MiniMax M2.5 and cheap long-running agent work
MiniMax M2.5 is interesting because it pushes the cost side of agentic coding and productivity work very aggressively.
Source
MiniMax M2.5: Built for Real-World Productivity
Why I saved it
I saved this because MiniMax is pushing a very practical claim: agentic work should become cheap enough to run for long periods.
That matters. If agent loops need many tool calls, retries, tests, and verification steps, then cost can decide whether a product is possible.
My notes
- MiniMax says M2.5 is trained with reinforcement learning in many real-world environments.
- The post highlights coding, tool use, search, office work, and BrowseComp-style tasks.
- It makes a strong cost argument for continuous agent operation.
- I want to compare these claims against independent benchmarks and my own repo tasks.
What I want to remember
The model that wins a one-shot benchmark may not be the best model for a long-running agent. Cost, latency, and stable tool use matter a lot.