Alibaba
Qwen3.8 Flash NextOpen
Alibaba Qwen3.8 Flash Next experimental high-throughput multimodal preview model.
Intelligence
66.8 #221
AAQI 61.5
Output Speed
168 t/s
TTFT 210 ms · ITL 6.0 ms/tok
Blended price (3:1)
$0.19 /1M
In $0.12/M · Out $0.4/M
Context
260K
≈ 650 A4 pages · max out 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Qwen3.8 Flash Next
Claude Mythos Preview (#1)
Average (68)
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- 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
Killer Use Cases
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.
Developer Quickstart & API
Set
DASHSCOPE_API_KEY first — get an API keycurl 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);Official Resources & Links
API deployment
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Scores by Source
Category Scores
Benchmark Results
Live Specs & Architecture
Under the hood
- Prompt caching supported — cached input reads at $0.02/1M tokens.
- Native multimodality: text, image, video in a single model.
Release date
Aug 18, 2026
Parameters
Not disclosed
License
Open Weights
Context Window
260,000 tokens
Max Output
8,192 tokens
Modalities
text + image + video
Cache Pricing
read $0.02 · write $0.16 /1M
Reasoning
—
Input Price
$0.12/M
Output Price
$0.4/M
Output Speed
168 t/s
Time to First Token
210 ms
Data Updated
Aug 28, 2026
Estimated monthly bill
Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...
Cite & embed
GitHub Markdown badge
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}
}Embed Score Badge
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