Published · 6d agoScience2 min read
The Cheap Model Billed More Per Turn: Routing to Haiku Mid-Session Costs 94% More
Prompt caching bills a warm frontier prefix at 10 percent of base rate. Once that holds, the cheap model's cold prefill of the same transcript is the expensive option.
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What happened
- Anthropic currently charges $5 per million input tokens for Opus 5 and $1 per million input tokens for Haiku 4.5.
- A single coding task can push 400K to 2M cumulative input tokens through the API, because every turn re-sends the full conversation so far.
- With prompt caching, when the next request starts with the exact same tokens as the last one, the provider reuses the stored key/value state instead of recomputing it and bills those tokens at 10% of the base input rate.
- The cache belongs to one model: Opus 5 and Haiku 4.5 have different weights and separate cache stores, and a prefix that is warm on one does not directly transfer to the other.
- A router that switches models mid-session discards the warm prefix and forces a full cold prefill of the entire conversation on the new model, so the bill covers the whole transcript rather than only the new instruction.
Compiled by The ScientistSomething wrong?How this is made
Why it matters
The +94% is arithmetic on posted prices, not a benchmark. Anthropic charges $5 per million input tokens for Opus 5 and $1 per million for Haiku 4.5 [1], and on a long session a router that hands one easy turn down to the cheap model raises that turn's input cost by about 94% [11]. That is the inverse of what a difficulty router is installed to do [6].
The mechanism is the cache, not the model. A request whose prefix matches the previous one is billed at 10% of the base input rate [3], so a warm Opus prefix reads at $0.50 per million rather than $5 [10]. But the cache belongs to one model: Opus 5 and Haiku 4.5 have different weights and separate cache stores, and a warm prefix does not transfer [4]. Switching discards the prefix and forces a cold prefill of the entire conversation on the new model, so the cheap rate applies to the whole transcript rather than to the new instruction [5]. And every turn re-sends the full conversation, which is why a single coding task can push 400K to 2M cumulative input tokens [2].
Run the numbers on a turn carrying 300,000 cached tokens plus a 1,000-token instruction: $0.155 on Opus with the cache warm, $0.301 on Haiku cold, or +94% [11]. As the new instruction shrinks toward nothing, the penalty converges on $1 over $0.50, a hard ceiling of +100% [12]. Switching only pays when new tokens exceed about 11% of the turn's input [13], which is rare deep in a session. The five-minute default cache lifetime, refreshed free only when the entry is used [7], compounds it: turns sent elsewhere do not refresh the frontier prefix, so a stretch of cheap turns can also expire the expensive model's cache [14].
Two things move the figure: a cheap model priced under 10% of the frontier rate, which would put it below $0.50 per million here [13], and routers that decide at request or task boundaries instead of at each step [9].
Claim ledger
Ranked by verification strength, evidence, and original report placement.
- [1]
Anthropic currently charges $5 per million input tokens for Opus 5 and $1 per million input tokens for Haiku 4.5.
- [2]
A single coding task can push 400K to 2M cumulative input tokens through the API, because every turn re-sends the full conversation so far.
ReportedView cited source - [3]
With prompt caching, when the next request starts with the exact same tokens as the last one, the provider reuses the stored key/value state instead of recomputing it and bills those tokens at 10% of the base input rate.
ReportedView cited source - [4]
The cache belongs to one model: Opus 5 and Haiku 4.5 have different weights and separate cache stores, and a prefix that is warm on one does not directly transfer to the other.
ReportedView cited source - [5]
A router that switches models mid-session discards the warm prefix and forces a full cold prefill of the entire conversation on the new model, so the bill covers the whole transcript rather than only the new instruction.
ReportedView cited source - [6]
Model routing is presented as one of the few cost levers in an agent stack that works without touching prompts, tools or model quality targets: a router scores the incoming prompt for difficulty and sends easy ones to a cheaper model, which on a workload where 70% of turns are trivial can produce large savings, but the reduction only holds for single independent prompts and not inside a long session.
ReportedView cited source
Sources & coverage · 3 publishers
The reporting this story was synthesized from, earliest first. Every link goes to the original.
- blog.dailydoseofds.com6d agoA Cheaper Model Does Not Imply a Cheaper Turn
- platform.claude.com6d agoPrompt caching - Claude Platform Docs
- developer.nvidia.com6d agoRoute AI Agents Across Models with NVIDIA NeMo Switchyard | NVIDIA Technical Blog


