Qwen: Qwen-Omni Turbo
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Qwen-Omni Turbo is Alibaba's cost-optimized omnimodal API model, built to process text, image, audio, and video inputs and return text responses in a single unified interface.
It is the lighter, faster tier in the Qwen-Omni family, designed for developers who need full multimodal coverage at lower latency and cost than the flagship Qwen-Omni model. Audio files up to 40 seconds and video files up to 150 MB are supported, spanning common formats such as MP3, WAV, MP4, and MOV. The model handles tool calling natively and is accessible via an OpenAI-compatible API.
Best suited for developers building applications that need to reason across mixed media inputs — such as audio transcription pipelines, video understanding workflows, or multimodal chatbots — where throughput and cost efficiency matter.
Context Window 33K
tokens
Max Output 2K
tokens
Input Cost $0.07
per million tokens
Output Cost $0.27
per million tokens
Input text, image, audio, video
modalities
Tool Use Yes
Knowledge Cutoff Apr 2024
Release Date Jan 19, 2025
Code Example
Add AI to your app with the Puter.js AI API, no API keys or setup required.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
More AI Models From Qwen
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Qwen3.6 Flash is the speed-optimized tier of Alibaba's Qwen3.6 model family, designed for high-throughput, low-latency inference pipelines. It sits alongside Qwen3.6 Max Preview, Plus, and 35B-A3B in the product lineup, targeting use cases where fast response times matter more than peak benchmark scores. Like other Qwen3.6 models, it builds on a hybrid architecture combining linear attention with sparse mixture-of-experts routing. It is best suited for high-volume production workloads such as classification, extraction, summarization, and lightweight agent tasks where latency and cost efficiency are the primary constraints.
ChatQwen3.5 Plus 2026-04-20
Qwen3.5 Plus is a proprietary hosted model from Alibaba, built on the Qwen3.5-397B-A17B Mixture-of-Experts architecture with 397 billion total parameters and 17 billion active per token. Its headline feature is a 1-million-token native context window — among the largest available via API — making it well suited for processing entire codebases, long documents, or extended multi-turn conversations in a single request. It supports both a deep-thinking mode and an "Auto" mode that adaptively invokes tools like web search and code interpreters. This April 20, 2026 snapshot reflects ongoing improvements to the model since its original February 2026 launch. The Qwen3.5 series demonstrated strong multimodal performance across reasoning, coding, and vision tasks. A solid general-purpose option for developers needing large-context capabilities without migrating to the newer Qwen3.6 line.
ChatQwen3.6 27B
Qwen3.6 27B is a dense 27-billion-parameter multimodal model from Alibaba's Qwen team, purpose-built for agentic coding and repository-level reasoning. It scores 77.2% on SWE-bench Verified and 59.3% on Terminal-Bench 2.0, outperforming the previous-generation Qwen3.5-397B-A17B across all major coding benchmarks despite being far smaller. It natively supports text, image, and video inputs with a 262K-token context window, extendable to 1M tokens. A standout feature is Thinking Preservation, which retains reasoning traces across conversation turns — reducing redundant computation in multi-step agent loops. The model uses a hybrid attention architecture combining Gated DeltaNet with traditional self-attention. Ideal for developers building coding agents, multi-turn tool-use workflows, or frontend generation pipelines.
Frequently Asked Questions
Qwen-Omni Turbo is no longer available through Puter.js. Explore other AI models for alternatives.
| Price per 1M tokens | |
|---|---|
| Input | $0.07 |
| Output | $0.27 |
Qwen-Omni Turbo was created by Qwen and released on Jan 19, 2025.
Qwen-Omni Turbo supports a context window of 33K tokens. For reference, that is roughly equivalent to 66 pages of text.
Qwen-Omni Turbo can generate up to 2K tokens in a single response.
Qwen-Omni Turbo has a knowledge cutoff date of Apr 2024. This means the model was trained on data available up to that date.
Qwen-Omni Turbo accepts the following input types: text, image, audio, video. It produces: text.
Yes, Qwen-Omni Turbo supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Qwen-Omni Turbo API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
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