Open-weights AI models have become good enough

Open-weights AI models from Chinese labs Moonshot AI (Kimi K3) and Alibaba (Qwen 3.8 Max) have reached a 'good enough' threshold for serious AI-assisted engineering, according to developer Senko's tests. While not matching frontier models like Anthropic's Fable 5 or OpenAI's GPT-5.5 Sol, they performed comparably in coding benchmarks, offering insurance against potential restrictions on proprietary models. The models' Chinese origin raises concerns about censorship and alignment, but their open nature allows post-training removal of such layers.

Open-weights AI models have become good enough Over the past week I've played around with Kimi K3 https://www.kimi.com/blog/kimi-k3 by Moonshot AI and Qwen 3.8 Max https://x.com/Alibaba Qwen/status/2078759124914098291 by Alibaba. Both are large Chinese open-weight models weights promised to be released soon and tout benchmarks showing they're as capable as the frontier western models Fable 5 by Anthropic and GPT-5.5 Sol by OpenAI . I wouldn't go that far, but these are really capable models. In my AI-coding tests, both have performed really well. Compare the test mini-games on my vibe-coding benchmark https://senko.net/vibecode-bench/ generated by Fable https://senko.net/vibecode-bench/2026/rts-fable-5.html , Sol https://senko.net/vibecode-bench/2026/rts-gpt-5.6-sol.html , K3 https://senko.net/vibecode-bench/2026/rts-kimi-k3.html and Qwen https://senko.net/vibecode-bench/2026/rts-qwen-3.8-max.html . In a more serious test, building a web app to a provided spec, K3, Qwen and Fable generated very similar results. An AI judge GPT-5.5 Sol via Codex gave a positive code review to all three of course, it did find some problems and room for improvement , with Fable being slightly better. “Good enough” is great What I am most excited about, though, is that the open-weights models have reached the threshold of “good enough” for serious AI-assisted engineering. The Anthropic and OpenAI models reached this around October-November last year, and it unleashed a huge adoption wave across the software development industry. But there was always a possibility of a rug-pull: either through intelligence tokens becoming too expensive and not included via standard subscriptions , models being explicitly withheld as with the Fable fiasco or nerfed Fable and Sol, with their overly aggressive allowed-usage filters . Open models are insurance against this. Although these models are too large to be reasonably hosted locally, there's already a cottage industry of third-party inference providers. And for organizations that really need to keep all their data close, there exists an expensive option of hosting it themselves. The Chinese scare There's much angst in the West about these models being Chinese https://stratechery.com/2026/whos-afraid-of-chinese-models/ . As such, they might reflect a kind of world-view that's not completely compatible with traditional western values. There's also a question of breaking western labs' terms of service and copying from them. Both Anthropic and OpenAI, having scraped the entire public internet and trained on copyrighted material such as books, don't get to complain. My personal view is that training on copyrighted material is fair use, but you don't then get to disallow training on your API results. The censorship and model alignment that follows the ideas of the Chinese government is real though let's not downplay the alignment done by western labs for different topics . But with these models being open, the censorship layer can relatively easily be stripped away by post-training. Western open models The Chinese models Kimi, Qwen, DeepSeek, GLM are the most prominent, but they don't have a monopoly on open models: one of the most popular small models is Google DeepMind's Gemma https://deepmind.google/models/gemma/ . Then there are models like Laguna S2.1 https://poolside.ai/blog/introducing-laguna-s-2-1 , Nemotron 3 https://research.nvidia.com/labs/nemotron/Nemotron-3/ and Mistral Small 4 https://mistral.ai/news/mistral-small-4/ and many more, which usually don't make a big splash but are nevertheless very capable. Taken together, there is a healthy ecosystem of open LLMs of various sizes from 4B to 3T params . Most of them are open-weights meaning you only get the weights , but some are fully open source you get the training data as well . As these models reach the point of “good enough” in their niches or for specific use cases like AI coding I started this post with , they are already solid alternatives to the big popular models. Hardware The one big difference between open-weights models and free software, in terms of user experience, is which hardware you need. Today mid-2026 , the average hobbyist has no way of running the large models in any usable way. Computers like Mac Studio, NVidia DGX Spark or the AMD Strix Halo, with unified memory, cost on the order of $5000 and can only run models up to 100B-200B params. Kimi K3 is 2.8T and Qwen 3.8 Max is 2.6T I'm optimistic, though. There's so much money sloshing around AI-capable hardware NVidia has a $5T market cap and 85% profit margins, high-bandwidth memory HBM makers are also rolling in money that I have no doubt there's going to be increased competition, increased performance, and reduced pricing in a few years' time it takes time to build fabs, sorry .