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Claude Haiku 5.5 vs Kimi K2.7 Code

vs

什么时候选哪个

claude-haiku-5-5 更便宜,各项上限也都更大:输入 $0.1、输出 $0.5,kimi-k2.7-code 为 $0.95 和 $4,分别便宜 9.5x 和 8x,缓存读取为 $0.01 对 $0.19,上下文为 1000000 token 对 256000,输出上限为 128000 token 对 32768。它对超过 100000 token 的提示词按 5x 计费,即便如此仍然更便宜。kimi-k2.7-code 接受视频输入,思考无法关闭。看重成本和长输入时选 claude-haiku-5-5,想用 Moonshot 的编程模型时选 kimi-k2.7-code。

Benchmark 成绩

Claude Haiku 5.5:供应商没有公布 benchmark 成绩。

高于同类均值Kimi K2.7 Code1 / 5
Claude Haiku 5.5 Kimi K2.7 Code 其他参测模型 同类均值 ★ 没有模型得分更高
MLS-Bench-Lite
N/A
35.1%
MCP-Atlas
N/A
76%

供应商公布: Alibaba (Qwen) Moonshot OpenAI Z.ai

价格

Claude Haiku 5.5 Kimi K2.7 Code Δ
输入 / 1M token $0.1 $0.95 0.11×
输出 / 1M token $0.5 $4 0.13×
缓存读取 / 1M token $0.01 $0.19 0.053×
缓存写入 1.25x (5m) / 2x (1h) - -

价格取自构建时的实时目录,最新价格见各模型页面。

两个模型所处的位置:全部 78 个按同一单位计费的聊天模型的每 1M token 输入价分布(对数刻度)

Claude Haiku 5.5 · $0.1 Kimi K2.7 Code · $0.95
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

Claude Haiku 5.5 Kimi K2.7 Code
工具调用 是 是
思考控制 可配置 始终开启
结构化输出 是 -
提示词缓存 显式(由你标记前缀) 隐式(自动)
缓存有效期 5m default, 1h option 未公布
最小缓存前缀 1024 个 token 未公布

规格

Claude Haiku 5.5 Kimi K2.7 Code
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 2026-10-07 2026-06
知识截止日期 2026-06 -
上下文窗口 1M 256K
最大输出 128K 33K
思考参数 output_config.effort
  • thinking.type
  • thinking.keep
可选值
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
type
  • accepts only enabled
keep
  • accepts only all
默认值 medium thinking on with Preserved Thinking on

规格照录自各供应商的文档;供应商没有公布的项目,对应的行直接省略,不做推断。 完整来源: Claude Haiku 5.5 · Kimi K2.7 Code

同一条提示词,两个模型,经网关实测

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 Haiku 5.5 通过 · 3 sentences

Enabling reasoning makes the model generate a long internal chain of thought before its answer, and those thinking tokens are billed as output tokens, which cost several times more than input tokens. The model often produces many times more output per request than it did without reasoning, so the per-call cost rises even when the visible answer looks about the same length. Because that extra spend scales with every request, the bill roughly doubled, and we can control it by setting reasoning budgets, using reasoning only for tasks that need it, or routing simpler requests to a non-reasoning model.

输出 332 tok (+159 思考) 延迟 2.9 s

Kimi K2.7 Code 通过 · 3 sentences

Enabling reasoning mode causes the model to generate an internal chain-of-thought before producing each answer, and that hidden reasoning text is billed like any other output. Those extra reasoning tokens typically add up to much more than the final response itself, so every API call now costs significantly more than before. Because our overall usage volume did not change, the doubled bill reflects that each query consumes roughly twice as many billable tokens.

输出 259 tok (+174 思考) 延迟 5.4 s

指令遵循(恰好三句,数得出来)、受众适配(对 CFO 说话的口吻),以及下方 token 计数暴露出的隐藏思考计费差额。

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 Haiku 5.5 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 `cur` counts adjacent equal *pairs*, not run *elements*. A run of k equal items produces k−1 matching pairs, so the function returns one less than the true length, and a single-element input produces no pairs at all, so it returns 0 instead of 1. Resetting `cur` to 0 on a mismatch compounds this by starting every run one step behind. The fix starts both `cur` and `best` at 1 (a non-empty list always has a run of at least one element), resets `cur` to 1 on a mismatch, and updates `best` after every element rather than only inside the equal-branch. The empty-list guard keeps the function from returning 1 for `[]`. […]

输出 1012 tok (+591 思考) 延迟 6.0 s

Kimi K2.7 Code 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 ``` The bug is that the function counts *transitions* between equal neighbors instead of counting the items themselves, so a single-element list never enters the loop and returns `0` instead of `1`. Likewise, resetting `cur` to `0` on a mismatch forgets the current item that starts a new run, which causes runs that end at the last index to be undercounted. Initializing both `best` and `cur` to `1` and resetting `cur` to `1` fixes both issues.

输出 410 tok (+222 思考) 延迟 9.4 s

修复是否真的正确(能运行)、解释的信息密度,以及在一个范围明确的任务上的 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,严格符合 schema

Claude Haiku 5.5 通过 · 5/5 fields, guidance "Formal guidance withheld pending the Q4 "

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": 11, "guidance": "Formal guidance withheld pending the Q4 close" } ``` Notes: The text gives revenue as 412 but doesn't state the currency, so I left `revenue_usd_m` as null. Growth is "eleven and change," so 11 is an approximation.

输出 725 tok (+572 思考) 延迟 4.6 s

Kimi K2.7 Code 通过 · 5/5 fields, guidance "withheld pending the Q4 close"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld pending the Q4 close" } ```

输出 721 tok (+663 思考) 延迟 13.2 s

是否严守 schema(不臆造字段)、能否顶住幻觉压力(原文明说暂不给出 guidance),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 Haiku 5.5 通过 · 120 words, 0 banned, 1 question

**Introducing Prompt Cache Sync, the newest feature in GatewayOne.** Teams routing requests across OpenAI, Anthropic, and Google have often paid to process the same long system prompts again and again. Now the gateway stores those prompts once and reuses them wherever they appear, regardless of provider. Repeated context is matched by content hash, so cached prefixes return faster and cost less, with no changes to your application code. How much could this save your team? Enable Prompt Cache Sync from the dashboard, and your next billing report will show the difference. Cache entries follow your existing retention settings and can be purged per project at any time. Availability begins this week for all Scale and Enterprise plans worldwide. Start today.

输出 2164 tok (+1929 思考) 延迟 9.9 s

Kimi K2.7 Code 通过 · 120 words, 0 banned, 1 question

We're introducing Prompt Cache for our API Gateway, a new capability that stores prompt responses across multiple AI providers. By caching replies, you reduce redundant calls, lower latency, and cut costs without changing how you build. Teams using several model providers can now reuse identical prompt results instantly, keeping traffic consistent and budgets predictable. Responses are keyed by provider, model, and exact prompt, so you always get the right result. Configure TTL, hit thresholds, and eviction rules from a single dashboard. It fits into your existing routing and requires no code changes. Setup takes minutes and works with your current endpoints. Want to see how much latency and spend you can trim? Check the docs to enable Prompt Cache today.

输出 2375 tok (+2235 思考) 延迟 38.6 s

是否守住约束(字数预算、禁用词表、只能有一个问句)、文风特征,以及长度控制。

改一行代码就能在两个模型之间切换

下方每个标签页里都有两个模型 ID,高亮的那两行是唯一要改的地方。端点不变,API key 不变,请求结构也不变。

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="claude-haiku-5-5",
    # model="kimi-k2.7-code",  # 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 key →

常见问题

Claude Haiku 5.5 和 Kimi K2.7 Code 哪个更便宜?

按「输入 / 1M token」算,Claude Haiku 5.5 更便宜($0.1 对 $0.95,相差 9.5×)。其他计费项的结论可能相反,完整价格见上方表格,实际成本取决于你的用量构成。

不用分别集成两次,就能对 Claude Haiku 5.5 和 Kimi K2.7 Code 做 A/B 测试吗?

可以。两个模型走同一个 OpenAI 兼容端点,用同一个 API key,切换时只要改一行里的模型名,所以可以给两个模型各分一部分流量,直接对比账单。

Claude Haiku 5.5 和 Kimi K2.7 Code 支持提示词缓存吗?

支持。两个模型的缓存读取价都低于各自的输入价,所以前缀能反复命中缓存的负载,实际成本会比按官网价估算的低。具体的缓存读取价见上方价格表。

相关对比

我们的实测研究