I still don't understand what the incentive is for releasing genuinely good model weights. What makes sense however is OpenAI releasing a somewhat generic model like gpt-oss that games the benchmarks just for PR. Or some Chinese companies doing the same to cut the ground from under the feet of American big tech. Are we really hopeful we'll still get decent open weights models in the future?
Until there is a sustainable, profitable and moat-building business model for generative AI, the competition is not to have the best proprietary model, but rather to raise the most VC money to be well positioned when that business model does arise.
Releasing a near stat-of-the-art open model instanly catapults companies to a valuation of several billion dollars, making it possible raise money to acquire GPUs and train more SOTA models.
Now, what happens if such a business model does not emerge? I hope we won't find out!
gpt-oss is killing the ongoing AIME3 competition on kaggle. They're using a hidden, new set of problems, IMO level, handcrafted to be "AI hardened". And gpt-oss submissions are at ~33/50 right now, two weeks into the competition. The benchmarks (at least for math) were not gamed at all. They are really good at math.
There is a leaderboard [1] but we'll have to wait till april for the competition to end to know what models they're using. The current number 3 on there (34/50) has mentioned in discussions that they're using gpt-oss-120b. There were also some scores shared for gpt-oss-20b, in the 25/50 range.
The next "public" model is qwen30b-thinking at 23/50.
Competition is limited to 1 H100 (80GB) and 5h runtime for 50 problems. So larger open models (deepseek, larger qwens) don't fit.
I find the qwen3 models spend a ton of thinking tokens which could hamstring them on the runtime limitations. Gpt-oss 120b is much more focused and steerable there.
The token use chart in the OP release page demonstrates the Qwen issue well.
Token churn does help smaller models on math tasks, but for general purpose stuff it seems to hurt.
