Gold on IMO and Putnam from a free 685B open model? DeepSeek just made elite math reasoning accessible to every dev.
What if the best math brain on the planet was open-source and ran on your GPUs? DeepSeek just made that real with Math-V2.
DeepSeek released Math-V2, a 685-billion-parameter open-weights model dominating math benchmarks. It snags gold-medal performance on IMO and Putnam - levels matching top proprietary rivals. Available now on Hugging Face, this is tuned specifically for reasoning over numbers and proofs[1].
Devs building STEM apps, quant tools, or education platforms: this plugs right into your workflow. No more brittle RAG hacks for math - native reasoning means reliable step-by-step solves for theorem proving, optimization, or data analysis. Pairs perfectly with tools like SymPy for hybrid symbolic-numeric power[1].
Versus closed math specialists like OpenAI’s o1, Math-V2 is fully open, letting you inspect, fine-tune, or distill to smaller sizes. In the landscape, it joins GLM-5 and earlier DeepSeek hits, creating an open math stack that rivals Claude’s reasoning edge without subscriptions. ‘Benchmaxxing’ trends from 2025 amplify this - RLVR-trained models like these are 2026’s focus[1].
Head to Hugging Face, spin it up in Colab or your cluster. Test on AIME problems or your custom evals - then integrate into notebooks or APIs. Question is, how long before this powers your next trading bot or research paper?
Source: dentro.de/ai