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DeepSeek V4.1 Flash (1017) vs Kimi K2.7 Code

vs

什么时候选哪个

deepseek-v4.1-flash-1017 价格更低,上下文和输出空间也更大:输入和输出价格分别为 $0.3 和 $1.2,而 kimi-k2.7-code 为 $0.95 和 $4,分别约为前者的 3.2 倍和 3.3 倍;上下文为 1000000 个 token,后者为 256000 个 token;最多可输出 393216 个 token,后者为 32768 个 token;缓存读取价格为 $0.006,后者为 $0.19。如果需要视频输入,且可以接受无法关闭的推理,可选择 kimi-k2.7-code,这款 DeepSeek 模型不支持视频输入。对于长上下文的文本和图像任务、批量起草内容或大量复用缓存的处理流水线,2026-10-17 发布的这款 DeepSeek 模型是更经济的默认选择。

Benchmark 成绩

DeepSeek V4.1 Flash (1017):供应商没有公布 benchmark 成绩。

高于同类均值Kimi K2.7 Code1 / 5
DeepSeek V4.1 Flash (1017) 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

DeepSeek V4.1 Flash (1017) Kimi K2.7 Code Δ
输入 / 1M token $0.3 $0.95 0.32×
输出 / 1M token $1.2 $4 0.3×
缓存读取 / 1M token $0.006 $0.19 0.032×
缓存写入 不单独收费 - -

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 79 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)

能力

DeepSeek V4.1 Flash (1017) Kimi K2.7 Code
工具调用 是 是
思考控制 是,但供应商未公布调节参数 始终开启
提示词缓存 隐式(自动) 隐式(自动)
缓存有效期 no fixed TTL (evicted when unused) 未公布
最小缓存前缀 未公布 未公布

规格

DeepSeek V4.1 Flash (1017) 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 2026-10-17 2026-06
上下文窗口 1M 256K
最大输出 393K 33K
思考参数 -
  • thinking.type
  • thinking.keep
可选值 -
type
  • accepts only enabled
keep
  • accepts only all
默认值 - 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: DeepSeek V4.1 Flash (1017) · 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

DeepSeek V4.1 Flash (1017) 通过 · 3 sentences

Enabling reasoning caused each request to generate additional internal reasoning tokens, which are billed like output tokens and can be several times longer than the final answer. Those extra tokens raise cost per call, and longer responses plus occasional retries pushed total usage to roughly double. We can control this with reasoning-effort limits, token budgets, caching, and spend monitoring by use case.

输出 253 tok (+178 思考) 延迟 5.4 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

DeepSeek V4.1 Flash (1017) 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` starts at `0` and is incremented only when two adjacent items match, so it counts equal adjacent pairs rather than run length; a run of length `k` is recorded as `k - 1`. Consequently, a single-element input returns `0`, and a run ending at the last index—especially a one-element final run—is undercounted or never counted. The fix initializes every run to length `1`, increments on a match, resets to `1` on a mismatch, updates `best` each iteration, and handles the empty input separately.

输出 1300 tok (+1095 思考) 延迟 11.8 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

DeepSeek V4.1 Flash (1017) 通过 · 5/5 fields, guidance "withheld pending the Q4 close"

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

输出 1375 tok (+1337 思考) 延迟 12.2 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

DeepSeek V4.1 Flash (1017) 通过 · 120 words, 0 banned, 1 question

Today we’re introducing Cross-Provider Prompt Cache in the API gateway. It stores identical prompt requests once and serves cached responses across supported model providers, reducing duplicate calls, latency, and spend. Teams can set cache keys by model, temperature, system prompt, and custom metadata, then choose TTLs per route or workspace. The gateway handles provider fallback while keeping cached results consistent with your policy. Is your team paying twice for the same prompt? Cross-Provider Prompt Cache helps you answer that. Configure it in the gateway dashboard, inspect hit rates by provider, and invalidate entries on demand. Existing gateway routes need no code changes; enable the feature and define your caching rules. Cross-Provider Prompt Cache is available for all API gateway customers.

输出 2487 tok (+2335 思考) 延迟 17.1 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="deepseek-v4.1-flash-1017",
    # 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

DeepSeek V4.1 Flash (1017) 和 Kimi K2.7 Code 哪个更便宜?

按「输入 / 1M token」算,DeepSeek V4.1 Flash (1017) 更便宜($0.3 对 $0.95,相差 3.2×)。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 DeepSeek V4.1 Flash (1017) 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。

DeepSeek V4.1 Flash (1017) 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