are concrete: 1.6 trillion total parameters, ~48 billion active, 1 million context window, 35 trillion training tokens, N-gram embeddings, and sparse attention. The strategic line is "training on 50,000 Chinese accelerators" — making it plausibly the first near-frontier model trained at this scale on domestic Chinese hardware. The model is also made by Meituan (the Keeta food-delivery operator), and you can only pay via WeChat Pay or Alipay.

API clampdowns push developers to weights

Clem of Hugging Face and the Information both argue that restricting frontier API access "does not slow developers down" — it just pushes them toward weights they control. The creator's framing: "you cannot unrelease a model file."

Watch on YouTube

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