qwen/qwen3.5-397b-a17b
Date de publication: 2026-02-16
262,144 context · $0.60/M input tokens · $3.60/M output tokens
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers state-of-the-art performance comparable to leading-edge models across a wide range of tasks, including language understanding, logical reasoning, code generation, agent-based tasks, image understanding, video understanding, and graphical user interface (GUI) interactions. With its robust code-generation and agent capabilities, the model exhibits strong generalization across diverse agent.
Tarification
Paiement à l'usage
Aucun coût initial, payez uniquement ce que vous utilisez
Essayer le modèle
Utilisation de l'API
Utilisez les exemples de code suivants pour intégrer notre API :
import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 2,
});
try {
const response = await client.chat.completions.create({
model: 'qwen/qwen3.5-397b-a17b',
messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 2,
});
try {
const response = await client.chat.completions.create({
model: 'qwen/qwen3.5-397b-a17b',
messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}Introduction au modèle
Qwen qwen3.5-397b-a17b
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers state-of-the-art performance comparable to leading-edge models across a wide range of tasks, including language understanding, logical reasoning, code generation, agent-based tasks, image understanding, video understanding, and graphical user interface (GUI) interactions. With its robust code-generation and agent capabilities, the model exhibits strong generalization across diverse agent.
Why It Looks Great
- Large Language Model architecture for efficient processing
- 262144 context window for long document handling
- Competitive pricing at $0.4/$2.3 per million tokens
Key Features
- Context Window: 262144 tokens
- Max Output: 65536 tokens
- Vision: Supported
- Function Calling: Supported
Specifications
| Specification | Value |
|---|---|
| Provider | Qwen |
| Model Type | Large Language Model (LLM) |
| Architecture | N/A |
| Context Window | 262144 tokens |
| Max Output | 65536 tokens |
| Input | Text |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
Pricing
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $0.4 |
| Output | $2.3 |
How to Use
- Write your prompt — describe the task, provide context, and specify desired output format.
- Submit — the model processes your request and returns the response.
API Integration
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: qwen/qwen3.5-397b-a17b
API Usage
Python SDK
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="qwen/qwen3.5-397b-a17b",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)cURL
curl https://llm.wavespeed.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "qwen/qwen3.5-397b-a17b",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: qwen/qwen3.5-397b-a17b
- Provider: Qwen
Infos
Fonctionnalités prises en charge
Guide d'accès API
Qwen3.5 397b A17b API
qwen/qwen3.5-397b-a17b
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers state-of-the-art performance comparable to leading-edge models across a wide range of tasks, including language understanding, logical reasoning, code generation, agent-based tasks, image understanding, video understanding, and graphical user interface (GUI) interactions. With its robust code-generation and agent capabilities, the model exhibits strong generalization across diverse agent.
Entrée
$0.6 /M
Sortie
$3.6 /M
Contexte
262K
Sortie max.
66K
Vision
Pris en charge
Utilisation d'outils
Pris en charge
Essayez Qwen3.5 397b A17b sur WaveSpeedAI
Accédez à Qwen3.5 397b A17b via notre API unifiée — compatible OpenAI, sans démarrages à froid, prix transparents.
Questions fréquentes sur Qwen3.5 397b A17b
Combien coûte l'API Qwen3.5 397b A17b ?+
Tarification sur WaveSpeedAI : $0.60 par million de tokens d'entrée et $3.60 par million de tokens de sortie. Le prompt caching et le traitement par batch sont facturés séparément et réduisent le coût effectif sur les charges longues et répétitives.
Quelle est la fenêtre de contexte de Qwen3.5 397b A17b ?+
Qwen3.5 397b A17b prend en charge jusqu'à 262K tokens de contexte et jusqu'à 66K tokens de sortie par requête.
Qwen3.5 397b A17b est-il compatible avec OpenAI ?+
WaveSpeedAI expose Qwen3.5 397b A17b à https://llm.wavespeed.ai/v1 via l’interface Chat Completions compatible OpenAI. La plupart des clients du SDK OpenAI fonctionnent en changeant l’URL de base et la clé API ; les champs facultatifs dépendent du modèle.
Comment démarrer avec Qwen3.5 397b A17b ?+
Connectez-vous à WaveSpeedAI, créez une clé API dans Access Keys, puis envoyez une requête à https://llm.wavespeed.ai/v1/chat/completions avec l’id du modèle affiché ci-dessus. Consultez le catalogue actuel pour la disponibilité, les capacités et les tarifs.