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tech 12 July 2026

Claude Code vs OpenCode: Understanding Token Overhead

In the realm of multi-agent systems, Claude Code and OpenCode stand out. However, their efficiency in terms of tokens used before even reading a prompt significantly differs. Let's explore why this matters for your project.

Article inspired by the original source
Claude Code sends 33k tokens before reading the prompt; OpenCode sends 7k ↗ systima.ai

Introduction: A World of Tokens

In the development of multi-agent systems, efficiency is crucial. Claude Code and OpenCode, two major platforms, exemplify this dynamic. The difference in token consumption before even reading a prompt is significant: approximately 33,000 tokens for Claude Code versus 7,000 for OpenCode. But why does this difference matter?

Why Measure Token Overhead?

In an environment where every token counts, reducing overhead can lower costs and improve performance. With high cache costs and inefficient token use, Claude Code can quickly become expensive. In contrast, OpenCode optimizes token usage, allowing substantial savings.

Comparative Analysis: Claude Code vs OpenCode

When Claude Code uses 33,000 tokens before even reading a request, it includes system prompts, tool schemas, and injected scaffolding. OpenCode, on the other hand, uses just 7,000 tokens. This disparity directly impacts efficiency and costs.

The Cost of Cache

OpenCode maximizes cache use by ensuring its request prefix is identical with each run. Claude Code, however, rewrites and overloads its cache, thus increasing costs. In practice, this means OpenCode can maintain low costs, while Claude Code sees its costs quickly rise.

Configuration and Complexity

Production configurations add thousands of additional tokens. For example, a 72KB instruction file can add an average of 20,000 tokens to each Claude Code request. OpenCode, with a more streamlined approach, avoids this type of token bloat.

Implications for Development

For tech decision-makers and developers, understanding these differences is crucial for optimizing costs and performance. In projects where the budget is tight, OpenCode might be the wise choice. However, for complex tasks requiring increased flexibility, Claude Code remains appealing despite its cost.

Conclusion

Token efficiency is a key element in choosing an agent platform. Claude Code and OpenCode offer distinct solutions with significant budgetary and operational implications. Choosing between them requires careful evaluation of the project's specific needs.

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Claude Code OpenCode token overhead multi-agent systems AI efficiency
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