Descriptions:
Running AI agents through tools like Open Claw or Hermes gets expensive quickly — not because of single queries, but because agentic workflows involve repeated cycles of planning, tool calls, error correction, and retries. Keith AI tests whether MiniMax M2.7 can serve as a practical, cost-effective backbone for these workflows, targeting the 80-90% of tasks that don’t require frontier-level reasoning.
The video frames the LLM selection problem across five categories: premium APIs (Claude, OpenAI), budget APIs (MiniMax, DeepSeek, Kimi), subsidized monthly subscriptions, free model tiers via Open Router and Nvidia, and local models. Keith quantifies the premium API cost problem from his own usage — roughly $10 per day with the Claude API during heavy agent runs — then walks through the complete setup for integrating MiniMax M2.7 into both Open Claw and Hermes. A notable inclusion is a step-by-step fix for MiniMax’s lack of native image recognition, solved by installing the MiniMax CLI and granting exec access to the agent session.
The comparison covers where MiniMax holds up (routine tasks, standard agent operations on the coding and international plans) and where it falls short (complex coding and multi-step reasoning). MiniMax’s own pricing tiers — including high-speed and standard variants — are reviewed alongside DeepSeek and Kimi. Side-by-side token usage and output quality comparisons round out a practical reference for developers running agent workflows who want to understand their real budget options beyond Claude Max or OpenAI’s premium tiers.
📺 Source: Keith AI · Published May 25, 2026
🏷️ Format: Review
