Alibaba
Qwen3.8 Flash Next开源
Alibaba Qwen3.8 Flash Next experimental high-throughput multimodal preview model.
智商指数
66.8 #221
AAQI 61.5
输出速度
168 t/s
TTFT 210 ms · ITL 6.0 ms/tok
混合价格 (3:1)
$0.19 /1M
输入 $0.12/M · 输出 $0.4/M
上下文
260K
≈ 650 页 A4 · 最大输出 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Qwen3.8 Flash Next
Claude Mythos Preview (#1)
Average (68)
缺少直接基准测量时,雷达图各轴由类别得分近似得出 — 详见 评分方法。
优势与取舍
优势所在
- Open weights — self-host, fine-tune and audit with no vendor lock-in
- 168 tok/s output — fast enough for real-time agents
- Sub-400ms time-to-first-token
- Very low blended price — $0.19/1M mixed tokens
杀手级用例
Real-time agents
Low-latency loops for voice, gaming and interactive copilots.
High-volume batch jobs
Classification, extraction and summarization at minimal cost per call.
Vision & document OCR
Extracts structure from scans, charts, screenshots and mixed-media inputs.
开发者快速上手与 API
请先设置
DASHSCOPE_API_KEY — 获取 API 密钥curl https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "qwen3-8-flash-next",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
api_key=os.environ["DASHSCOPE_API_KEY"],
)
resp = client.chat.completions.create(
model="qwen3-8-flash-next",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.DASHSCOPE_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "qwen3-8-flash-next",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);官方资源与链接
API 部署
对比托管商与延迟
Groq、Cerebras、Together、DeepInfra、Fireworks、Bedrock 与 Azure 的吞吐量、TTFT 和缓存定价。
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
各榜单得分
分类能力得分
基准测试分项
实时规格与架构
底层解析
- Prompt caching supported — cached input reads at $0.02/1M tokens.
- Native multimodality: text, image, video in a single model.
发布日期
2026年8月18日
参数量
Not disclosed
开源协议
Open Weights
上下文窗口
260,000 tokens
最大输出
8,192 tokens
模态
text + image + video
缓存价格
读 $0.02 · 写 $0.16 /1M
推理
—
输入价格
$0.12/M
输出价格
$0.4/M
输出速度
168 t/s
首Token延迟
210 ms
数据更新
2026年8月28日
每月账单预估
每月预估支出
$0.00
正在计算相对 GPT-4o 的节省额…
引用与嵌入
GitHub Markdown 徽章
Loading badge markdown...BibTeX
@misc{llmpodium2026qwen38flashnext,
title = {Benchmark Analysis and Intelligence Evaluation of Qwen3.8 Flash Next},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/qwen3-8-flash-next}
}嵌入评分徽章
将实时 Podium 评分徽章添加到 GitHub 或 Hugging Face README —— 链接回实时排名。
预览
Markdown
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替代模型推荐
基于能力、定价和基准测试得分与 Qwen3.8 Flash Next 最接近的替代模型。