Kimi K3 vs MiniMax M3
何時該用哪一個 — 綜合評斷，而非基準測試數據表
兩者都接受文字、影像和視訊輸入並回傳文字，雙方都有對話、程式碼、推理和工具使用，所以真正的差別是價格和輸出餘量：kimi-k3 的輸入貴 10 倍、輸出貴 12.5 倍、快取讀取貴 5 倍。需要它 1048576 token 的脈絡和同樣 1048576 token 的最大輸出，來一次性生成超長產物時選 kimi-k3，因為 minimax-m3 的輸出上限是 524288。對成本敏感的批量、它的長脈絡標誌，以及關閉思考的選項（kimi-k3 不允許）則選 minimax-m3。
定價
| Kimi K3 | MiniMax M3 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $3 | $0.3 | 10× |
| 輸出 / 1M tokens | $15 | $1.2 | 13× |
| 快取讀取 / 1M tokens | $0.3 | $0.06 | 5× |
| 快取寫入 | — | 不額外計費 | — |
費率取自建置時的即時目錄；各模型頁面皆附有目前的費率卡。
它們的相對位置 — 在此計費單位下，所有 63 個 聊天 模型的 每 1M tokens 的輸入價格（對數尺度）
能力
| Kimi K3 | MiniMax M3 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 常駐開啟 | 可配置 |
| 結構化輸出 | 是 | — |
| 提示快取 | 隱式（自動） | 隱式（自動） |
| 快取生命週期 | 未公開 | 未公開 |
| 最小快取前綴 | 未公開 | 512 個 token |
規格
| Kimi K3 | MiniMax M3 | |
|---|---|---|
| 輸入模態 | 文字 影像 影片 | 文字 影像 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | — | 2026-06-01 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 1M | 524K |
| 思考參數 | reasoning_effort (top-level; the thinking object is not accepted) |
|
| 可接受的值 | reasoning_effort
| thinking.type
reasoning_split
|
| 預設值 | max | adaptive: thinking on, with the model deciding when extra reasoning helps |
規格摘錄自各供應商的文件；供應商未發布的資料列會直接省略，而非自行推測。 完整來源： Kimi K3 · MiniMax M3
單一提示詞，兩款模型 — 經由閘道測量
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
MiniMax M3 通過 · 3 sentences
Reasoning-enabled models deliberately produce extensive internal "chain-of-thought" tokens before answering, often consuming 5–10x more tokens per query than a standard model. Because LLM providers bill per token for both that reasoning trace and the final response, your cost-per-query rises proportionally—and that multiplier is the primary driver of the doubled bill. The right response isn't to disable reasoning, but to route only genuinely complex queries to it while keeping routine traffic on the cheaper standard tier, which typically recovers 60–80% of the cost without sacrificing quality where it matters.
輸出 665 tok (+548 思考) 延遲 5.7 s
指令遵循（恰好三句，可數）、受眾適配（面向 CFO 的語氣），以及下方 token 計量所暴露的隱藏思考計費缺口。
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
MiniMax M3 通過 · 8/8 cases
The bug is in the loop bound: `range(1, len(xs))` iterates from index 1 up to but not including `len(xs) - 1`... wait, actually it goes up to `len(xs) - 1`, so the last index is included. The real problems are two: 1. **Starting `best` and `cur` at 0** and iterating from index 1 means a single-element list (`len(xs) == 1`) never enters the loop, so the function returns `0` instead of `1`. The fix is to either seed `best = cur = 1` when `xs` is non-empty, or include index 0 in the loop logic. 2. **Missing the final run**: the function only updates `best` *inside* the loop when it finds a continuation. If the longest run ends at the very last element, the loop body never gets to compare it after the increment — actually it does, but only if the run is longer than the previous `best`. […]
輸出 464 tok 延遲 7.9 s
修復是否真的正確（可執行）、解釋的資訊密度，以及在一個邊界明確的任務上的 token 效率。
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
MiniMax M3 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```
輸出 2212 tok (+2161 思考) 延遲 23.3 s
schema 服從度（不臆造欄位）、幻覺壓力（guidance 明確被暫緩給出），以及結構化輸出路徑的差異。
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
MiniMax M3 通過 · 120 words, 0 banned, 1 question
Introducing PromptCache, our new API gateway feature that caches prompts across multiple LLM providers. Developers can store prompt-completion pairs centrally, reducing redundant inference calls and lowering costs without sacrificing quality. How will your workflow change when repeated requests resolve instantly from a shared cache layer? The gateway intercepts outgoing requests, checks for matching prompt fingerprints, and serves previously generated completions when available, falling back to the original provider on misses. Compatible with OpenAI, Anthropic, Cohere, and custom endpoints, PromptCache integrates with existing routing rules and supports TTL, versioning, and per-tenant namespaces. Teams now gain predictable latency, reduced token spend, and improved throughput during traffic spikes today. […]
輸出 3256 tok (+2892 思考) 延遲 21.5 s
約束服從度（字數預算、禁用詞表、唯一的那句問句）、文風指紋，以及長度控制。
只需一行程式碼即可在兩者間切換
以下每個頁籤中都有這兩個 ID — 醒目提示的這兩行是唯一的修改處。相同的端點，相同的金鑰，相同的請求結構。
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="kimi-k3",
# model="minimax-m3", # 取消註解此行，並註解上一行
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: "kimi-k3",
// model: "minimax-m3", // 取消註解此行，並註解上一行
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": "kimi-k3",
# "model": "minimax-m3", # 取消註解此行，並註解上一行
"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: "kimi-k3",
// Model: "minimax-m3", // 取消註解此行，並註解上一行
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("kimi-k3")
// .model("minimax-m3") // 取消註解此行，並註解上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Kimi K3 和 MiniMax M3 哪個比較便宜？
MiniMax M3 在 輸入 / 1m tokens 上較便宜（$0.3 對比 $3，相差 10×）。其他項目可能呈現相反結果 — 上表提供完整資訊，實際成本取決於您的使用組合。
我可以在不進行兩次整合的情況下，對 Kimi K3 和 MiniMax M3 進行 A/B 測試嗎？
可以。兩者皆透過同一個相容 OpenAI 的端點提供服務，並使用同一把 API 金鑰 — 切換只需更改一行的模型字串，因此您可以將部分流量分別導向兩者並直接比較帳單。
Kimi K3 與 MiniMax M3 支援提示快取嗎？
是的 — 兩者的快取讀取費率皆低於其輸入費率，因此具有暖前綴的工作負載成本會低於牌價所示。確切的快取讀取列請見上方的定價表。