MiniMax M3's 1M-token context beats GPT-5.5 at 5% compute cost
minimax released m3, an open-weight model with a 1-million-token context and sparse attention that slashes compute by 95%. it beats gpt-5.5 on coding benchmarks, signaling that proprietary moats are eroding as open models match proprietary capabilities.
chinese ai company minimax launched m3, an open-weight model combining native multimodality, a 1-million-token context, and coding performance competitive with proprietary leaders like gpt-5.5 and opus 4.7. the model uses 'minimax sparse attention' to cut compute costs by 95% and boost processing speeds, allowing it to achieve 83.5 points on browsecomp and 59% on swe-bench pro. internal tests showed m3 performing autonomous tasks such as reproducing research papers and optimizing nvidia gpu kernels for 24 hours without intervention. this development signals a significant shift in the ai landscape, as open models begin to rival proprietary systems in complex reasoning and long-context tasks.