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Claude Fable 5 vs Qwen3.8 Max

vs

何时使用哪一个 — 经过整理的结论,而非基准测试表格

两者都接收文本和图像输入,返回文本,且都接近百万 token 大关(claude-fable-5 为 1000000,而 qwen3.8-max 为 983616),因此真正的区别在于价格表:claude-fable-5 的输入成本高出 5x($10 对比 $2),输出成本高出约 8.3x($50 对比 $6),缓存读取高出 4x($1 对比 $0.25)。当你想要其始终开启的思考功能——该功能无法禁用,因此会按该 $50 的输出费率收取推理费用——时,请选择 claude-fable-5;对于大批量的长上下文工作,请选择 qwen3.8-max,它具有略大的 131072 最大输出,对比 128000。

定价

Claude Fable 5 Qwen3.8 Max Δ
输入 / 1M tokens $10 $2
输出 / 1M tokens $50 $6 8.3×
缓存读取 / 1M tokens $1 $0.25
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 63 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

Claude Fable 5 Qwen3.8 Max
工具使用
思考控制 始终开启 是 —— 厂商未公布调节参数
结构化输出
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 Fable 5 Qwen3.8 Max
输入模态 文本 图像 文本 图像
输出模态 文本 文本
发布日期 2026-06-09 2026-08-03
知识截止日期 2026-01
上下文窗口 1M 984K
最大输出 128K 131K
思考参数 output_config.effort (thinking.type is adaptive-only and needs no configuration)
允许的值
effort
  • low
  • medium
  • high
  • xhigh
  • max

both "enabled" and "disabled" return 400

默认值

thinking always on (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 Fable 5 · Qwen3.8 Max

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 Fable 5 通过 · 3 sentences

When we enabled reasoning, the model began generating extensive internal "thinking" tokens before each answer, and those tokens are billed as output even though users never see them. Output tokens typically cost several times more than input tokens, so adding thousands of hidden reasoning tokens per request effectively multiplied our per-query cost. In short, we're paying for a much longer response behind the scenes in exchange for higher answer quality, and we can manage this by tuning reasoning effort levels or reserving reasoning mode for complex tasks only.

输出 228 tok 延迟 6.7 s

Qwen3.8 Max 通过 · 3 sentences

Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.

输出 378 tok (+305 思考) 延迟 8.6 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 Fable 5 通过 · 8/8 cases

## Fixed code ```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: cur = 1 best = max(best, cur) return best ``` ## Explanation The original function counts **adjacent equal pairs** rather than **items in a run**: a run of length *n* contains only *n − 1* equal neighbor pairs, so every result is off by one — a single-element list yields 0 instead of 1, and `[1, 2, 2]` yields 1 instead of 2 (which callers perceive as "missing" the run at the end). The fix is to initialize both `cur` and `best` to 1 (any nonempty list has a run of at least length 1), reset `cur` to 1 rather than 0 when

输出 531 tok 延迟 12.5 s

Qwen3.8 Max 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: cur = 1 best = max(best, cur) return best ``` The bug is that the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.

输出 1616 tok (+1411 思考) 延迟 34.7 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 Fable 5 通过 · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: "up eleven and change" is rendered as 11 (the precise figure isn't stated), and guidance is null because it was explicitly withheld.

输出 192 tok 延迟 6.5 s

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

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

输出 1199 tok (+1141 思考) 延迟 24.4 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 Fable 5 通过 · 120 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching for our API Gateway** Starting today, your repeated prompts are cached once and reused across OpenAI, Anthropic, Google, and any other provider you route through the gateway. Instead of paying full token costs every time your application sends the same system prompt or context block, the gateway detects matching prefixes and serves cached results in milliseconds. Why keep spending money and latency on identical requests? Early adopters report cost reductions of up to 60 percent on high-volume workloads, along with faster median response times. Configuration is simple: enable caching in your dashboard, set a TTL, and choose which routes participate. Cache entries are encrypted at rest and never shared between accounts. Available now on all plans.

输出 1173 tok 延迟 18.2 s

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

Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.

输出 2744 tok (+2591 思考) 延迟 46.3 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-fable-5",
    # model="qwen3.8-max",  # 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 Fable 5 和 Qwen3.8 Max 哪个更便宜?

在 输入 / 1m tokens 方面,Qwen3.8 Max 更便宜($2 对比 $10,相差 5.0×)。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 Fable 5 和 Qwen3.8 Max 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 Fable 5 和 Qwen3.8 Max 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