Qwen3 Coder Next

Chat Completions

qwen3-coder-next

Qwen|Created Feb 4, 2026|262.1k context

Chat Completions

Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per token, delivering performance comparable to models with 10 to 20x higher active compute, which makes it well suited for cost-sensitive, always-on agent deployment.

Overview Specifications Activity Performance Uptime Examples API Reference

Compare

Pricing-50%

Pay-as-you-go rates for this model. More details in the billing guide.

Input Tokens (1M)

$0.06

Cached Input Tokens (1M)

$0.04

Output Tokens (1M)

$0.40

Capabilities

Input Modalities

Text

Output Modalities

Text

Supported Parameters

Available parameters for API requests

Max Completion TokensPresence PenaltyResponse FormatTemperatureTool ChoiceToolsTop PFrequency PenaltyLogit BiasLogprobsStopReasoning Effort

Usage Analytics

Token usage of this model on our platform

Throughput

Not enough throughput data

Time-To-First-Token (TTFT)

Not enough TTFT data

Code Example

Example code for using this model through our API with Python (OpenAI SDK) or cURL. Replace placeholders with your API key and model ID.

Basic request example. Ensure API key permissions. For more details, see our documentation.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.naga.ac/v1",
    api_key="YOUR_API_KEY",
)

resp = client.chat.completions.create(
    model="qwen3-coder-next",
    messages=[
        {"role": "user", "content": "What's 2+2?"}
    ],
    temperature=0.2,
)
print(resp.choices[0].message.content)