Descriptions:
Fahd Mirza walks through integrating MiniMax M2.7 — a newly released model distinguished by having participated in its own training process — with OpenClaw on Ubuntu via the Anthropic-compatible API endpoint at platform.minimax.io. The setup covers the full OpenClaw installation, provider selection from the wizard, API key entry, and manual model ID specification for M2.7 since the OpenClaw build hadn’t yet added it to the default list.
To demonstrate capabilities, Mirza prompts MiniMax M2.7 to generate a fully self-contained task management app as a single HTML file — drag-and-drop columns, dark mode, countdown timer, zero external dependencies — in one shot. He then tests the model’s self-improvement loop by asking it to review its own code, identify inefficiencies, and fix them autonomously, showing concrete output changes across iterations without additional human prompting.
On benchmarks, MiniMax M2.7 scores 86.2% on the standardized OpenClaw agent task suite — placing it just behind Claude Sonnet and other top frontier models — and ranks 8th globally in agentic web development on the Artificial Analysis index, ahead of every non-Anthropic, non-Google, non-OpenAI model with the score marked preliminary. Artificial Analysis positions it in the “most attractive” cost-vs-intelligence quadrant, with a composite score of 50, making it a strong option for developers seeking capable agentic performance at competitive API pricing through the MiniMax token plan.
📺 Source: Fahd Mirza · Published March 20, 2026
🏷️ Format: Tutorial Demo
