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AI Engineering
Hacker News·23d ago·5 min read
Claude Code sends 33k tokens before reading the prompt; OpenCode sends 7k
TL;DR
Claude Code consumes significantly more tokens than OpenCode before processing prompts, using 33,000 tokens compared to OpenCode's 7,000. This was determined by testing both models under identical conditions and measuring their token usage.
✦ Why It Matters
Choose OpenCode for projects where token efficiency is critical to reduce costs and improve performance.
Key Takeaways
- Evaluate token usage when selecting AI models for projects.
- Consider caching strategies to optimize token consumption.
- Test models under identical conditions for accurate comparisons.
- Prioritize models that minimize resource usage in production.
Full Summary
In a comparative analysis of Claude Code and OpenCode, researchers measured the token consumption of both models when tasked with generating responses. Claude Code utilized approximately 33,000 tokens, which included system prompts, tool schemas, and additional scaffolding, before even receiving the user prompt.
In contrast, OpenCode only required about 7,000 tokens, demonstrating a more efficient approach. The testing was conducted on the same machine and under identical conditions to ensure accuracy.
OpenCode's consistent request prefix allowed it to cache its payload, leading to lower token usage. These results suggest that engineers should consider token efficiency when selecting AI models for their applications, especially in resource-constrained environments.