Qwen3 Coder Flash

Qwen3 Coder Flash

latest
Model IDqwen3-coder-flash
Compare with other models

Lightweight, fast coding model optimized for speed.

Up to 1M Tokens ContextFast InferenceCodingFunction CallingMultilingual

Input <= 32k

Save 20%
$0.37$0.30/1M

input tokens

$1.88$1.50/1M

output tokens

Cached $0.04$0.03/1M cached tokens

32k < Input <= 128k

Save 20%
$0.63$0.50/1M

input tokens

$3.13$2.50/1M

output tokens

Cached $0.06$0.05/1M cached tokens

128k < Input <= 256k

Save 20%
$1.00$0.80/1M

input tokens

$5.00$4.00/1M

output tokens

Cached $0.10$0.08/1M cached tokens

256k < Input <= 1m

Save 20%
$2.00$1.60/1M

input tokens

$12.00$9.60/1M

output tokens

Cached $0.20$0.16/1M cached tokens

from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="Qwen/Qwen3-Coder-Flash", messages=[ { "role": "user", "content": "Write a Python function to calculate fibonacci sequence" } ], max_tokens=8962, temperature=0.1, top_p=1, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n")

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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