DeepSeek V4.1 Flash vs Qwen3.8 Flash
什么时候选哪个
两款模型均具有 1,000,000 token 的上下文窗口、文本加图像输入,以及相同的聊天、视觉、代码、推理和工具标志,因此区别在于成本和输出上限。qwen3.8-flash 的输入价格减半(每百万 $0.15 对比 $0.3),输出便宜约 2.6 倍($0.47 对比 $1.2),同样接受视频,带有一个长上下文标志并允许您关闭思考功能,这使其成为大批量或多模态摄取工作的默认选项。当您需要一条比 Qwen 的限制允许的更长的回复时,请选择 deepseek-v4.1-flash:其最大输出为 393,216 token,而后者为 131,072,空间约为 3 倍。
Benchmark 成绩
供应商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| DeepSeek V4.1 Flash | Qwen3.8 Flash | Δ | |
|---|---|---|---|
| 输入 / 1M token | $0.3 | $0.15 | 2× |
| 输出 / 1M token | $1.2 | $0.47 | 2.6× |
| 缓存读取 / 1M token | $0.006 | $0.016 | 0.38× |
| 缓存写入 | 不单独收费 | 1.25x | - |
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 80 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 V4.1 Flash | Qwen3.8 Flash | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 是,但供应商未公布调节参数 | 可配置 |
| 结构化输出 | - | 是 |
| 提示词缓存 | 隐式(自动) | 隐式 + 显式 |
| 缓存有效期 | no fixed TTL (evicted when unused) | explicit: 5m, reset on hit |
| 最小缓存前缀 | 未公布 | 1,024 个 token |
规格
| DeepSeek V4.1 Flash | Qwen3.8 Flash | |
|---|---|---|
| 输入模态 | 文本 图像 | 文本 图像 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-09-10 | 2026-08-27 |
| 上下文窗口 | 1M | 1M |
| 最大输出 | 393K | 131K |
| 思考参数 | - |
|
| 可选值 | - | enable_thinking
thinking_budget
preserve_thinking
|
| 默认值 | - | on qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, and preserve_thinking is off |
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 V4.1 Flash · Qwen3.8 Flash
同一条提示词,两个模型,经网关实测
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 Flash 通过 · 3 sentences
Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.
输出 330 tok (+262 思考) 延迟 8.9 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 V4.1 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 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 the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.
输出 2123 tok (+1933 思考) 延迟 43.8 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 V4.1 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 Flash 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
输出 519 tok (+463 思考) 延迟 10.5 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 V4.1 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 Flash 通过 · 120 words, 0 banned, 1 question
We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.
输出 5958 tok (+5805 思考) 延迟 88.8 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-v4.1-flash",
# model="qwen3.8-flash", # 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-flash", // 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-flash", # 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-flash", // 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-flash") // 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 Flash 哪个更便宜?
按「输入 / 1M token」算,Qwen3.8 Flash 更便宜($0.15 对 $0.3,相差 2.0×)。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 V4.1 Flash 和 Qwen3.8 Flash 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 V4.1 Flash 和 Qwen3.8 Flash 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。