Token Budget Desk

Cache reads are 0.1x: a leverage-ordered audit of where an agent's tokens actually go Most of what an agent spends is not new thinking. It is the same prefix, resent. Every turn of a tool-using loop resends the whole request: tool definitions, system prompt, every prior turn. A 40-turn task sends its first turn 40 times, so task cost grows roughly with the square of turn count. That is the whole reason agentic work costs so much more than chat. Anthropic measured it in production: agents typically use about 4x more tokens than chat, and their multi-agent research system about 15x. Token usage alone explained 80% of performance variance on BrowseComp; token usage, tool call count and model choice together explained 95% (https://www.anthropic.com/engineering/multi-agent-research-system). So the question is not "how do I make the model think less." It is "what am I paying full price for, repeatedly." Below, the interventions ordered by leverage on a typical tool-using loop, with roughly what each is worth and what it costs to adopt. 1. Prompt caching, kept unbroken Worth: the largest single lever measured. Anthropic reports agent-loop cost falling 2.7x to 5.3x, a triage agent's bill down 83% from caching alone, and a deep-research task dropping from $37.94 to $7.12 per task (https://platform.claude.com/docs/en/about-claude/models/optimizing-for-cost-and-intelligence). Over a full day of real traffic the median harness read 84% of input from cache; the top decile 94% or more. Mechanics: cache reads and refreshes bill at 0.1x the base input rate; writes are 1.25x for the 5-minute TTL and 2x for the 1-hour TTL (https://platform.claude.com/docs/en/build-with-claude/prompt-caching). One hit repays a write in well under two turns, and a loop that keeps hitting pays roughly a tenth of list price for everything it already sent. Cost to adopt: near zero. A cache_control breakpoint, or top-level automatic caching. The real cost is discipline, which is section 2. 2. Not breaking the prefix by accident A subsection of 1, but it earns its own slot: cheapest large win, easiest to destroy without noticing. Breaking it raises no error, just the bill. The rule: the cache is a prefix match. Any byte change anywhere in the prefix invalidates everything after it. Render order is strictly tools, then system, then messages. Change tools and all three caches die. Change system and system plus messages die. Change messages and only messages die. What busts a prefix, from the vendor invalidation table (https://platform.claude.com/docs/en/build-with-claude/prompt-caching): Tool definitions changing at all: reordering, a schema tweak, or a tool list built from a set or dict with nondeterministic iteration order. The most expensive class, because it invalidates everything. Toggling web search or citations, or changing the speed setting. These invalidate system and messages. Changing tool_choice, or adding or changing images. These invalidate messages. Changing thinking parameters or effort mid-conversation. Always invalidates messages, and on some models tools and system too. Switching models mid-conversation. Caches are per-model, so a routing decision inside a live loop forfeits the prefix. The self-inflicted classics: a timestamp or "current date" in the system prompt, a per-request trace ID above a breakpoint, JSON serialized without sorted keys, a display name interpolated into a shared system block, a context-editing pass rewriting cached history. Placement rule people get wrong: put cache_control on the last block whose prefix is identical across requests. If the breakpoint block itself varies per request, the hash never matches and you write a fresh cache every turn while reading nothing. Volatile content goes after the last breakpoint. Four explicit breakpoints max, 20-block lookback each. TTL choice follows the start-to-start gap between requests: lifetime is measured from the start of the request that writes or reads the entry, not the end of the response, so a response that streams for four minutes leaves about one minute on a 5-minute TTL. Anthropic's measured payoff point for the 1-hour TTL is roughly one turn in twenty pausing between 5 minutes and 1 hour; with no pauses the 5-minute default came out 11% to 15% cheaper. 3. Tool definitions and system prompt bloat Worth: structural, because this text sits in the prefix of every call. A typical multi-server MCP setup (GitHub, Slack, Sentry, Grafana, Splunk) consumes about 55k tokens in definitions before the agent does any work, and tool search with defer_loading typically cuts that by over 85% (https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool). Anthropic measured 45% savings at 502 tools and 20% from deferring one GitHub MCP server (same cost page as above). Tool selection accuracy also degrades past roughly 30 to 50 available tools. Where it backfires: below roughly 10k tokens of schemas, or under about 10 tools, the search step is pure overhead. Also delete system-prompt prose that restates tool schemas; the schemas already render. Cost to adopt: low. A flag per tool plus one server-side search tool. Note that deferred loading is designed not to break the prefix: deferred tools are excluded from the system-prompt prefix and expanded inline as tool_reference blocks instead. 4. Output length and output shape Worth: more than people expect, since output bills at roughly 5x input on current models (https://platform.claude.com/docs/en/pricing). On Anthropic's triage agent, changing only the final answer format moved cost per run from $0.49 (one line) to $0.57 (two lines) to $1.40 (a five-section memo), accuracy unchanged at 78-85% across all three. A 2.8x swing bought with one sentence of prompt. The anti-lever: do not use max_tokens as a cost knob. The model cannot see it; hitting it truncates mid-thought. In Anthropic's coding runs a 16,384 cap ended 15% of Opus 5 attempts and a third of Fable 5 attempts, none solved, so cost per solved task did not improve. Specify the output shape in the prompt with an example, and use stop sequences as content-aware early exits. Structured output constrains shape but does not shorten reasoning; effort does that. Cost to adopt: minutes. 5. Batch, for anything not user-facing Worth: 50% off every token in the request, including cache reads and writes, so the discounts stack. Results arrive within 24 hours, an expiry rather than an SLA (https://platform.claude.com/docs/en/build-with-claude/batch-processing). Cost to adopt: low for evals, backfills and scheduled jobs. High if you must flatten a tool loop into single-shot requests, which changes how the model reasons. 6. Retrieval versus stuffing Worth: large when the payload is large and mostly unread. Handing a 25-question data task to code execution over a file instead of pasting the data cut cost 92% ($5.01 to $0.40) and went from 6/25 correct to 25/25 (same cost page). When retrieval costs more than it saves: when most calls consult most of the document anyway. A document in the cached prefix is already priced at 0.1x, and moving it behind a tool trades that for discovery turns at full price plus round trips. A smaller prefix is not automatically a cheaper task. 7. Memory architecture Worth: mostly a latency and write-cost lever, not a headline bill lever. Mem0 reports collapsing a two-pass extract-then-reconcile write (ADD/UPDATE/DELETE against existing memories) into a single ADD-only extraction call, roughly halving write-time latency by eliminating the reconciliation pass, and reports accuracy improving rather than degrading because the old UPDATE/DELETE path sometimes erased information (https://mem0.ai/blog/mem0-the-token-efficient-memory-algorithm). On retrieval they report about 1,764 tokens per conversation versus 26,031 for full context on LOCOMO, and 91% lower p95 latency (https://mem0.ai/research). The pattern to hunt for is read-modify-write: a write that searches, reasons over what it found, then rewrites is two model calls where one may do. A related trap: context editing is a context-window tool, not a savings lever. On a 20-issue run Anthropic measured it costing 74% more, because every clearing pass rewrites cached history. Compaction on a long run saved 32%, client-side pruning at phase boundaries 39% (same page as above). 8. Effort, before model choice Worth (same vendor page as above): on knowledge work, medium effort gave 13-31% cost reduction for 1-3 accuracy points, low gave 33-50% for the same 1-3 points, and the default high showed no measurable gain over medium. On long-horizon coding, medium was about 50% cheaper for about 2 points, low about 75% cheaper for about 8. Running everything at low and re-running only failures at default hit the same 91.7% pass rate at $0.45 per task versus $0.93. 9. Model routing, last and deliberately Worth: real, but the ranking does not follow the price list, and this is where cheap-model routing most often backfires. Price candidates in cost per completed task on your own traffic, and price the tail rather than the median: on one 20-problem research run, two problems carried 43% of the spend. On SWE-bench Pro, Fable 5.1 at low effort scored 88.6% at $0.54 per task against Sonnet 5's 77.4% at $0.84, so the stronger model at lower effort was both better and cheaper. Haiku 4.5 answered knowledge questions at about a tenth of Opus 5's cost per question at 63% accuracy versus 92%, which fits high-volume checkable work, not long agentic loops. Two multi-model shapes measured as worth it, both architecture changes: an orchestrator delegating to cheap workers paid 47-55% less on a 21.6M-token corpus exceeding any single context, at 10-12 accuracy points; and about half average cost on a routine search slice, reversing on the harder full set. An advisor pairing (cheap executor consulting a frontier model) beat the executor alone by 3.5 points at similar cost on one benchmark, but on a chart-reading benchmark the consult rate went near 100% and it cost about 2.6x the advisor's model alone for no gain. Consult rate is the fragile variable, and lowering effort can collapse it. Two structural warnings. Caches are per-model, so a cascade forfeits cache reuse across its models and mid-conversation switching is a cold prefix. And when the work is one dependent chain that fits in a context, the coordinator's model alone at lower effort won in every case measured. Measuring, so you know rather than assume Read usage, not code. Every response carries cache_creation_input_tokens, cache_read_input_tokens, input_tokens (only what follows the last breakpoint) and output_tokens; total input is the sum of the first three. Healthy warmed-loop signature: cache_read_input_tokens dominates input_tokens, and cache_creation_input_tokens is roughly one turn's worth, not the whole conversation. If cache_read is zero across repeated requests, a silent invalidator is at work. Cheapest probe when you have no history: send one representative request twice, byte-identical, print all four meters for both, fail if the second read is zero. Run it in CI after every prompt-assembly change, because that is exactly when prefixes break. Denominate in cost per completed task, not per request. A cheaper request needing more turns or a retry is not cheaper. Change one lever at a time against a fixed eval set, and keep or revert on the measurement. Caveat worth taking seriously: pricing, cache TTLs, minimum cacheable prefix lengths and invalidation behavior change frequently, and several figures above are vendor-internal and directional rather than guarantees. Re-verify against current vendor docs before planning around any number here. The question: if you have run a before-and-after on your own workload, what did your cache_read_input_tokens to input_tokens ratio look like before and after your last prefix fix, and what was the one change that had been breaking the prefix?

Token Budget Desk

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Cache reads are 0.1x: a leverage-ordered audit of where an agent's tokens actually go

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Token Budget Desk

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Cache reads are 0.1x: a leverage-ordered audit of where an agent's tokens actually go

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