DeepSeek V4 Pro (0813) vs Kimi K3
什么时候选哪个
两者都是支持文本输出的推理模型，具备聊天、代码和工具使用功能，且上下文大约为一百万 token，因此区别在于价格与输入类型的对比：deepseek-v4-pro-0813 每百万输入运行成本为 $1.32、输出为 $3.96，而 kimi-k3 为 $3 和 $15，前者在输入上大约便宜 2.3x，在输出上大约便宜 3.8x，缓存读取价格为 $0.132 对比 $0.3。当您需要图像或视频输入，或单次响应超过 393,216 token 并最高可达其 1,048,576 最大输出时，请选择 kimi-k3。大规模处理纯文本任务时请选择 deepseek-v4-pro-0813，并请注意 kimi-k3 无法关闭思考功能。
Benchmark 成绩
18 项两边都有成绩。
供应商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| DeepSeek V4 Pro (0813) | Kimi K3 | Δ | |
|---|---|---|---|
| 输入 / 1M token | $1.32 | $3 | 0.44× |
| 输出 / 1M token | $3.96 | $15 | 0.26× |
| 缓存读取 / 1M token | $0.132 | $0.3 | 0.44× |
| 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 76 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 Pro (0813) | Kimi K3 | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 始终开启 | 始终开启 |
| 结构化输出 | 是 | 是 |
| 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 Pro (0813) | Kimi K3 | |
|---|---|---|
| 输入模态 | 文本 | 文本 图像 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-08-13 | 2026-07-16 |
| 上下文窗口 | 1M | 1M |
| 最大输出 | 393K | 1M |
| 思考参数 | reasoning_effort | reasoning_effort (top-level; the thinking object is not accepted) |
| 可选值 | reasoning_effort
| reasoning_effort
|
| 默认值 | - | max |
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 Pro (0813) · Kimi K3
同一条提示词，两个模型，经网关实测
DeepSeek V4 Pro (0813) 通过 · 3 sentences
Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.
输出 308 tok (+226 思考) 延迟 5.1 s
Kimi K3 通过 · 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.
输出 755 tok (+637 思考) 延迟 20.8 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。
DeepSeek V4 Pro (0813) 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 `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.
输出 1315 tok (+1130 思考) 延迟 16.9 s
Kimi K3 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 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. […]
输出 1837 tok (+1547 思考) 延迟 47.2 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。
DeepSeek V4 Pro (0813) 通过 · 5/5 fields, guidance null
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}
输出 2155 tok (+2121 思考) 延迟 26.0 s
Kimi K3 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
输出 924 tok (+863 思考) 延迟 25.9 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。
DeepSeek V4 Pro (0813) 通过 · 121 words, 0 banned, 1 question
Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.
输出 2845 tok (+2694 思考) 延迟 25.5 s
Kimi K3 通过 · 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.
输出 1527 tok (+1354 思考) 延迟 37.9 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-pro-0813",
# model="kimi-k3", # 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)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-pro-0813",
// model: "kimi-k3", // 取消注释此行，注释上一行
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-pro-0813",
# "model": "kimi-k3", # 取消注释此行，注释上一行
"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-pro-0813",
// Model: "kimi-k3", // 取消注释此行，注释上一行
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-pro-0813")
// .model("kimi-k3") // 取消注释此行，注释上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常见问题
DeepSeek V4 Pro (0813) 和 Kimi K3 哪个更便宜？
按「输入 / 1M token」算，DeepSeek V4 Pro (0813) 更便宜（$1.32 对 $3，相差 2.3×）。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 Pro (0813) 和 Kimi K3 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 Pro (0813) 和 Kimi K3 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。