Independent analysis reveals stark contrasts in token efficiency between Claude Code and the OpenCode assistant, highlighting significant cost and latency implications for production agentic workflows. Researchers deployed a logging proxy at the API boundary to capture exact request payloads and usage blocks, ensuring ground-truth telemetry unaffected by model-side abstractions or gateway wrapping. On initial interactions, Claude Code consumes approximately 33,000 tokens before user input arrives, driven by a comprehensive tool schema suite, background orchestration prompts, and injected system reminders. OpenCode operates at roughly 7,000 tokens with a streamlined prompt and ten core tools. This substantial baseline immediately occupies over 15 percent of a standard 200,000 context window, accelerating context consumption and compaction costs before meaningful code generation begins. Cache stability proved to be the defining differentiator. OpenCode maintains byte-identical request prefixes across sessions, enabling full payload caching and minimal cache-write overhead. Claude Code exhibits prefix instability, frequently rewriting tens of thousands of cache tokens mid-session due to dynamic scaffolding and varying agent system prompts. Consequently, Claude Code generated up to 54 times more cache writes than OpenCode on identical tasks, directly inflating operational costs under premium pricing tiers where cache writes command a substantial markup. Real-world configurations further amplify baseline expenses. Adding a 72KB production instruction file increases per-request token counts by approximately 20,000. Integrating multiple MCP servers or subagent delegation introduces substantial overhead, with Claude Code's subagent architecture multiplying token consumption by 4.2 times for parallelized tasks, as each delegation carries its own bootstrap cost and parent ingestion requirement. OpenCode handles similar configurations with a leaner architectural footprint and reduced per-call payloads. Despite its higher baseline and cache inefficiency, Claude Code demonstrates performance advantages on complex, multi-step workflows. By batching tool calls into parallel round trips, it reduced cumulative input to approximately 121,000 tokens across multiple requests, compared to OpenCode's serial approach, which consumed roughly 132,000 tokens. The total cost convergence ultimately depends on task parallelization versus sequential execution patterns. The findings underscore that harness architecture and environment configuration, rather than model selection alone, dictate AI agent economics. The study also addresses growing regulatory demands, emphasizing that granular API-level telemetry is essential for EU AI Act compliance and production audit trails. Researchers have open-sourced their measurement framework, enabling organizations to verify exact inference payloads and establish reproducible token benchmarks for July 2026 and beyond.