DeepSeek V4 Flash (0731) vs Kimi K3
什麼情況選哪一個
兩者皆為具備約百萬 token context 的 text-out reasoning 模型 (deepseek-v4-flash-0731 為 1000000，kimi-k3 為 1048576)，並支援 chat、code 與 tools，因此差異在於成本與 input：deepseek-v4-flash-0731 執行 input 為 $0.44、output 為 $1.32，而 kimi-k3 的 input 收費約高出 6.8x ($3)，output 約高出 11x ($15)。當您需要 image 或 video input，或是高達 1048576 token 的單次回覆時請選擇 kimi-k3；處理高用量文字工作請選擇 deepseek-v4-flash-0731，其 393216 最大 output 與 $0.044 的 cache 讀取能保持低廉成本。
Benchmark 成績
兩邊都有成績的有 18 項。
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| DeepSeek V4 Flash (0731) | Kimi K3 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.44 | $3 | 0.15× |
| 輸出 / 1M tokens | $1.32 | $15 | 0.088× |
| 快取讀取 / 1M tokens | $0.044 | $0.3 | 0.15× |
| 快取寫入 | 不額外計費 | - | - |
費率取自網站建置時的即時目錄；各模型頁面都列有最新的價目。
兩者的相對位置：每 1M tokens 的輸入價格，涵蓋同一計費單位下全部 76 個聊天模型（對數尺度）
功能
| DeepSeek V4 Flash (0731) | Kimi K3 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是，但供應商未公布調整參數 | 一律開啟 |
| 結構化輸出 | 是 | 是 |
| 提示詞快取 | 隱式（自動） | 隱式（自動） |
| 快取存活時間 | no fixed TTL (evicted when unused) | 未公布 |
| 最小快取前綴 | 未公布 | 未公布 |
規格
| DeepSeek V4 Flash (0731) | Kimi K3 | |
|---|---|---|
| 輸入模態 | 文字 | 文字 圖像 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-07-31 | 2026-07-16 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 393K | 1M |
| 思考參數 | - | reasoning_effort (top-level; the thinking object is not accepted) |
| 可接受的值 | - | reasoning_effort
|
| 預設值 | - | max |
規格摘錄自各供應商的文件；供應商沒有公布的項目就直接略過，不自行推測。 完整來源： DeepSeek V4 Flash (0731) · Kimi K3
同一段提示詞，兩個模型，經閘道實測
DeepSeek V4 Flash (0731) 通過 · 3 sentences
Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.
輸出 365 tok (+266 思考) 延遲 5.1 s
Kimi K3 通過 · 3 sentences
Reasoning models don't just answer questions—they "think" first, generating long internal chains of step-by-step logic before producing a response. Those hidden thinking steps are billed as output tokens (the most expensive kind, typically 3–5x the price of input tokens), and a single query can generate thousands of them even when the visible answer is only a paragraph long. So you're paying for dramatically more compute per request: the bill doubled because the model does far more work behind the scenes, not because usage increased.
輸出 755 tok (+637 思考) 延遲 20.8 s
是否照指令做（剛好三句，數得出來）、語氣是否貼合對象（寫給 CFO 看），以及下方 token 計量顯示出的隱藏思考計費落差。
DeepSeek V4 Flash (0731) 通過 · 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 original bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]
輸出 1268 tok (+1016 思考) 延遲 14.0 s
Kimi K3 通過 · 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 a fencepost error: `cur` counts *matching adjacent pairs* rather than *items in the run*, because it starts at 0 and only increments when `xs[i] == xs[i - 1]`. A run of length L contains L−1 equal pairs, so every run was undercounted by exactly one. For a single-element list (a run of length 1) the loop never runs and the function returns 0 instead of 1; likewise any run ending at the last index — e.g. the two `2`s in `[1, 2, 2]` — reported 1 instead of 2, making it look like no run existed at all. […]
輸出 1837 tok (+1547 思考) 延遲 47.2 s
修正是否真的正確（跑得起來）、說明的資訊密度，以及在範圍明確的任務上的 token 效率。
DeepSeek V4 Flash (0731) 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }
輸出 1119 tok (+1072 思考) 延遲 12.3 s
Kimi K3 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
輸出 924 tok (+863 思考) 延遲 25.9 s
是否遵守 schema（不自創欄位）、面對幻覺誘因的表現（題目明確不給 guidance），以及結構化輸出路徑的差異。
DeepSeek V4 Flash (0731) 未通過 · 141 words, 0 banned, 1 question
**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]
輸出 254 tok (+80 思考) 延遲 4.4 s
Kimi K3 通過 · 120 words, 0 banned, 1 question
Meet Prompt Cache, a new API gateway capability that stores prompt responses and serves them across OpenAI, Anthropic, Google, and Azure endpoints. It matches requests by model, prompt hash, tools, temperature, and tenant policy, so repeated work returns fast while sensitive contexts stay isolated. Teams set TTLs, stale rules, encryption scopes, and bypass flags per route. Analytics show hit rate, latency saved, token spend avoided, and drift risk by provider. What changes for developers? Keep one integration, add cache headers, and watch fallback logic respect consent, residency, and audit needs. During rollout, canary keys compare fresh answers with cached copies before promotion. Prompt Cache cuts vendor calls, steadies p95 latency, and gives platform owners controls for cost, quality, and compliance.
輸出 1527 tok (+1354 思考) 延遲 37.9 s
是否遵守限制（字數上限、禁用詞清單、只能有一句問句）、文字風格的特徵，以及長度控制。
改一行程式碼就能在兩者之間切換
下面每個頁籤都列了這兩個模型 ID，要改的只有醒目標示的那兩行。端點、金鑰和請求格式都不變。
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="deepseek-v4-flash-0731",
# model="kimi-k3", # 取消這一行的註解，並把上一行註解掉
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "deepseek-v4-flash-0731",
// model: "kimi-k3", // 取消這一行的註解，並把上一行註解掉
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-flash-0731",
# "model": "kimi-k3", # 取消這一行的註解，並把上一行註解掉
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "deepseek-v4-flash-0731",
// Model: "kimi-k3", // 取消這一行的註解，並把上一行註解掉
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("deepseek-v4-flash-0731")
// .model("kimi-k3") // 取消這一行的註解，並把上一行註解掉
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
DeepSeek V4 Flash (0731) 和 Kimi K3 哪個比較便宜？
以「輸入 / 1M tokens」來看，DeepSeek V4 Flash (0731) 比較便宜（$0.44 對 $3，相差 6.8×）。其他項目的結果可能相反，完整價目請看上表；實際成本要看你的用量組合。
可以只串接一次，就對 DeepSeek V4 Flash (0731) 和 Kimi K3 做 A/B 測試嗎？
可以。兩個模型都走同一個 OpenAI 相容端點，用的也是同一把 API 金鑰，切換時只要改一行裡的模型名稱字串。你可以把一部分流量分別導到兩邊，再直接比較帳單。
DeepSeek V4 Flash (0731) 與 Kimi K3 支援提示詞快取嗎？
支援。兩者的快取讀取費率都低於輸入費率，所以前綴已經進快取的工作負載，實際成本會比官網價算出來的低。確切的快取讀取價格請見上方定價表。