Introduction

Progress in AI is driven not by a single model, but by the ability to continually improve upon the current state of models. Achieving this requires treating model development as a system-level optimization problem, for which the solution is building a hill-climbing machine for rapid improvement. Our process includes a scaling-focused framework for pretraining modeling decisions, as well as a robust reinforcement learning recipe and infrastructure that sustains long, log-linear performance improvement. The first model developed using our process is MAI-Thinking-1, a 35B active / 1T total parameter MoE that stands among the strongest models of similar size on STEM reasoning and coding tasks (e.g., 52.8% on SWE-Bench Pro, 97.0% on AIME 2025, and 87.7% on LiveCodeBench v6). MAI-Thinking-1 is trained from-scratch, exclusively on clean, enterprise-grade data, without distillation from third-party models. In this technical report, we offer a deep dive into the development of MAI-Thinking-1. By sharing our technical details and learnings we hope to cultivate a transparent and science-driven approach to further development in AI.

Benchmarking

Training

Parameters