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Claude Code Opus 5.5 定价与任务成本分析

What a task costs on Opus 5.5

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做 Agent 开发的同学必看,Opus 5.5 的定价策略变了,缓存读取大幅降价,直接影响长链路成本。赶紧用官方计算器算算你的业务能省多少。

What a task costs on Opus 5.5

在 Opus 5.5 上执行一项任务的成本

Opus 5.5 costs less per token than Opus 5.

Opus 5.5 每 token 的成本低于 Opus 5。

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  • Addy Osmani
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The cost of a task, and the cost of a retry

任务的成本与重试的成本

You don't set out to buy millions of tokens. You set out to build a feature, finish a migration, or run a task. The token count is whatever the model needed to get there.

你并不是为了购买数百万个 token 而去使用模型。你是为了构建一个功能、完成一次迁移或运行一项任务。token 的数量取决于模型到达目标所需的量。

Two models with the same cost per token can cost very different amounts on the same task. One reads the code once. The other reads it, tries a fix, and reads it again. Each of those steps is a turn, and each turn resends the conversation so far. So the model that needs more turns costs more, even at the same price.

两个每 token 成本相同的模型,在同一项任务上的总花费可能大相径庭。一个模型只读取代码一次;另一个模型则先读取,尝试修复后再重新读取。每一步都是一个回合(turn),每个回合都会重新发送迄今为止的对话内容。因此,即使单价相同,需要更多回合的模型花费也更高。

By the end of this post you should be able to answer three questions about your own work:

读完本文后,你应该能够回答关于你自己工作的三个问题:

  • What do my typical tasks cost me on Opus 5.5?
  • Which settings change that, and by how much?
  • How do I check my own session usage?
  • 在 Opus 5.5 上,我典型的任务花费是多少?
  • 哪些设置会影响这一成本,影响幅度有多大?
  • 我如何查看自己的会话使用情况?

The tradeoff I want to share upfront is that every way to spend fewer tokens can also cost you a finished task. Lower effort, a smaller model, or less context can all certainly save tokens. A retry costs more than those savings. This post attempts to put a price on each tradeoff.

我想提前分享的权衡是:任何减少 token 消耗的方法也可能导致无法完成任务。更少的努力、更小的模型或更少的上下文确实都能节省 token。但重试的成本高于这些节省。本文试图为每种权衡定价。

Some numbers here are list prices, and some are illustrations built from them. The figures are interactive, so change the inputs as you read. These are best effort illustrations, so be sure to check our docs and your own math.

这里的某些数字是列表价格,另一些则是基于它们构建的示例。图表是交互式的,请在阅读时更改输入值。这些仅为尽力而为的示例,请务必查阅我们的文档并进行你自己的计算。

What does a task cost?

一个任务的成本是多少?

A task in Claude Code is a loop. The model reads the conversation, calls a tool, reads the result, and goes round again until it's done. Each trip round the loop is one request. Four things set what the loop costs.

Claude Code 中的一个任务是一个循环。模型阅读对话,调用工具,读取结果,然后再次循环,直到完成。每一轮循环是一次请求。有四个因素决定了循环的成本。

Turns. Every turn resends the conversation so far. Fewer turns means less input processed.

轮次(Turns)。每一轮都会重新发送迄今为止的对话。轮次越少,处理的输入就越少。

Cache reads. Most of what a turn resends is text the model saw on the previous turn. It's billed as a cache read, at a small fraction of the input price.

缓存读取(Cache reads)。每一轮重新发送的内容大多是模型在前一轮看到过的文本。它按缓存读取计费,价格仅为输入价格的很小一部分。

Output token type. The most expensive tokens, at five times the input price. Thinking is billed as output, so a model that reasons less on the way to the answer costs less.

输出令牌类型(Output token type)。最昂贵的令牌,价格是输入价格的五倍。思考过程按输出计费,因此推理较少的模型成本更低。

Model. Each model has its own prices, listed on the pricing page, so the model you pick sets the price of every token.

模型(Model)。每个模型都有各自的价格,列在定价页面上,因此你选择的模型决定了每个令牌的价格。

Our examples use Opus 5.5 API list prices: $4 per million input tokens, $20 per million output tokens and $0.20 per million cache reads. Like the calculator further down, the examples bill cached input at the read price and everything else at the input price, and leave out cache writes. The token counts are illustrations.

我们的示例使用 Opus 5.5 API 列表价格:每百万输入令牌 4 美元,每百万输出令牌 20 美元,每百万缓存读取 0.20 美元。与下面的计算器一样,示例将缓存输入按读取价格计费,其他所有内容按输入价格计费,并省略了缓存写入。令牌数量仅为说明用途。

Turns

轮次

Let’s say a task starts with 20K tokens of context and grows to 120K as the model reads files and tool results. At 40 turns, the average turn sends about 70K tokens. That's about 2.8M input tokens for the task, though the conversation never grew past 120K. With 90% read from cache, the input costs about $1.62. The same task in 25 turns processes about 1.75M tokens and costs about $1.02 in input.

假设一个任务以 20K 令牌的上下文开始,随着模型读取文件和工具结果,增长到 120K。在 40 轮时,平均每轮发送约 70K 令牌。这意味着该任务的输入令牌总数约为 2.8M,尽管对话从未超过 120K。如果 90% 来自缓存,输入成本约为 1.62 美元。同样的任务在 25 轮中处理约 1.75M 个令牌,输入成本约为 1.02 美元。

A turn costs more than the tokens it adds, because it resends everything before it. So the cheapest turn is the one you don't need.

一轮的成本高于它所增加的令牌,因为它会重新发送之前的所有内容。因此,最便宜的一轮是你不需要的那一轮。

One habit that can cut turns is giving the model a way to check its work. For example, a test to run, a build, or a script that calls the endpoint. A model that can check its own work finds its mistakes earlier.

减少轮次的一个习惯是给模型一种检查其工作的方式。例如,运行测试、构建或调用端点的脚本。能够自行检查工作的模型能更早发现错误。

A model that gathers what it needs in one pass, and batches its tool calls, pays the resend fewer times too.

一次性收集所需信息并批量调用工具的模型,也会减少重新发送的次数。

Cache reads

缓存读取

The same 2.8M input tokens cost $11.20 if none come from cache. At a 90% hit rate they cost $1.62, and at 96% about $0.99. No other setting moves input cost this much. A steady session keeps a high hit rate on its own. I cover some actions to avoid breaking your cache later in this post.

如果这 2.8M 个输入令牌没有任何来自缓存,成本为 11.20 美元。命中率为 90% 时,成本为 1.62 美元;命中率为 96% 时,成本约为 0.99 美元。没有其他设置能让输入成本发生如此大的变化。稳定的会话自然会保持高命中率。我在本文稍后部分介绍了一些避免破坏缓存的操作。

Output tokens

输出令牌

On Opus 5.5, an output token costs 100 times a cache read. The 60K output tokens of a typical task cost $1.20, the same as reading 6M tokens from cache. Output includes thinking. You pay for all of it, even when Claude Code only shows you a summary. That's why effort, which mostly changes how much the model thinks, moves the bill so much.

在 Opus 5.5 中,一个输出 token 的成本是缓存读取的 100 倍。典型任务的 6 万个输出 token 成本为 1.20 美元,相当于从缓存中读取 600 万个 token 的费用。输出包含思考过程。你为此付费,即使 Claude Code 只向你展示摘要。这就是为什么“努力”(effort,主要改变模型思考量)会对账单产生如此大的影响。

Model

模型

A model with cheaper cache reads mostly helps long sessions. One with cheaper output mostly helps tasks that need a lot of reasoning.

缓存读取更便宜的模型主要有助于长会话。输出更便宜的模型主要有助于需要大量推理的任务。

What changed in Opus 5.5

Opus 5.5 发生了什么变化

Two things changed: the price, and how much work the model does.

有两点发生了变化:价格,以及模型执行的工作量。

Every price line is lower. Input and output tokens are 20% cheaper than on Opus 5. Cache reads are 60% cheaper. The input price falls, and the read rate falls with it, from a tenth of the input price to a twentieth. Fig A compares the two models per million tokens. These are API list prices. On a Pro, Max or Team plan, the lower Opus 5.5 price is passed on to your limits, including cached context, so they go about 25% further than on Opus 5. The extra cut on cache reads is an API price change.

每条价格线都更低了。输入和输出 token 比 Opus 5 便宜 20%。缓存读取便宜 60%。输入价格下降,读取率也随之下降,从输入价格的十分之一降至二十分之一。图 A 比较了两个模型每百万 token 的价格。这些是 API 列表价格。在 Pro、Max 或 Team 套餐中,较低的 Opus 5.5 价格会反映在你的限额中,包括缓存上下文,因此它们比 Opus 5 多出约 25% 的使用空间。缓存读取的额外折扣是 API 价格的变化。

Fig A. Price per million tokens.

图 A. 每百万 token 的价格。

On an API key, the cache-read price cut matters most for Claude Code. A long agentic session spends most of its input on cache reads. In the session priced in Fig B below, the cache line falls from $1.00 to $0.40, the largest drop on the receipt.

对于 API 密钥而言,缓存读取价格的削减对 Claude Code 最为重要。一个长期的智能体会话将其大部分输入用于缓存读取。在下方图 B 定价的会话中,缓存行从 1.00 美元降至 0.40 美元,这是收据中最大的降幅。

How much you save depends on the shape of your work. A session that is mostly cache reads can save up to 60% on input. A short question with no cache and a long answer can save up to 20%, because output dominates it. Most Claude Code tasks sit between the two. The calculator below shows where yours sits.

你能节省多少取决于你工作的形态。主要是缓存读取的会话可以在输入上节省高达 60%。没有缓存且回答很长的简短问题可以节省高达 20%,因为输出占主导地位。大多数 Claude Code 任务介于两者之间。下面的计算器显示你的任务处于什么位置。

Opus 5.5 can use more tokens on an answer, because it always thinks before it replies. We expect people to get more done on Opus 5.5, but it varies by task, so measure it on your own work. Fig C compares cost per task on the two models. This part depends on your work far more than the price does.

Opus 5.5 可以在回答中使用更多的 token,因为它总是在回复之前进行思考。我们预计人们会在 Opus 5.5 上完成更多工作,但这因任务而异,所以请在你自己的工作中衡量它。图 C 比较了两个模型每项任务的成本。这部分比价格更依赖于你的工作。

On a well-scoped task, both models finish in about the same number of turns, and the price cut is all you get. The gap should be biggest on open-ended tasks, where a model can spend many turns on the wrong idea. No single number holds for every codebase, so measure it (see the last section).

在范围明确的任务中,两个模型完成的轮数大致相同,你得到的只是价格削减。差距应该在开放式任务中最大,因为模型可能会在错误的想法上花费很多轮次。没有一个单一的数字适用于每个代码库,所以要衡量它(见最后一节)。

Long runs end with a report. Opus 5.5 closes a long run with what it changed, what it found, and what it needs from you. That can save money too, because you rerun a session less often when you can see what happened.

长运行以报告结束。Opus 5.5 通过总结它做了什么更改、发现了什么以及需要你做什么来结束长运行。这也能省钱,因为当你能看到发生了什么时,你重新运行会话的频率会降低。

The same tasks side-by-side

相同的任务并排对比

Fig B prices one session on both models with the same token counts. So the difference is the price change and nothing else. Switch models to compare. The token counts are illustrative.

图 B 对两个模型使用相同的 token 数量计算单次会话的价格。因此,差异仅体现价格变化,无其他因素。切换模型即可进行比较。token 数量仅为示意。

Same task on Opus 5 On Opus 5.5

同一任务在 Opus 5 与 Opus 5.5 上的对比

$3.50

$3.50

at Opus 5 list prices

按 Opus 5 的列表价格

Fig B. Illustrative session with the same tokens on both models, so this is the price change alone.

图 B:两个模型使用相同 token 数量的示意性会话,因此此处仅展示价格变化。

Fig B. Illustrative session with the same tokens on both models, so this is the price change alone.

图 B:两个模型使用相同 token 数量的示意性会话,因此此处仅展示价格变化。

The receipt has the three lines /usage shows for a session. Cache reads are the biggest line by tokens, at 2M. Output is the smallest by tokens and the biggest by cost. Fresh input sits between them. It covers the first read of each file and each new tool result.

收据包含三条 /usage 记录,对应一次会话。缓存读取(Cache reads)按 token 数量计是最大的部分,达 2M。输出(Output)按 token 数量计最小,但按成本计最大。新鲜输入(Fresh input)介于两者之间。它涵盖每个文件的首次读取以及每个新工具结果的处理。

Fig B gives both models the same token counts, so it shows the price change alone. Your own sessions can use more or fewer tokens on Opus 5.5. Priced that way, the session costs about 31% less.

图 B 为两个模型分配了相同的 token 数量,因此仅显示价格变化。您自己的会话在 Opus 5.5 上可能使用更多或更少的 token。按此定价方式,会话成本降低约 31%。

A recorded run adds the second effect, the change in how much work the model does. On a task with a false start, the gap should widen. Try your own numbers

已记录的运行增加了第二种效应,即模型执行工作量的变化。对于存在失败尝试的任务,差距应进一步扩大。请使用您自己的数据进行测算。

Set what one of your tasks uses, or start from a preset. The presets are pretty illustrative but I’d still recommend doing your own math. Cached input bills at the cache-read price and fresh input at the input price, so the cache slider shows how much of the gap comes from cache reads.

设置您某个任务所使用的参数,或从预设开始。这些预设具有很强的示意性,但我仍建议您自行计算。缓存输入按缓存读取价格计费,新鲜输入按输入价格计费,因此缓存滑块显示了差距中有多少来自缓存读取。

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