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Claude Opus 5.5 vs Claude Sonnet 5.5

vs

何时用哪一个

这是同一个 Claude 系列的两个层级,而非双生模型:Anthropic 将 claude-opus-5-5 定位为更高层级,专为长时间运行的智能体编程与知识工作而构建,并将 claude-sonnet-5-5 定位为较低层级,指出其速度更快。在纸面参数上,它们共享 1000000-token 的上下文、128000 的最大输出、文本与图像输入以及 $0.2 的缓存读取费用,因此费率表体现了层级差距:claude-opus-5-5 为 $4 输入与 $20 输出,是定价为 $2 与 $10 的 claude-sonnet-5-5 的 2 倍。请将大流量与延迟敏感型流量放在 claude-sonnet-5-5 上,并将耗时长且困难的智能体任务发送给 claude-opus-5-5。

Benchmark 成绩

Claude Sonnet 5.5:厂商没有公布过 benchmark 成绩。

高于同侪均值无人分数更高Claude Opus 5.59 / 97 / 9
Claude Opus 5.5 Claude Sonnet 5.5 其他被测模型 同侪均值 ★ 无人分数更高
Terminal-bench 4.0
无人分数更高 66.4%
N/A
OSWorld 2.0 partial
无人分数更高 81.8%
N/A
Terminal-Bench-Science 0.1
58.7%
N/A
Humanity's Last Exam with tools
无人分数更高 67.7%
N/A
AutomationBench
40%
N/A
Chartography with tools
无人分数更高 89%
N/A

厂商公布: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Z.ai

定价

Claude Opus 5.5 Claude Sonnet 5.5 Δ
输入 / 1M tokens $4 $2 2×
输出 / 1M tokens $20 $10 2×
缓存读取 / 1M tokens $0.2 $0.2 =
缓存写入 1.25x (5m) / 2x (1h) 1.25x (5m) / 2x (1h) -

费率取自构建时的实时目录;每个模型页面均附有当前的费率卡。

它们的位置 — 以该计费单位计费的所有 76 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

能力

Claude Opus 5.5 Claude Sonnet 5.5
工具使用 是 是
思考控制 始终开启 始终开启
结构化输出 是 是
提示词缓存 显式(由您标记前缀) 显式(由您标记前缀)
缓存生存时间 5m default, 1h option 5m default, 1h option
最小缓存前缀 1024 个 token 1024 个 token

规格

Claude Opus 5.5 Claude Sonnet 5.5
输入模态 文本 图像 文本 图像
输出模态 文本 文本
发布日期 2026-09-22 2026-09-28
知识截止日期 2026-06 2026-06
上下文窗口 1M 1M
最大输出 128K 128K
思考参数 output_config.effort thinking.type
允许的值
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
thinking.type
  • adaptive (default)
  • between_tools
默认值 medium adaptive, effort high

规格转录自各供应商的文档;若供应商未发布某项数据,则直接省略该行,而非进行推断。 完整来源: Claude Opus 5.5 · Claude Sonnet 5.5

单个 Prompt,两个模型 —— 通过网关实测

提示词 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 检查 恰好 3 句

Claude Opus 5.5 通过 · 3 sentences

When we turned on reasoning, the model began "thinking out loud" before each answer, generating thousands of internal working tokens that users never see but that we are billed for just like the final response. Those hidden tokens are charged at the output rate, typically the most expensive rate, so the same question with the same visible answer now costs roughly twice as much or more. We can bring the bill back down without losing the quality gains by capping the reasoning budget and reserving reasoning mode for the complex tasks that actually benefit from it.

输出 263 tok (+102 思考) 延迟 5.7 s

Claude Sonnet 5.5 通过 · 3 sentences

Enabling reasoning makes the model "think" before it answers, and that internal thinking is generated as extra text (reasoning tokens) that we are billed for at the same rate as output tokens, even though users never see it. Output tokens also cost several times more than input tokens, so a single request that used to produce a short answer can now generate many times more billable text behind the scenes. Because our request volume and prompts didn't change, the doubling comes almost entirely from this hidden thinking, and we can bring it down by limiting reasoning effort, turning it off for simple tasks, and reserving it for the workloads where the accuracy gain justifies the cost.

输出 205 tok 延迟 3.6 s

指令遵循(恰好三句,可数)、受众适配(面向 CFO 的语气),以及下方 token 计量所暴露的隐藏思考计费缺口。

提示词 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 检查 修复通过测试

Claude Opus 5.5 通过 · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` **Explanation:** The original code counted *matching adjacent pairs* rather than *items in a run*. Both `cur` and `best` started at 0, and `cur` reset to 0 on a mismatch, so a run of length *n* was reported as *n − 1*. For a single-element list the loop never executes, so it returned 0 instead of 1. For `[5, 5]` it returned 1 instead of 2. The fix treats every element as the start of a run of length 1: `best` and `cur` start at 1, and `cur` resets to 1 (not 0) when the value changes. An explicit check returns 0 for an empty list. The "misses runs at the last index" reports are the same off-by-one bug rather than a separate one. […]

输出 723 tok (+303 思考) 延迟 10.4 s

Claude Sonnet 5.5 通过 · 8/8 cases

**The bug:** `cur` counts the number of *adjacent equal pairs* rather than the number of items in the run. A run of length k contains only k−1 such pairs, so every result is one too small. The counter also starts at 0 and the loop starts at index 1, so a single-element list never updates `best` and returns 0 instead of 1. (An empty list should return 0.) In addition, `cur` resets to 0 on a mismatch, when it should reset to 1, because the item at `xs[i]` begins a new run of length 1. The claim that it "misses runs ending at the last index" is really the same off-by-one: the final run is counted, but as one less than its true length. Because `best` is updated inside the loop on every extension, the last run isn't skipped. […]

输出 444 tok 延迟 6.7 s

修复是否真的正确(可运行)、解释的信息密度,以及在一个边界明确的任务上的 token 效率。

提示词 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 检查 合法 JSON,schema 精确

Claude Opus 5.5 通过 · 5/5 fields, guidance "Withheld pending Q4 close"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` **Judgment calls to check:** - **quarter**: Kept as "September quarter" rather than "Q3", since the fiscal calendar isn't stated. The text mentions an upcoming "Q4 close," so it may be fiscal Q3, but that's not certain. - **revenue_usd_m**: The text says only "412 for the topline." It doesn't state currency or units, and the figure is on a constant-currency basis, not reported USD. USD millions is an assumption. Use `null` if you need strictly stated values. - **growth_pct**: The source says "eleven and change," so the true figure is slightly above 11. 11 is an approximation. - **guidance**: Guidance was explicitly withheld. I recorded that rather than `null`, since the text addresses it directly.

输出 829 tok (+488 思考) 延迟 10.9 s

Claude Sonnet 5.5 通过 · 5/5 fields, guidance null

Here is the extracted JSON: ```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on the choices: - **quarter**: The text says "September quarter" without specifying a fiscal year or Q-number, so I kept the wording as stated. - **revenue_usd_m**: The text gives "412" for the topline, with no currency or unit stated explicitly. I've taken it as 412 million USD per the field definition, but the text does not confirm that, and the figure is constant-currency. - **growth_pct**: "Eleven and change" means slightly above 11, so 11 is an approximation. The exact figure isn't given. - **guidance**: Guidance is withheld pending the Q4 close, so no guidance is stated, hence null.

输出 300 tok 延迟 3.5 s

schema 服从度(不臆造字段)、幻觉压力(guidance 明确被暂缓给出),以及结构化输出路径的差异。

提示词 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 检查 120 词,0 个禁用词

Claude Opus 5.5 通过 · 120 words, 0 banned, 1 question

Today we're launching Cross-Provider Prompt Caching for our API gateway. Why pay twice for the same context? Our gateway now stores repeated prompt prefixes, system instructions, and document chunks, then reuses them whether your request goes to OpenAI, Anthropic, Google, or an open-source model. Teams running retrieval pipelines or long agent loops can cut token costs by up to 60 percent and trim latency on every cached call. Setup takes one configuration flag; no code changes are required. Cache rules respect tenant boundaries, expire on your schedule, and log every hit for audit review. Dashboards show savings by provider, route, and application in real time. Cross-Provider Prompt Caching is available on all Pro and Enterprise plans. Turn it on today.

输出 1263 tok (+1007 思考) 延迟 13.5 s

Claude Sonnet 5.5 通过 · 122 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching** Your API gateway now caches prompts across every connected LLM provider, whether you route traffic to OpenAI, Anthropic, Google, or a self-hosted model. Here's how it works: the gateway fingerprints each prompt prefix, stores it centrally, and applies the right provider-specific caching mechanism automatically. When you switch providers or fail over during an outage, your cached context follows you. Early testing shows input token costs dropping by up to 70% on repeated system prompts and long documents, with latency improvements of 40% or more on cache hits. Tired of paying to send the same instructions thousands of times a day? Enable it with one configuration flag. Cache rules, TTLs, and per-team isolation are available in the dashboard today.

输出 266 tok 延迟 3.8 s

约束服从度(字数预算、禁用词表、唯一的那句问句)、文风指纹,以及长度控制。

只需一行代码即可在它们之间切换

两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点,相同的密钥,相同的请求结构。

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="claude-opus-5-5",
    # model="claude-sonnet-5-5",  # 取消注释此行,注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

获取 API 密钥 →

常见问题

Claude Opus 5.5 和 Claude Sonnet 5.5 哪个更便宜?

在 输入 / 1m tokens 方面,Claude Sonnet 5.5 更便宜($2 对比 $4,相差 2.0×)。其他行可能得出相反的结论——上方表格提供了完整信息,实际成本取决于你的组合使用情况。

我可以在不进行两次集成的情况下,对 Claude Opus 5.5 和 Claude Sonnet 5.5 进行 A/B 测试吗?

可以。两者均通过同一个兼容 OpenAI 的端点提供服务,并使用同一把 API 密钥——切换只需更改一行模型字符串,因此你可以将一部分流量路由到各个模型并直接比较账单。

Claude Opus 5.5 和 Claude Sonnet 5.5 支持提示词缓存吗?

是的 —— 两者对缓存读取的计费均低于其输入费率,因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。

相关对比

来自我们的实测研究