Alibaba
Qwen3.8 Flash NextAperto
Alibaba Qwen3.8 Flash Next experimental high-throughput multimodal preview model.
Intelligenza
66.8 #221
AAQI 61.5
Velocità Uscita
168 t/s
TTFT 210 ms · ITL 6.0 ms/tok
Prezzo combinato (3:1)
$0.19 /1M
Input $0.12/M · Output $0.4/M
Contesto
260K
≈ 650 pagine A4 · output max 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Qwen3.8 Flash Next
Claude Mythos Preview (#1)
Average (68)
Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.
Punti di forza e compromessi
Dove vince
- 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
Casi d'uso 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 per sviluppatori e API
Imposta prima
DASHSCOPE_API_KEY — ottieni una chiave 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);Risorse e link ufficiali
Deployment API
Confronta host e latenza
Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Punteggi per Fonte
Punteggi per Categoria
Risultati dei Benchmark
Specifiche live e architettura
Sotto il cofano
- Prompt caching supported — cached input reads at $0.02/1M tokens.
- Native multimodality: text, image, video in a single model.
Data di Rilascio
18 ago 2026
Parametri
Not disclosed
Licenza
Open Weights
Finestra di Contesto
260,000 tokens
Output max
8,192 tokens
Modalità
text + image + video
Prezzi cache
lettura $0.02 · scrittura $0.16 /1M
Ragionamento
—
Prezzo Ingresso
$0.12/M
Prezzo Uscita
$0.4/M
Velocità Uscita
168 t/s
Tempo al Primo Token
210 ms
Dati aggiornati
28 ago 2026
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
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}
}Incorpora il badge del punteggio
Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.
Anteprima
Markdown
Loading badge snippet...HTML
Loading badge snippet...Altri modelli di Alibaba
Modelli alternativi
Migliori alternative a Qwen3.8 Flash Next in base a capacità, prezzi e benchmark.