Developers are currently witnessing a fundamental shift in how they interact with their codebases, moving away from simple autocomplete suggestions toward fully autonomous agents. The promise is a world where an AI can navigate a repository, execute tests, and fix bugs with a single high-level instruction. However, as teams integrate these agents into their daily workflows, a new and unexpected friction point has emerged: the invisible tax of the bootstrap prompt. The excitement of autonomous coding is increasingly colliding with the reality of the API bill, where the cost of a single question is no longer determined by the length of the answer, but by the massive architectural overhead required to make the agent functional.

The Architecture of the Baseline Cost

When evaluating the operational efficiency of a coding agent, the most critical metric is the size of the system prompt and tool definitions sent with every request. Claude Code, Anthropic's specialized coding agent, carries a heavy initial load, consuming approximately 33,000 tokens on its first request. In stark contrast, OpenCode, an open-source alternative, requires only about 6,900 tokens to achieve its initial state. This means Claude Code enters a conversation with a cost baseline 4.7 times higher than OpenCode before the user has even typed a single character of their query.

This discrepancy stems from the sheer volume of instructions and capabilities the agent must carry. Claude Code defines 27 distinct tools, which account for roughly 24,000 tokens of the payload. Its system prompt adds another 6.5k tokens to the mix. OpenCode takes a leaner approach, utilizing only 10 tools that occupy about 4,800 tokens, paired with a system prompt of approximately 2.0k tokens. The correlation is direct: the more tools an agent is equipped with, the higher the baseline cost for every interaction.

For developers working in production environments, these costs scale rapidly. The inclusion of instruction files such as `AGENTS.md` or `CLAUDE.md` within a repository adds an average of 20,000 tokens per request. Furthermore, integrating five MCP (Model Context Protocol) servers—the standard for connecting AI models to external data—injects an additional 5,000 to 7,000 tokens. In a fully configured professional environment, the baseline cost can skyrocket to between 75,000 and 85,000 tokens before the first command is even processed. The complexity of the developer's environment effectively becomes a multiplier for the API expenditure.

The Tension Between Cache Efficiency and Batching

While the initial token count provides a snapshot of cost, the true financial impact is revealed through cache efficiency and request patterns. OpenCode utilizes a request prefix that remains byte-identical across executions, allowing it to pay the cache cost only once per session. Claude Code, however, frequently rewrites tens of thousands of prompt cache tokens mid-session. In specific operational tasks, this lack of stability leads to Claude Code consuming up to 54 times more cache tokens than OpenCode.

Yet, a counter-intuitive efficiency emerges when tasks move from simple queries to multi-step operations. Claude Code employs a batching strategy, grouping multiple tool calls into a small number of requests. OpenCode follows a sequential structure, executing one tool call per turn. While OpenCode's baseline is lower, it must pay that baseline repeatedly for every single turn. In complex, multi-stage workflows, Claude Code's ability to minimize the total number of requests can actually result in lower overall token consumption than the more lightweight but chatty OpenCode.

This efficiency is completely erased, however, when developers implement a fan-out architecture using sub-agents. In one observed instance, a small task that required 121,000 tokens surged to 513,000 tokens when the work was split between two sub-agents. This happens because each sub-agent must pay its own bootstrap cost independently. When the parent agent then consumes the transcripts of these sub-agents, the costs compound. This recursive duplication creates a multiplier effect where distributing a task can increase the cost by more than four times compared to a single-agent execution.

Ultimately, the total cost of operating an AI agent is a simple product of the baseline token cost and the total number of requests. The strategic choice for enterprises lies in balancing the high-capability, high-baseline approach of batching against the low-baseline, high-frequency approach of sequential execution.