DeepSeek V4.1 Flash vs Qwen3.8 Max
何时用哪一个
这两者在事实上高度重合：都接受文本和图像输入，返回文本，且都涵盖聊天、视觉、代码、推理和工具，上下文窗口几乎相同，deepseek-v4.1-flash 为 1000000，qwen3.8-max 为 983616。区别在于成本和输出空间——deepseek-v4.1-flash 的输入和输出价格分别为 $0.3 和 $1.2，对比后者的 $2 和 $6，便宜了约 6.7x 和 5x，缓存读取价格为 $0.03 对比 $0.25，并允许 393216 个最大输出 tokens 而不是 131072。单看这些事实，除了继续使用您已经在运行的模型外，没有任何理由偏好 qwen3.8-max；对于新工作，deepseek-v4.1-flash 以更低的成本覆盖了相同的形态。
Benchmark 成绩
双方都被测过的 10 项。
厂商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定价
| DeepSeek V4.1 Flash | Qwen3.8 Max | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $0.3 | $2 | 0.15× |
| 输出 / 1M tokens | $1.2 | $6 | 0.2× |
| 缓存读取 / 1M tokens | $0.03 | $0.25 | 0.12× |
| 缓存写入 | 不单独收费 | 1.25x | - |
费率取自构建时的实时目录；每个模型页面均附有当前的费率卡。
它们的位置 — 以该计费单位计费的所有 69 个 聊天 模型的 每 1M token 的输入价格（对数刻度）
能力
| DeepSeek V4.1 Flash | Qwen3.8 Max | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是 —— 厂商未公布调节参数 | 是 —— 厂商未公布调节参数 |
| 结构化输出 | - | 是 |
| 提示词缓存 | 隐式（自动） | 隐式 + 显式 |
| 缓存生存时间 | no fixed TTL (evicted when unused) | explicit: 5m, reset on hit |
| 最小缓存前缀 | 未公开 | 1024 个 token |
规格
| DeepSeek V4.1 Flash | Qwen3.8 Max | |
|---|---|---|
| 输入模态 | 文本 图像 | 文本 图像 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-09-10 | 2026-08-03 |
| 上下文窗口 | 1M | 984K |
| 最大输出 | 393K | 131K |
规格转录自各供应商的文档；若供应商未发布某项数据，则直接省略该行，而非进行推断。 完整来源： DeepSeek V4.1 Flash · Qwen3.8 Max
单个 Prompt，两个模型 —— 通过网关实测
DeepSeek V4.1 Flash 通过 · 3 sentences
Enabling reasoning changed the unit economics: the model now spends extra billable tokens internally to “think” before producing the answer, and those reasoning tokens are charged like output tokens even though users never see them. That added token generation—plus longer contexts and occasional retries on harder prompts—roughly doubled our cost per request. We can control this by capping reasoning depth, routing only complex queries to reasoning models, and caching common prompts to bring the bill back down.
输出 423 tok (+327 思考) 延迟 14.6 s
Qwen3.8 Max 通过 · 3 sentences
Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.
输出 378 tok (+305 思考) 延迟 8.6 s
指令遵循（恰好三句，可数）、受众适配（面向 CFO 的语气），以及下方 token 计量所暴露的隐藏思考计费缺口。
DeepSeek V4.1 Flash 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` is counting equal adjacent pairs, not elements in the current run: every time `xs[i] == xs[i - 1]`, the run length increases by one element, but the first element of the run was never counted, so all runs are undercounted by one. Also, `best` starts at `0`, so single-element inputs return `0` instead of `1`, and a one-element run ending at the last index is missed entirely.
输出 918 tok (+733 思考) 延迟 11.4 s
Qwen3.8 Max 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 the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.
输出 1616 tok (+1411 思考) 延迟 34.7 s
修复是否真的正确（可运行）、解释的信息密度，以及在一个边界明确的任务上的 token 效率。
DeepSeek V4.1 Flash 通过 · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":"withheld pending the Q4 close"}
输出 1707 tok (+1667 思考) 延迟 15.1 s
Qwen3.8 Max 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
输出 1199 tok (+1141 思考) 延迟 24.4 s
schema 服从度（不臆造字段）、幻觉压力（guidance 明确被暂缓给出），以及结构化输出路径的差异。
DeepSeek V4.1 Flash 通过 · 116 words, 0 banned, 1 question
Today we're launching Cross-Provider Prompt Cache for our API gateway. It stores identical prompt requests and their responses across supported model providers, then serves cached results when a match is found. Teams can cut duplicate inference costs, reduce latency, and keep behavior consistent during provider failover. The cache works with configurable TTLs, per-route rules, and cache-key controls, so you decide what is reusable and what must stay fresh. Does your application send the same prompts to multiple providers? Now your gateway can answer many of those calls without another upstream request. Existing observability dashboards show hit rates, saved tokens, and estimated spend reduction. Enable it in the gateway console, set your policy, and start caching today.
输出 917 tok (+770 思考) 延迟 9.0 s
Qwen3.8 Max 通过 · 120 words, 0 banned, 1 question
Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.
输出 2744 tok (+2591 思考) 延迟 46.3 s
约束服从度（字数预算、禁用词表、唯一的那句问句）、文风指纹，以及长度控制。
只需一行代码即可在它们之间切换
两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点，相同的密钥，相同的请求结构。
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",
# model="qwen3.8-max", # 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-v4.1-flash",
// model: "qwen3.8-max", // 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-v4.1-flash",
# "model": "qwen3.8-max", # 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-v4.1-flash",
// Model: "qwen3.8-max", // 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-v4.1-flash")
// .model("qwen3.8-max") // 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 V4.1 Flash 和 Qwen3.8 Max 哪个更便宜？
在 输入 / 1m tokens 方面，DeepSeek V4.1 Flash 更便宜（$0.3 对比 $2，相差 6.7×）。其他行可能得出相反的结论——上方表格提供了完整信息，实际成本取决于你的组合使用情况。
我可以在不进行两次集成的情况下，对 DeepSeek V4.1 Flash 和 Qwen3.8 Max 进行 A/B 测试吗？
可以。两者均通过同一个兼容 OpenAI 的端点提供服务，并使用同一把 API 密钥——切换只需更改一行模型字符串，因此你可以将一部分流量路由到各个模型并直接比较账单。
DeepSeek V4.1 Flash 和 Qwen3.8 Max 支持提示词缓存吗？
是的 —— 两者对缓存读取的计费均低于其输入费率，因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。