I Ran OpenCode Instead of Claude Code for Two Weeks. It Was 78% Slower — and I Kept It Anyway.
Summary
After two weeks of using OpenCode as a coding agent, an engineer found it to be 78% slower than Claude Code on the same underlying model but ultimately adopted it as their default. OpenCode, which crossed 160,000 GitHub stars, is model-agnostic, supporting over 75 providers including local models like Qwen 3.6 (27B parameters, quantized to ~17GB) via Ollama on a 36GB MacBook Pro. Its integration with the Language Server Protocol allows the agent to see code diagnostics, improving accuracy. While initially frustrated by the speed, the author realized the cost savings and flexibility of switching between free local models for routine tasks and paid frontier APIs for complex problems outweighed the latency. This hybrid approach significantly reduced API spend and mitigated vendor lock-in, despite OpenCode's beta desktop application and slower, more verbose operation.
Key takeaway
For AI Engineers or Software Engineers evaluating coding agents for daily development, you should prioritize model flexibility and cost-efficiency over raw speed for most tasks. A model-agnostic agent like OpenCode allows you to seamlessly switch between free local models for routine work and paid frontier APIs for complex problems. This hybrid approach significantly reduces API spend and mitigates vendor lock-in, optimizing your workflow for both cost and capability.
Key insights
The value of a coding agent lies in its flexibility to integrate diverse models, not just raw speed.
Principles
- Vendor lock-in limits experimentation.
- Model-agnostic tools enable hybrid workflows.
- Slower, verbose agents can improve plan verification.
Method
OpenCode functions as an agent harness with a terminal UI, desktop app (beta), and editor extension. It uses a tool loop, session management, and plan-versus-build mode, integrating Language Server Protocol diagnostics for feedback.
In practice
- Configure OpenCode to switch between local and hosted models.
- Use local models for routine tasks to reduce API costs.
- Review agent plans carefully, especially with slower, verbose agents.
Topics
- OpenCode
- Coding Agents
- LLM Orchestration
- Local LLMs
- Vendor Lock-in
- Developer Productivity
Best for: Machine Learning Engineer, CTO, VP of Engineering/Data, Software Engineer, AI Engineer, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.