“The entire reinforcement learning phase used only 512 H800s for three weeks, with a rental cost of just $534,700,” the company explained. “This is an order of magnitude less than initially anticipated.”
However, industry analysts urge caution. “MiniMax’s debut reasoning model, M1, has generated justified excitement with its claim of reducing computational demands by up to 70% compared to peers like DeepSeek-R1,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “However, amid growing scrutiny of AI benchmarking practices, enterprises must independently replicate such claims across practical workloads.”
Extended context capabilities
M1’s most impressive feature might be its ability to handle massive amounts of information simultaneously. With a context window supporting one million input tokens and up to 80,000 output tokens, the model can essentially read and understand multiple novel tasks at once while maintaining coherence across the entire conversation.
This story originally appeared on Computerworld
