What does the hybrid thinking mode actually change about how Qwen3-14B responds?

In thinking mode, the model produces an internal chain-of-thought trace before delivering its final answer, allocating more compute to complex reasoning steps. Non-thinking mode skips that trace and responds directly. You control the mode per request with the `enable_thinking` parameter and tune the thinking budget to balance latency against answer depth.

How does Qwen3-14B compare to the previous Qwen2.5 generation at the same size?

Alibaba Cloud positions Qwen3-14B as matching Qwen2.5-32B-Base performance despite being less than half the size.

Under what license is Qwen3-14B released?

Qwen3-14B is released under the Apache 2.0 license. Through AI Gateway, you access the model via hosted inference without managing your own infrastructure.

What is the context window for Qwen3-14B?

The context window is 41.0K tokens, which applies to the combined prompt and output length.

Can I use Qwen3-14B for agentic workflows with tool calling?

Yes, Qwen3 models support agentic scenarios including tool calling and MCP (Model Context Protocol). The Qwen-Agent framework provides additional scaffolding for multi-tool workflows if needed.

How does AI Gateway handle provider outages for this model?

AI Gateway automatically retries failed requests across the available providers in DeepInfra. If one provider is unavailable, requests are rerouted without changes to your application code.

Alibaba Cloud

Qwen3-14B

Qwen3-14B is a 14-billion-parameter dense language model from Alibaba Cloud that combines hybrid thinking modes with context of 41.0K tokens, delivering Qwen2.5-32B-class capability at a fraction of the parameter count.

View API reference

Input and output price: Input $0.12, Output $0.24, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'alibaba/qwen-3-14b',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Latency API Similar About FAQ

Copy link to headingPlayground

Try out Qwen3-14B 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.

Qwen3-14B

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

DeepInfra

Legal:Terms•Privacy

41K

16K

0.2 s

$0.12/M

$0.24/M

—

04/28/2025

Getting started

Call Qwen3-14B 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/qwen-3-14b',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Qwen3-14B 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/qwen-3-14b',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
  });

  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/qwen-3-14b`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Qwen3-14B supports up to `16,384` output tokens. Reasoning tokens count toward this limit.
`reasoning`	`'provider-default' \| 'none' \| 'minimal' \| 'low' \| 'medium' \| 'high' \| 'xhigh'`	No	Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under `providerOptions` take precedence when both are set. See the Reasoning section below.
`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 41K-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/qwen-3-14b',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['deepinfra'],
      },
    },
  });

  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.

Reasoning

AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.

Learn more in the AI Gateway reasoning guide.

reasoning.ts

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

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen-3-14b',
    prompt: 'Explain the Monty Hall problem step by step.',
    reasoning: 'high',
  });

  console.log(result.text);
}

main().catch(console.error);

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/qwen-3-14b',
    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 Qwen3-14B

Qwen3-14B is a dense transformer model with no sparse routing or mixture-of-experts. Every inference call activates all 14 billion parameters. This architecture trades raw efficiency for predictability: memory requirements and compute costs stay consistent across request types, which simplifies capacity planning.

The model includes Alibaba Cloud's hybrid thinking system. In thinking mode, Qwen3-14B works through a chain-of-thought before producing its final answer, allocating more compute to harder problems. In non-thinking mode, it responds immediately without the intermediate reasoning trace. The enable_thinking parameter controls which mode activates. You can adjust the thinking budget per request to match how much latency you're willing to accept.

Within the Qwen3 family, the 14B sits at a practical inflection point. Alibaba Cloud's benchmarks show Qwen3-14B matches Qwen2.5-32B-Base. You get the previous generation's mid-tier performance from a model less than half the size. That translates directly to lower hosting costs for teams running inference at scale.

The model covers 119 languages and dialects across Indo-European, Sino-Tibetan, Afro-Asiatic, Austronesian, and other language families. The result is strong coverage across coding, mathematics, and general instruction-following tasks.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: Your choice of provider may matter for latency-sensitive applications or where data residency requirements constrain which infrastructure regions are acceptable.
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 Qwen3-14B

Best for

Reasoning-intensive tasks on a budget: The hybrid thinking mode lets you activate deep reasoning selectively without committing to a larger model full-time. Use thinking mode for complex derivations and non-thinking mode for fast follow-up queries in the same session
Multilingual applications: With 119 languages covered, Qwen3-14B suits applications that need to handle user input from diverse linguistic backgrounds, customer support platforms, global content tools, or localization pipelines
Code generation and review: The model handles code completion, explanation, and debugging across common programming languages
Balanced latency and quality: When you need better output quality than the smallest models but can't justify the compute cost of the 32B or larger variants, the 14B sits in a useful middle ground

Consider alternatives when

Higher reasoning headroom is needed: For the hardest mathematical proofs, complex multi-step logic, or the most demanding coding challenges, Qwen3-32B or the MoE variants offer stronger ceiling performance
Throughput at the lowest possible cost per token: The Qwen3-30B-A3B MoE model activates only 3B parameters per inference, which can be significantly cheaper to serve despite its larger total parameter count
Vision or multimodal inputs are required: Qwen3-14B handles text only; a multimodal model would be needed for image or audio processing tasks

Copy link to headingConclusion

Qwen3-14B gives teams a dense, fully-activating model with the flexibility to run reasoning-heavy or latency-optimized inference from the same checkpoint. Accessing it through AI Gateway removes the overhead of managing multiple provider accounts while keeping automatic failover and consolidated billing in place.

Copy link to headingFrequently Asked Questions

What does the hybrid thinking mode actually change about how Qwen3-14B responds?
In thinking mode, the model produces an internal chain-of-thought trace before delivering its final answer, allocating more compute to complex reasoning steps. Non-thinking mode skips that trace and responds directly. You control the mode per request with the enable_thinking parameter and tune the thinking budget to balance latency against answer depth.
How does Qwen3-14B compare to the previous Qwen2.5 generation at the same size?
Alibaba Cloud positions Qwen3-14B as matching Qwen2.5-32B-Base performance despite being less than half the size.
Under what license is Qwen3-14B released?
Qwen3-14B is released under the Apache 2.0 license. Through AI Gateway, you access the model via hosted inference without managing your own infrastructure.
Which languages does Qwen3-14B support?
It covers 119 languages and dialects across Indo-European, Sino-Tibetan, Afro-Asiatic, Austronesian, Dravidian, Turkic, and other language families.
What is the context window for Qwen3-14B?
The context window is 41.0K tokens, which applies to the combined prompt and output length.
Can I use Qwen3-14B for agentic workflows with tool calling?
Yes, Qwen3 models support agentic scenarios including tool calling and MCP (Model Context Protocol). The Qwen-Agent framework provides additional scaffolding for multi-tool workflows if needed.
How does AI Gateway handle provider outages for this model?
AI Gateway automatically retries failed requests across the available providers in DeepInfra. If one provider is unavailable, requests are rerouted without changes to your application code.

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

Agent Stack

Core Platform

Tools

Learn

Build

Explore

Qwen3-14B

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

Reasoning

Tool calling

Copy link to headingMore models by Alibaba Cloud

Copy link to headingAbout Qwen3-14B

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Qwen3-14B

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions