qwen/qwen3.5-397b-a17b
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.
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Bez kosztów początkowych, płacisz tylko za to, czego używasz
Użyj poniższych przykładów kodu, aby zintegrować się z naszym API:
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)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.
| 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 |
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $0.4 |
| Output | $2.3 |
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: qwen/qwen3.5-397b-a17b
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 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!"}]
}'
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.
Wejście
$0.6 /M
Wyjście
$3.6 /M
Kontekst
262K
Maks. wyjście
66K
Vision
Obsługiwane
Użycie narzędzi
Obsługiwane
Uzyskaj dostęp do Qwen3.5 397b A17b przez nasze ujednolicone API — kompatybilne z OpenAI, bez zimnych startów, przejrzyste ceny.
Otwórz PlaygroundCennik na WaveSpeedAI: $0.60 za milion tokenów wejściowych i $3.60 za milion tokenów wyjściowych. Prompt caching i przetwarzanie wsadowe są rozliczane oddzielnie i obniżają efektywny koszt długich, powtarzalnych obciążeń.
Qwen3.5 397b A17b obsługuje do 262K tokenów kontekstu i do 66K tokenów wyjściowych na zapytanie.
Tak. WaveSpeedAI udostępnia Qwen3.5 397b A17b przez endpoint kompatybilny z OpenAI pod https://llm.wavespeed.ai/v1. Skieruj oficjalny OpenAI SDK na ten base URL ze swoim kluczem API WaveSpeedAI — bez innych zmian w kodzie.
Zaloguj się do WaveSpeedAI, utwórz klucz API w Access Keys, a następnie wyślij żądanie na https://llm.wavespeed.ai/v1/chat/completions z id modelu pokazanym powyżej. Nowe konta otrzymują darmowe kredyty na ocenę Qwen3.5 397b A17b.