Claude Code’s Hidden Prompt Bloat: How Attribution Headers Sabotage Local Model Efficiency

Recent investigations by developers using local instances of Anthropic’s Claude Code have uncovered a critical performance bottleneck rooted in the tool’s attribution header system. According to a detailed report posted on the r/LocalLLaMA subreddit, the Claude Code CLI client was appending a dynamically generated billing header—x-anthropic-billing-header: cc_version=2.1.39.c39; cc_entrypoint=cli; cch=56445;—to every user prompt as a system message. This header, which changes with each request, forces the underlying language model to reprocess the entire prompt history from scratch, nullifying the efficiency gains of key-value (KV) cache reuse that local LLM deployments rely on for responsiveness.

As one user noted, this behavior turned what should be a low-latency, locally hosted coding assistant into a sluggish, token-intensive process. The culprit was identified as Anthropic’s attempt to track usage metrics and attribution for its Claude Code product, even when deployed offline. The header’s inclusion in the system prompt, rather than as a metadata header in the API request, meant that the model treated it as part of the conversational context, triggering full re-embedding and attention computation on every interaction.

According to a GitHub issue (#1161) referenced by the original poster, the solution was straightforward: setting the environment variable CLAUDE_CODE_ATTRIBUTION_HEADER=0 in the user’s ~/.claude/settings.json file. This disables the injection of the billing header into the prompt entirely, allowing the KV cache to function as intended. Users who applied this fix reported latency reductions of up to 70% and restored responsiveness for iterative coding workflows.

This issue highlights a broader tension in the AI tooling ecosystem: the conflict between commercial telemetry and user performance expectations. While companies like Anthropic have legitimate reasons to monitor product usage—especially for freemium or enterprise-tier tools—the method of embedding tracking data directly into user prompts represents a misstep in user-centric design. As local LLM adoption grows, developers increasingly demand tools that respect their infrastructure optimizations, not undermine them with opaque background processes.

According to a discussion on Hacker News, similar issues have been reported with other AI coding assistants that inject metadata into prompts, though none as systematically as Claude Code’s current implementation. One developer noted that enabling "fast mode" in Claude’s web interface—designed to reduce latency by trimming non-essential context—also implicitly avoids such overhead, suggesting Anthropic is aware of the performance trade-offs but has not yet aligned its local and cloud experiences.

Meanwhile, the lack of official documentation on this behavior has left many users frustrated. The fix, while simple, is buried in community forums and GitHub threads, not in Anthropic’s official Claude Code documentation. This raises questions about transparency and user support for enterprise and developer-facing tools. As AI tools become more embedded in professional workflows, the expectation for predictable, high-performance behavior is no longer optional—it’s a baseline requirement.

For now, users running Claude Code locally are advised to manually configure their settings to disable attribution headers. Developers are encouraged to audit their system prompts for unintended metadata injection, especially when integrating proprietary or third-party AI tools. Anthropic has not yet issued a public statement or patch, but given the growing community outcry, a formal fix may be imminent. Until then, the lesson is clear: when AI tools claim to be "local," they must truly respect local execution environments—not just in deployment, but in design.