Alibaba
Qwen3.8 Flash NextTerbuka
Alibaba Qwen3.8 Flash Next experimental high-throughput multimodal preview model.
Inteligensi
66.8 #221
AAQI 61.5
Kecepatan output
168 t/s
TTFT 210 ms · ITL 6.0 ms/tok
Harga gabungan (3:1)
$0.19 /1M
Input $0.12/M · Output $0.4/M
Konteks
260K
≈ 650 halaman A4 · output maks 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Qwen3.8 Flash Next
Claude Mythos Preview (#1)
Average (68)
Sumbu radar diaproksimasi dari skor kategori jika pengukuran benchmark langsung tidak tersedia — lihat Metodologi.
Kelebihan & Trade-off
Di mana ia menang
- 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
Kasus Penggunaan Killer
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.
Quickstart Developer & API
Atur
DASHSCOPE_API_KEY dulu — dapatkan kunci APIcurl 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);Sumber Daya & Tautan Resmi
Deployment API
Bandingkan host & latensi
Throughput, TTFT, dan harga cache di Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock, dan Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Skor per sumber
Skor per kategori
Hasil benchmark
Spesifikasi Live & Arsitektur
Di balik kap
- Prompt caching supported — cached input reads at $0.02/1M tokens.
- Native multimodality: text, image, video in a single model.
Tanggal rilis
18 Agu 2026
Parameter
Not disclosed
Lisensi
Open Weights
Jendela konteks
260,000 tokens
Output maks
8,192 tokens
Modalitas
text + image + video
Harga cache
baca $0.02 · tulis $0.16 /1M
Penalaran
—
Harga input
$0.12/M
Harga output
$0.4/M
Kecepatan output
168 t/s
Waktu ke token pertama
210 ms
Data diperbarui
28 Agu 2026
Estimasi tagihan bulanan
Estimasi pengeluaran bulanan
$0.00
Menghitung penghematan vs GPT-4o…
Kutip & sematkan
Badge Markdown GitHub
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}
}Sematkan Lencana Skor
Tambahkan lencana Podium Score langsung ke README GitHub atau Hugging Face Anda — menautkan ke peringkat langsung.
Pratinjau
Markdown
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Model Alternatif
Alternatif terdekat untuk Qwen3.8 Flash Next berdasarkan kemampuan, harga, dan skor benchmark.