qwen/qwen3-vl-8b-instruct
Date de publication: 2025-10-15
131,072 context · $0.08/M input tokens · $0.50/M output tokens
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...
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-vl-8b-instruct',
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-vl-8b-instruct',
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-vl-8b-instruct
**Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, **
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon temporal reasoning, DeepStack for fine-grained visual-text alignment, and text-timestamp alignment for precise event localization.
The model supports a native 256K-token context window, extensible to 1M tokens, and handles both static and dynamic media inputs for tasks like document parsing, visual question answering, spatial reasoning, and GUI control. It achieves text understanding comparable to leading LLMs while expanding OCR coverage to 32 languages and enhancing robustness under varied visual conditions.
Why It Looks Great
- Large Language Model architecture for efficient processing
- 131072 context window for long document handling
- Competitive pricing at $0.1/$0.5 per million tokens
Key Features
- Context Window: 131072 tokens
- Max Output: 32768 tokens
- Vision: Supported
- Function Calling: Supported
Specifications
| Specification | Value |
|---|---|
| Provider | Qwen |
| Model Type | Large Language Model (LLM) |
| Architecture | N/A |
| Context Window | 131072 tokens |
| Max Output | 32768 tokens |
| Input | Text |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
Pricing
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $0.1 |
| Output | $0.5 |
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-vl-8b-instruct
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-vl-8b-instruct",
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-vl-8b-instruct",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: qwen/qwen3-vl-8b-instruct
- Provider: Qwen
Infos
Fonctionnalités prises en charge
Guide d'accès API
Qwen3 Vl 8b Instruct API
qwen/qwen3-vl-8b-instruct
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...
Entrée
$0.08 /M
Sortie
$0.5 /M
Contexte
131K
Sortie max.
33K
Vision
Pris en charge
Utilisation d'outils
Pris en charge
Essayez Qwen3 Vl 8b Instruct sur WaveSpeedAI
Accédez à Qwen3 Vl 8b Instruct via notre API unifiée — compatible OpenAI, sans démarrages à froid, prix transparents.
Questions fréquentes sur Qwen3 Vl 8b Instruct
Combien coûte l'API Qwen3 Vl 8b Instruct ?+
Tarification sur WaveSpeedAI : $0.08 par million de tokens d'entrée et $0.50 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 Vl 8b Instruct ?+
Qwen3 Vl 8b Instruct prend en charge jusqu'à 131K tokens de contexte et jusqu'à 33K tokens de sortie par requête.
Qwen3 Vl 8b Instruct est-il compatible avec OpenAI ?+
WaveSpeedAI expose Qwen3 Vl 8b Instruct 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 Vl 8b Instruct ?+
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.