How to Run OpenClaw with Open-Source Models

2026-05-23 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The OpenClaw assistant, facing Anthropic's ban on Claude Code subscriptions and high API pricing for Claude Opus 4.6, now requires alternative Large Language Models. Initial attempts with OpenAI's GPT-5.4 encountered "lazy" behavior, prompting a search for more effective options. This led to the identification of Chinese alternatives: the open-source Kimi-K2.5 and GLM-5.1, and the proprietary MiniMax-M2.7. The content details how to run OpenClaw with these diverse models, focusing on Kimi-K2.5, outlining optimization tricks, and discussing its specific downsides to ensure an effective assistant.

Key takeaway

For AI Engineers managing OpenClaw deployments, the recent Claude subscription ban and high API costs for Claude Opus 4.6 mean you must actively seek alternative LLMs. You should evaluate open-source options like Kimi-K2.5 and GLM-5.1 to ensure continued assistant functionality. Be prepared to address potential "lazy" model behaviors and implement optimization strategies to maintain an effective and cost-efficient OpenClaw assistant.

Key insights

OpenClaw can be powered by alternative LLMs like Kimi-K2.5 following Claude's subscription ban.

Principles

• LLM "laziness" can hinder assistant effectiveness.
• Open-source LLMs offer viable alternatives to proprietary models.

Topics

• OpenClaw
• Alternative LLMs
• Open-source Models
• Kimi-K2.5
• Claude Opus 4.6
• API Costs
• Model Optimization

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.