# Qwen3-14B

A dense 14-billion-parameter model supporting 119 languages, with a dual-mode reasoning system that lets you choose between deep chain-of-thought and instant response depending on task complexity.

- **Model ID:** `alibaba/qwen-3-14b`
- **Type:** chat
- **Providers:** deepinfra
- **Context window:** 40,960
- **Maximum output tokens:** 16,384
- **Pricing:** $0.12/1M input tokens, $0.24/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/qwen-3-14b

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

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

## About

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.

## What to consider

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

## When to use

### 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

## 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

- **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

## Frequently 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.

## Links

- [Model page](https://vercel.com/ai-gateway/models/qwen-3-14b)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://deepinfra.com/Qwen/Qwen3-14B)
- [Provider pricing](https://deepinfra.com/pricing)