MiniMax M2.7 powers local OpenCode sub-agents

MiniMax 2.7's recursive self-optimization training marks a shift from general-purpose LLMs to models built specifically for "agentic feel" and multi-step reasoning. Built-in routing for native agent teams prevents task collapse, allowing @general and @explore sub-agents to operate with high reliability. On 2026 hardware like the M3 Ultra, 512GB unified memory makes 230B parameter MoE models viable for local development. Performance reaches 200 tokens per second for prompt processing, a threshold where agentic tool-calling feels instantaneous. Finally, the synergy between OpenCode for execution and OpenClaw for persistence is defining the autonomous developer workflow.