# Qwen: Qwen3 Coder Next — Use via Hanzo AI API

> Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE d... Access via Hanzo's OpenAI-compatible API. Context: 262K. Get started free.

[Models](https://hanzo.ai/models)/[Qwen](https://hanzo.ai/models/qwen)/qwen3-coder-next

# Qwen: Qwen3 Coder Next

[Qwen](https://hanzo.ai/models/qwen)

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...

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[Get API Key](https://hanzo.ai/signup)[View Docs](https://docs.hanzo.ai)[Try in Chat](https://hanzo.ai/chat)

## Specifications

Context Window

262K

Modalities

text

Input

$0.144 / M tokens

Output

$0.960 / M tokens

Status

available

Category

third-party

Model ID

qwen/qwen3-coder-next

## Quick Start

TypeScript

```
import OpenAI from 'openai' const client = new OpenAI({ apiKey: process.env.HANZO_API_KEY, baseURL: 'https://api.hanzo.ai/v1' }) const response = await client.chat.completions.create({ model: 'qwen/qwen3-coder-next', messages: [{ role: 'user', content: 'Hello!' }] }) console.log(response.choices[0].message.content)
```

Python

```
from openai import OpenAI client = OpenAI( api_key=os.environ["HANZO_API_KEY"], base_url="https://api.hanzo.ai/v1" ) response = client.chat.completions.create( model="qwen/qwen3-coder-next", messages=[{"role": "user", "content": "Hello!"}] ) print(response.choices[0].message.content)
```

cURL

```
curl https://api.hanzo.ai/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $HANZO_API_KEY" \ -d '{ "model": "qwen/qwen3-coder-next", "messages": [{"role": "user", "content": "Hello!"}] }'
```

Go

```
package main import ( "context" "fmt" "os" "github.com/sashabaranov/go-openai" ) func main() { cfg := openai.DefaultConfig(os.Getenv("HANZO_API_KEY")) cfg.BaseURL = "https://api.hanzo.ai/v1" client := openai.NewClientWithConfig(cfg) resp, _ := client.CreateChatCompletion(context.Background(), openai.ChatCompletionRequest{ Model: "qwen/qwen3-coder-next", Messages: []openai.ChatCompletionMessage{ {Role: openai.ChatMessageRoleUser, Content: "Hello!"}, }, }, ) fmt.Println(resp.Choices[0].Message.Content) }
```

## More from Qwen

[Qwen2.5 72B Instruct33KQwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...](https://hanzo.ai/models/qwen/qwen-2.5-72b-instruct)[Qwen: Qwen2.5 7B Instruct33KQwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...](https://hanzo.ai/models/qwen/qwen-2.5-7b-instruct)[Qwen2.5 Coder 32B Instruct33KQwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...](https://hanzo.ai/models/qwen/qwen-2.5-coder-32b-instruct)[Qwen: Qwen-Plus1MQwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.](https://hanzo.ai/models/qwen/qwen-plus)[Qwen: Qwen Plus 07281MQwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.](https://hanzo.ai/models/qwen/qwen-plus-2025-07-28)[Qwen: Qwen2.5 VL 72B Instruct128KQwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.](https://hanzo.ai/models/qwen/qwen2.5-vl-72b-instruct)

[View all Qwen models →](https://hanzo.ai/models/qwen)

## Use Qwen: Qwen3 Coder Next via Hanzo AI

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