I run mlcontests.com, a website that lists machine learning competitions from across multiple platforms - Kaggle, AIcrowd, Zindi, Codabench, Tianchi, etc…

Like previous years, I’ve just written up a summary of last year’s competitions and winning solutions.

With help from several of the competition platforms, I tracked down around 400 competitions that happened last year, as well as info on the #1 winning solution for 73 of those.

Some highlights:

• Tabular data competitions are starting to show potential signs of change: after years of gradient-boosted decision trees dominating, AutoML packages (specifically AutoGluon) and tabular foundation models (TabPFN) were used in some winning solutions. Having said that, GBDTs (in particular, XGBoost and LightGBM, and to a slightly lesser extent, Catboost) were still the go-to for most tabular problems, sometimes in an ensemble with a neural net. One winner used TabM.
• Compute budgets are growing! At the extreme high end, one team (of NVIDIA employees) used 512 H100s for 48 hours to train their winning solution for the AI Mathematical Olympiad progress prize 2. Equivalent on-demand cloud cost for that would be around $60k. At least 3 other winning teams also used over $500 worth of compute, which is more than we'd generally seen in previous years. In contrast, there are also still plenty of people training winning solutions only on Kaggle Notebooks or other free compute. (including third-place on the AIMO progress prize 2, which didn't involve any training!)
• In language/reasoning competitions, Qwen2.5 and Qwen3 models were the go-to. Almost every winning solution to a text-related competition used Qwen in some way. Unlike previous years, there was very little use of BERT-style models in winning solutions.
• Efficiency is a key component of quite a few solutions, and for text competitions that often means using vLLM (for inference) or Unsloth (for fine-tuning). Some teams used LoRA, some did full fine-tuning (if they have the GPUs).
  
• For the first time, Transformer-based models won more vision competitions than CNN-based ones, though CNN-based models still won several vision competitions.
  
• In audio competitions featuring human speech, most winners fine-tuned a version of OpenAI's Whisper model.
• PyTorch was used in 98% of solutions that used deep learning. Of those, about 20% used PyTorch Lightning too.
• Somewhat surprisingly, Polars uptake was still quite low and no winners used JAX.
• None of the big budget prizes -- ARC, AIMO, Konwinski -- have paid out a grand prize yet, though in AIMO 3 (currently happening) the scores are getting close to the grand prize amount.

Python packages popular among competition winners

Way more info in the full report, which you can read here (no paywall, no cookies): https://mlcontests.com/state-of-machine-learning-competitions-2025?ref=mlcr25