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Claude Sonnet 5.5 vs Qwen3.8 Flash

vs

何时用哪一个

两者均具备 1,000,000 token 的上下文窗口,因此区分点在于成本和输入:qwen3.8-flash 的输入为 $0.15,输出为 $0.47,而 claude-sonnet-5-5 为 $2 和 $10,大约便宜了 13x 和 21x,且前者的缓存读取为 $0.016,后者为 $0.2。对于大体量或长上下文的工作,在文本和图像之外需要视频输入时,或者当你需要完全关闭思考功能(claude-sonnet-5-5 不允许这样做)时,请选择 qwen3.8-flash。如果你需要 Anthropic 较新的 2026-09-28 版本来处理文本和图像任务,并能接受较高的费率,请选择 claude-sonnet-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Qwen3.8 Flash12 / 163 / 16
Claude Sonnet 5.5 Qwen3.8 Flash 其他被测模型 同侪均值 ★ 无人分数更高
SWE-Bench Pro
N/A
62.5%
OSWorld 2.0 partial
N/A
52.3%
JobBench
N/A
55.7%
GPQA Diamond
N/A
91.7%
ERQA
N/A
无人分数更高 72.3%
Agents' Last Exam Pass
N/A
24.3%
LVBench
N/A
76.6%

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

定价

Claude Sonnet 5.5 Qwen3.8 Flash Δ
输入 / 1M tokens $2 $0.15 13×
输出 / 1M tokens $10 $0.47 21×
缓存读取 / 1M tokens $0.2 $0.016 13×
缓存写入 1.25x (5m) / 2x (1h) 1.25x -

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 Sonnet 5.5 Qwen3.8 Flash
工具使用 是 是
思考控制 始终开启 可配置
结构化输出 是 是
提示词缓存 显式(由您标记前缀) 隐式 + 显式
缓存生存时间 5m default, 1h option explicit: 5m, reset on hit
最小缓存前缀 1024 个 token 1024 个 token

规格

Claude Sonnet 5.5 Qwen3.8 Flash
输入模态 文本 图像 文本 图像 视频
输出模态 文本 文本
发布日期 2026-09-28 2026-08-27
知识截止日期 2026-06 -
上下文窗口 1M 1M
最大输出 128K 131K
思考参数 thinking.type
  • enable_thinking
  • thinking_budget
  • preserve_thinking
允许的值
thinking.type
  • adaptive (default)
  • between_tools
enable_thinking
  • true
  • false
thinking_budget
  • in tokens
preserve_thinking
  • true
  • false
默认值 adaptive, effort high

on

qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, and preserve_thinking is off

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 5.5 · Qwen3.8 Flash

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 5.5 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

Qwen3.8 Flash 通过 · 3 sentences

Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.

输出 330 tok (+262 思考) 延迟 8.9 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 5.5 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

Qwen3.8 Flash 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.

输出 2123 tok (+1933 思考) 延迟 43.8 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 5.5 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

Qwen3.8 Flash 通过 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }

输出 519 tok (+463 思考) 延迟 10.5 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 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

Qwen3.8 Flash 通过 · 120 words, 0 banned, 1 question

We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.

输出 5958 tok (+5805 思考) 延迟 88.8 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-sonnet-5-5",
    # model="qwen3.8-flash",  # 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 5.5 和 Qwen3.8 Flash 哪个更便宜?

在 输入 / 1m tokens 方面,Qwen3.8 Flash 更便宜($0.15 对比 $2,相差 13×)。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 5.5 和 Qwen3.8 Flash 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 5.5 和 Qwen3.8 Flash 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