Chinese LLMs are no longer “the cheap alternative”
Summary
Chinese LLMs like Kimi K3 and MiMo-V2.5-Pro now deliver frontier-level performance at lower cost, closing the gap with U.S. systems and potentially becoming the default choice for many teams.
Similar Articles
after a month with 5 Chinese coding LLMs, is M3 actually going to take the top spot?
A user shares a month-long comparison of five Chinese coding LLMs (Kimi K2.6, GLM-5.1, MiMo V2.5 Pro, MiniMax 2.7, DeepSeek V4 Pro) on a TypeScript/Next.js codebase, rating each in categories like frontend, backend, code review, all-rounder, and reasoning. They note MiniMax 2.7 achieves ~90% of Opus 4.6 quality at ~7% cost and speculate whether the upcoming MiniMax 3.0 will close gaps in planning and test coverage to become the top spot.
Why current LLM costs are not sustainable
The article argues that current high LLM pricing is unsustainable due to diminishing performance gains, the rise of open-weight models, specialized AI chips reducing inference costs, and zero switching costs, predicting significant price drops as competition intensifies.
I track LLM prices every 3 hours. GLM-5.2 quietly went from ~$0.57/$1.80 to $0.90/$3.08 per 1M this week, with no announcement.
A price tracker found that GLM-5.2 and Tencent's Hy3 quietly changed prices multiple times in a week, highlighting volatility in Chinese LLM pricing.
Do you think dedicated hardware for running local LLMs will become affordable anytime soon?
Discusses the potential for affordable dedicated hardware for running local LLMs, considering Chinese manufacturers' ability to produce low-cost hardware at scale.
These startups are chasing the next big thing in LLMs
MIT Technology Review reports on a wave of startups pursuing post-transformer architectures for LLMs, as the dominant model family faces growing costs, energy use, and context-length limits. Companies like Subquadratic aim to build the next generation of AI.