Claude Code consumes approximately 33,000 tokens prior to receiving prompts, compared to OpenCode's 7,000 tokens. This significant difference highlights Claude Code's inefficiency in token usage and caching methods, impacting operational costs for users employing agentic AI.
In a head-to-head evaluation, Claude Code consumed around 33,000 tokens before even receiving the prompt, while OpenCode utilized roughly 7,000 tokens. This indicates a stark difference in how each platform manages token usage during API calls.
Claude Code demonstrates poor cache efficiency, rewriting tens of thousands of prompt-cache tokens during sessions. In contrast, OpenCode maintains a consistent, byte-identical request prefix, allowing for significant savings on caching costs.
Additional configuration files, such as AGENTS.md, contribute significantly to the token count, with production repositories reaching 75,000 to 85,000 tokens before a user interaction occurs. This bloat emphasizes the importance of understanding token overhead in AI operations.
Using subagents increases token consumption, where a task requiring 121,000 tokens directly could escalate to 513,000 tokens when distributed among two subagents due to individual bootstrap costs.
Understanding token usage is critical for managing operational costs in AI deployments, particularly under regulatory frameworks like the EU AI Act. Agencies must be able to log and analyze the data sent by their AI systems for compliance and performance evaluation.
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Claude Code consumes approximately 33,000 tokens prior to receiving prompts, compared to OpenCode's 7,000 tokens. This significant difference highlights Claude Code's inefficiency in token usage and caching methods, impacting operational costs for users employing agentic AI.