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Claude Sonnet 5 vs GPT-5.6

vs

何时使用哪一个 — 经过整理的结论,而非基准测试表格

这两者形态接近——都接受文本和图像输入、返回文本、最大输出 128000 token,上下文也大致相同(claude-sonnet-5 为 1000000,gpt-5.6 为 1050000),思考都可关闭——所以真正的差别是价目表。claude-sonnet-5 每百万输入 $2、输出 $10,对比 gpt-5.6 的 $5 和 $30,即 OpenAI 这款输入贵 2.5 倍、输出贵 3 倍,缓存读取 $0.5 对 $0.2。大批量对话、代码和工具工作选 claude-sonnet-5;专门想用 OpenAI 的技术栈,或想要它略晚的 2026 年 2 月知识截止时选 gpt-5.6。

定价

Claude Sonnet 5 GPT-5.6 Δ
输入 / 1M tokens $2 $5 0.4×
输出 / 1M tokens $10 $30 0.33×
缓存读取 / 1M tokens $0.2 $0.5 0.4×
缓存写入 1.25x (5m) / 2x (1h) 不单独收费

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 63 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

Claude Sonnet 5 · $2 GPT-5.6 · $5
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

Claude Sonnet 5 GPT-5.6
工具使用
思考控制 可配置 可配置
结构化输出
提示词缓存 显式(由您标记前缀) 隐式(自动)
缓存生存时间 5m default, 1h option 5–10m, up to 1h
最小缓存前缀 1024 个 token 1024 个 token

规格

Claude Sonnet 5 GPT-5.6
输入模态 文本 图像 文本 图像
输出模态 文本 文本
发布日期 2026-06-30 2026-07-09
知识截止日期 2026-01 2026-02
上下文窗口 1M 1.1M
最大输出 128K 128K
思考参数
  • thinking.type "adaptive"
  • output_config.effort
reasoning.effort
允许的值
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max

"enabled" returns 400

reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
默认值

thinking on (adaptive)

effort
  • high
medium

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 Sonnet 5 · GPT-5.6

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 Sonnet 5 通过 · 3 sentences

Reasoning mode works by having the model generate extended internal "thinking" steps before producing its final answer, and since we're billed on total tokens, that hidden reasoning text adds significantly to what we pay for—even though you never see it in the output. This is different from simply getting longer visible responses; the model is essentially "showing its work" behind the scenes, and complex prompts can trigger thousands of extra reasoning tokens per request. The upside is improved accuracy on complex tasks, but if we want to control costs, we should selectively enable reasoning only for queries that truly need it, rather than applying it universally.

输出 194 tok 延迟 4.3 s

GPT-5.6 通过 · 3 sentences

Enabling reasoning makes the model generate many additional internal tokens before producing an answer, so each request consumes substantially more compute. Those reasoning tokens are billed even though they are not visible to users, and longer processing can also increase tool calls, retries, and output length. The bill doubled because our cost per request rose, not necessarily because usage volume increased; we can control it by limiting reasoning effort and routing simpler tasks to cheaper models.

输出 126 tok (+30 思考) 延迟 3.0 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 Sonnet 5 通过 · 8/8 cases

## The Bug The function has two related off-by-one issues rooted in the same cause: `cur` and `best` are initialized to `0` instead of `1`, treating run lengths as the number of *matches* between adjacent elements rather than the number of *elements* in the run. This means a single-element input never enters the loop (since `range(1, 1)` is empty) and returns `0` instead of the correct answer of `1`. It also means that for any input, the run length is undercounted by exactly one — a run of 3 equal elements only produces 2 "match" increments, so `best` ends up as 2 instead of 3. The claim that it "misses runs that end at the last index" is actually a symptom of this same undercount: the logic does update `best` correctly on every iteration (including the last), but because it's counting matches instead of elements, the final reported value is always one less than the true longest run. […]

输出 536 tok 延迟 7.1 s

GPT-5.6 通过 · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: best = max(best, cur) cur = 1 return max(best, cur) ``` The original code counted equal *transitions* rather than items, so a run of one had length `0`, a run of three had length `2`, and tracking from zero caused the off-by-one error. The corrected version starts each run at length `1`, resets to `1` when the value changes, and performs a final `max` after the loop so a run ending at the last element is included; an empty input still returns `0`.

输出 277 tok (+93 思考) 延迟 4.2 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 Sonnet 5 通过 · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11.5, "guidance": null } ```

输出 348 tok 延迟 5.3 s

GPT-5.6 通过 · 5/5 fields, guidance "withheld pending the Q4 close"

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld pending the Q4 close" }

输出 179 tok (+118 思考) 延迟 3.7 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 Sonnet 5 通过 · 123 words, 0 banned, 1 question

## Introducing Cross-Provider Prompt Caching We're rolling out a new caching layer for our API gateway that works across every major LLM provider you connect through it. Instead of resending identical prompts and paying full price each time, the gateway recognizes repeated requests and serves cached responses in milliseconds—regardless of whether you're routing to OpenAI, Anthropic, or Google. What does this mean for your bill and your latency? Both drop, often significantly, especially for teams running high-volume, repetitive workloads like customer support bots or batch content generation. The cache is configurable per route, with adjustable TTLs and invalidation rules, so you stay in control of freshness versus cost. Available now for all Pro and Enterprise plans. Check your dashboard to enable it today.

输出 259 tok 延迟 4.8 s

GPT-5.6 通过 · 120 words, 0 banned, 1 question

Today we are introducing Prompt Cache, a new API gateway feature that stores reusable prompt prefixes and serves them across supported model providers. By recognizing identical system instructions, tools, examples, and context blocks, Prompt Cache reduces repeated input processing, lowers latency, and helps control token costs without changing application code. Teams can configure retention windows, cache keys, provider routing, encryption, and regional storage policies from one dashboard or API. What happens when a preferred provider is unavailable? The gateway can route requests to another provider while reusing eligible cached content, preserving performance and consistency. Built-in metrics report hit rates, savings, latency, and provider usage, while audit logs support governance. Prompt Cache is available today in public preview for all customers.

输出 628 tok (+473 思考) 延迟 7.3 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-sonnet-5",
    # model="gpt-5.6",  # 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 Sonnet 5 和 GPT-5.6 哪个更便宜?

在 输入 / 1m tokens 方面,Claude Sonnet 5 更便宜($2 对比 $5,相差 2.5×)。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 Sonnet 5 和 GPT-5.6 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 Sonnet 5 和 GPT-5.6 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