Claude Code context and token efficiency

Claude Code's overhead is structural, not accidental. Its system prompt is model-conditional - 27,787 characters sent to Sonnet versus 10,526 to Fable - and it bundles large tool schemas (trimmed from 99,778 to 82,283 characters), which together push the first-turn baseline to about 32,800 tokens against OpenCode's ~6,900 ^[1]. Add MCP servers and CLAUDE.md files and the pre-prompt payload rises to roughly 75,000 tokens, about one-sixth of a 200k window consumed before any code loads ^[4]. The subtler tax is caching: rather than reusing a byte-identical cached prefix the way OpenCode does, Claude Code advances its cache breakpoint each turn and re-writes tens of thousands of cache tokens mid-session, writing up to 54x more cache tokens (53,839 vs 1,003) on an identical file-summarize task ^[1].

Pre-prompt bloat shrinks the budget; context rot degrades what you do with it, and the two stack. Jake Minns distinguishes intrinsic rot - a fixed attention budget whose signal-to-noise falls as tokens pile up - from content rot, the accumulation of stale or contradictory notes from tool calls and failed attempts ^[2]. The decline is gradual and starts early rather than at a cliff: retrieval accuracy drops more than 30% when the relevant information sits in the middle of the window ^[2]. Practitioners describe the symptoms vividly - output quality tanking well before the nominal limit, and the agent revisiting solved problems and retrying failed approaches - with the community converging on 'context rot' as the name for why a bigger window does not guarantee better results.

The dominant community read is that this is a discipline problem, not a model problem - captured in the widely-shared 'stop blaming Claude, your harness is the problem' setup post, which reports context discipline roughly halving token usage. But the favored remedy, subagents, splits opinion. Commenter a_c warns every subagent resends the same ~30k system prompt, easily eating 30% of a 5-hour window across seven subagents before real work begins ^[5]; mips_avatar counters that those shared prompts are cache reads, roughly 10x cheaper than a full prefill ^[5]; and wongarsu notes slow subagents can evict the orchestrator's context from cache so it must be re-paid at full price ^[5]. Dedicated context layers show the upside is real: an Insforge Skills + CLI setup cut one workload from 10.4M to 3.7M tokens and 10 errors to 0 ^[3], and community demos of MCP virtualization layers claim similar savings, though such results draw healthy skepticism until independently reproduced.