Testing MiniMax M2.7 via API on three real ML and coding workflows

Testing MiniMax M2.7 via API on three real ML and coding workflows I recently got access to some MiniMax M2.7 API credits, so I decided to plug this model directly into Claude Code and run it on three workflows I do regularly. The same tasks were run using Claude Opus 4.7 as the comparison baseline. The three workflows: scaffolding an entry for an active Kaggle competition, drafting and auditing knowledge-base notes for my Obsidian vault, and updating an old PyTorch project that became outdated. I wanted to find out how well M2.7 works inside an agentic loop when the task has clear boundaries. The results were consistent across the three runs: M2.7 was useful when the constraints were explicit, and the output format was concrete. It stumbled when important context was left implicit, though some of the same gaps appeared with Opus 4.7 as well. For the more open-ended cases, I would still keep a human review pass in the loop. Setup I added a claude-mm command that points Claude Code at…