DeepSeek V4 Flash (0731) vs MiniMax M3
何時該用哪一個 — 綜合評斷，而非基準測試數據表
deepseek-v4-flash-0731 與 minimax-m3 兩者皆為 text-out 模型，具備 1000000-token 的 context，且擁有近乎相同的 input 定價 (每百萬 $0.308 與 $0.3)，因此差異在於 output 與重複使用：minimax-m3 每百萬 output token 收費 $1.2，約為 deepseek-v4-flash-0731 的 $0.924 的 1.3x，且其 $0.06 的 cache 讀取費用約為 $0.0308 費率的 1.9x。在 output 與高度依賴 cache 的迴圈佔據大量帳單的場景中，若要處理高用量的 text chat、code 與 reasoning，請選擇 deepseek-v4-flash-0731。當您需要 image 或 video input、524288-token 的最大 output，或停用 thinking 的選項時，請選擇 minimax-m3。
定價
| DeepSeek V4 Flash (0731) | MiniMax M3 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.308 | $0.3 | 1× |
| 輸出 / 1M tokens | $0.924 | $1.2 | 0.77× |
| 快取讀取 / 1M tokens | $0.0308 | $0.06 | 0.51× |
| 快取寫入 | 不額外計費 | 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
| DeepSeek V4 Flash (0731) | MiniMax M3 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是 —— 廠商未公布調節參數 | 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 | no fixed TTL (evicted when unused) | 未公開 |
| 最小快取前綴 | 未公開 | 512 個 token |
規格
| DeepSeek V4 Flash (0731) | MiniMax M3 | |
|---|---|---|
| 輸入模態 | 文字 | 文字 影像 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-07-31 | 2026-06-01 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 393K | 524K |
| 思考參數 | — |
|
| 可接受的值 | — | thinking.type
reasoning_split
|
| 預設值 | — | 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： DeepSeek V4 Flash (0731) · MiniMax M3
單一提示詞，兩款模型 — 經由閘道測量
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
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。
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
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。
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
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。
DeepSeek V4 Flash (0731) 未命中 · 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
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="deepseek-v4-flash-0731",
# 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: "deepseek-v4-flash-0731",
// 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": "deepseek-v4-flash-0731",
# "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: "deepseek-v4-flash-0731",
// 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("deepseek-v4-flash-0731")
// .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
DeepSeek V4 Flash (0731) 和 MiniMax M3 哪個比較便宜？
MiniMax M3 在 輸入 / 1m tokens 上較便宜（$0.3 對比 $0.308，相差 1.0×）。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 DeepSeek V4 Flash (0731) 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。
DeepSeek V4 Flash (0731) 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。