AI model rankings may be shifting faster than old cost assumptions
A simple ranking of AI labs no longer fits the current model landscape. The old view was that OpenAI led, Anthropic was close behind, Google appeared strong when it chose to, and Chinese labs were more than a year behind on . That view now feels weak because Codex and GLM-5.2 can sit side by side in the same workflow, and DeepSeek, Qwen, and Kimi have also moved near the top within the past year.
Chinese labs may be improving faster than Western labs, and the old ranking may also have been too simple from the beginning. The key question is what creates an advantage if the largest computing cluster is not enough by itself. Possible answers include faster training cycles, better data, and techniques.
A caveat remains that some labs may be using from rather than creating every gain independently.
Key points
- The old mental model of OpenAI first, Anthropic second, Google uneven, and Chinese labs behind is breaking down.
- GLM-5.2, DeepSeek, Qwen, and Kimi are presented as signs that the field has become more crowded near the top.
- Large compute may not be the only moat for .
- Training speed, data quality, and may matter more than expected.
- should keep testing models by real task cost and output quality instead of assuming one permanent winner.