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Qwen3 Next 80B A3B Thinking
latestModel IDQwen/Qwen3-Next-80B-A3B-Thinking
Qwen3-Next-80B-A3B-Thinking is a next-generation foundation model from Alibaba's Qwen team featuring a revolutionary Hybrid Attention mechanism (Gated DeltaNet + Gated Attention) with High-Sparsity MoE architecture. With 80B total parameters and only 3.9B active per token, it delivers 10x higher throughput than Qwen3-32B on long contexts while outperforming Gemini-2.5-Flash-Thinking on multiple benchmarks.
256K Tokens ContextFast InferenceCodingFunction CallingMultilingual
Save 20%
$0.19$0.15
/ 1M input tokens
$1.5$1.2
/ 1M output tokens
Request (Non streaming)
from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="Qwen/Qwen3-Next-80B-A3B-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=8192,
temperature=0.6,
top_p=0.95,
stream=False
)
print(response.choices[0].message.content)from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="Qwen/Qwen3-Next-80B-A3B-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=8192,
temperature=0.6,
top_p=0.95,
stream=False
)
print(response.choices[0].message.content)Example response
A chat completion API provides a standard way to send conversational input and receive model-generated text in a single request. Key benefits include: • Interoperability: any client can use HTTP with JSON request and response bodies. • Flexibility: system prompts, user messages, and parameters such as temperature and max tokens are easy to configure. • Observability: responses typically include token usage fields for cost and performance tracking. A typical integration sends a POST request with the model name and messages array, then reads the assistant message from the first choice in the response.
Request (Non streaming)
from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="Qwen/Qwen3-Next-80B-A3B-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=8192,
temperature=0.6,
top_p=0.95,
stream=False
)
print(response.choices[0].message.content)from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="Qwen/Qwen3-Next-80B-A3B-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=8192,
temperature=0.6,
top_p=0.95,
stream=False
)
print(response.choices[0].message.content)Example response
A chat completion API provides a standard way to send conversational input and receive model-generated text in a single request. Key benefits include: • Interoperability: any client can use HTTP with JSON request and response bodies. • Flexibility: system prompts, user messages, and parameters such as temperature and max tokens are easy to configure. • Observability: responses typically include token usage fields for cost and performance tracking. A typical integration sends a POST request with the model name and messages array, then reads the assistant message from the first choice in the response.
Streaming supported • Function calling supported • See all examples in Playground Open in Playground for streaming, files, and all parameters.
Open in Playground