What does "Next" indicate in the model name?

The "Next" suffix designates this as a forward-tracking model identifier. It resolves to Alibaba Cloud's rolling Qwen3-Coder checkpoint rather than a single fixed snapshot. As Alibaba Cloud releases improved versions, this identifier updates to point at them.

Will my application behave differently after an underlying model update?

Potentially yes. "Next" identifiers trade reproducibility for recency. If consistent behavior is required, for testing, compliance, or quality assurance, pin your application to a specific versioned model identifier instead.

What are the core coding capabilities shared with the rest of the Qwen3-Coder family?

Core capabilities include multi-language code generation, multi-step agentic task execution, tool calling, and extended context handling. See https://aws.amazon.com/about-aws/whats-new/2026/02/amazon-bedrock-adds-support-six-open-weights-models/ for the full capability overview.

How large is the context window for Qwen3 Coder Next?

The context window is 256K tokens. The Qwen3-Coder architecture natively supports long contexts, with YaRN extrapolation available to extend beyond native limits.

Is there a latency or throughput difference between this and pinned Qwen3-Coder versions?

This depends on which specific checkpoint "Next" points to at any given time. Live metrics are shown on this page and reflect real-time traffic conditions.

Can Qwen3 Coder Next be used for browser-use and tool-use agentic benchmarks?

The Qwen3-Coder family achieved strong reported results on open models for Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use. This variant, tracking the rolling Qwen3-Coder line, should carry those agentic characteristics forward.

What should I do if I need reproducible outputs from this model?

Switch to a versioned model ID for that workload. You can use AI Gateway to access both: the "Next" identifier for ongoing development and exploration, and a pinned version for the production component that requires stability.

Alibaba Cloud

Qwen3 Coder Next

Qwen3 Coder Next is the forward-looking iteration of Alibaba Cloud's agentic coding model line, designed for developers who want rolling updates to code generation and software engineering capabilities as Alibaba Cloud publishes new Qwen3-Coder checkpoints.

View API reference

Input and output price: Input $0.50, Output $1.20, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'alibaba/qwen3-coder-next',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out Qwen3 Coder Next by Alibaba Cloud. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

Qwen3 Coder Next

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

Bedrock

Legal:Terms•Privacy

256K

0.2 s

82 tps

$0.50/M

$1.20/M

—

07/22/2025

Getting started

Call Qwen3 Coder Next through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-coder-next',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Qwen3 Coder Next request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-coder-next',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
    temperature: 0.5,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `alibaba/qwen3-coder-next`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Qwen3 Coder Next supports up to `256,000` output tokens.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 256K-token context window

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK alibaba provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-coder-next',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['bedrock'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-coder-next',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by Alibaba Cloud

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

alibaba/qwen3.8-max-0902

991K

4.0 s

50 tps

$2/M

$6/M

Read$0.25/M

Write$2.50/M

—

09/01/2026

alibaba/qwen3.8-flash

991K

1.1 s

96 tps

$0.16/M

$0.47/M

Read$0.02/M

Write$0.20/M

—

08/26/2026

alibaba/qwen3.8-27b

0.6 s

133 tps

$0.10/M

$0.40/M

Read$0.01/M

Write$0.63/M

—

08/14/2026

alibaba/qwen3.8-max

2.5 s

160 tps

$2/M

$6/M

Read$0.25/M

Write$2.50/M

—

08/02/2026

alibaba/qwen3.7-flash

991K

2.5 s

140 tps

$0.03/M+2 more

$0.13/M+2 more

Read$0.006/M

Write$0.04/M

—

07/28/2026

alibaba/qwen3.7-plus

2.0 s

58 tps

$0.40/M+1 more

$1.60/M+1 more

Read$0.08/M

Write$0.50/M

—

06/02/2026

Copy link to headingAbout Qwen3 Coder Next

Qwen3 Coder Next is the forward-tracking identifier in the Qwen3-Coder model series: it resolves to whichever checkpoint Alibaba Cloud assigns to the rolling Qwen3-Coder line. Rather than referencing a fixed model version, it tracks forward progress as the team continues refining the model's coding, reasoning, and agentic capabilities.

The Qwen3-Coder family was built on the recognition that production software engineering requires more than code completion. It requires models that can plan across multi-step tasks, use tools, interpret execution results, and maintain coherence across extended sessions. The "Next" designation points at the rolling checkpoint for that line.

For developers building applications that need to follow ongoing improvements in open agentic coding capability, automated software development tools, AI pair programmers, or repository-level automation, Qwen3 Coder Next is the identifier that tracks those improvements without requiring code changes when underlying model weights are updated. The tradeoff is that behavior may shift between underlying versions; applications that need strict reproducibility should pin to a versioned model identifier instead.

The core capabilities of the Qwen3-Coder line (deep code understanding, multi-turn agentic task execution, tool calling, and long context support) carry through to this variant.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: Because this model tracks a moving development line, provider availability may shift as new checkpoints are deployed. AI Gateway's failover mechanism helps maintain continuity during transitions.
Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use Qwen3 Coder Next

Best for

Applications where recency of capability matters: For teams who want their coding AI to automatically pick up new Qwen3-Coder checkpoints as Alibaba Cloud releases them, this identifier avoids manual version updates
Exploratory and research-oriented coding tools: When you're building and iterating rapidly and want whatever checkpoint this identifier resolves to at each stage of development rather than maintaining a pinned version
Agentic software engineering workflows: The Qwen3-Coder foundation (long-horizon agent RL, tool calling, plan-execute-debug loops) applies to this variant, making it suitable for multi-step coding automation
Prototyping new coding AI capabilities: Testing what each new checkpoint can do before deciding whether to pin a specific version for production is a natural use of a "next" model identifier

Consider alternatives when

Reproducibility is required: "Next" model identifiers may point to different underlying checkpoints over time. For production systems where consistent behavior is a compliance or quality requirement, use a specific versioned model ID
You need guaranteed stability across deployments: Applications in production where behavior consistency is critical (code review systems, automated test generation with known expectations) should pin to a fixed model version
Cost predictability is important: As model versions update, performance characteristics including speed and token usage may change. Fixed model versions are easier to budget around

Copy link to headingConclusion

Qwen3 Coder Next gives development teams a stable API integration that tracks ongoing work in Alibaba Cloud's agentic coding line. The practical advantage is receiving checkpoint updates without migration effort; the practical caution is that behavior isn't frozen. For most exploratory and rapid-iteration use cases, this tradeoff is favorable. AI Gateway adds reliability across Bedrock on top of that access.

Copy link to headingFrequently Asked Questions

What does "Next" indicate in the model name?
The "Next" suffix designates this as a forward-tracking model identifier. It resolves to Alibaba Cloud's rolling Qwen3-Coder checkpoint rather than a single fixed snapshot. As Alibaba Cloud releases improved versions, this identifier updates to point at them.
Will my application behave differently after an underlying model update?
Potentially yes. "Next" identifiers trade reproducibility for recency. If consistent behavior is required, for testing, compliance, or quality assurance, pin your application to a specific versioned model identifier instead.
What are the core coding capabilities shared with the rest of the Qwen3-Coder family?
Core capabilities include multi-language code generation, multi-step agentic task execution, tool calling, and extended context handling. See https://aws.amazon.com/about-aws/whats-new/2026/02/amazon-bedrock-adds-support-six-open-weights-models/ for the full capability overview.
How large is the context window for Qwen3 Coder Next?
The context window is 256K tokens. The Qwen3-Coder architecture natively supports long contexts, with YaRN extrapolation available to extend beyond native limits.
Is there a latency or throughput difference between this and pinned Qwen3-Coder versions?
This depends on which specific checkpoint "Next" points to at any given time. Live metrics are shown on this page and reflect real-time traffic conditions.
Can Qwen3 Coder Next be used for browser-use and tool-use agentic benchmarks?
The Qwen3-Coder family achieved strong reported results on open models for Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use. This variant, tracking the rolling Qwen3-Coder line, should carry those agentic characteristics forward.
What should I do if I need reproducible outputs from this model?
Switch to a versioned model ID for that workload. You can use AI Gateway to access both: the "Next" identifier for ongoing development and exploration, and a pinned version for the production component that requires stability.

Your use is subject to Alibaba Cloud's Terms & Privacy Policies.

Agent Stack

Core Platform

Tools

Learn

Build

Explore

Qwen3 Coder Next

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Tool calling

Copy link to headingMore models by Alibaba Cloud

Copy link to headingAbout Qwen3 Coder Next

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Qwen3 Coder Next

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions