Qwen: Qwen2.5-Omni 7B
qwen/qwen2-5-omni-7b
Access Qwen2.5-Omni 7B from Qwen using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "qwen/qwen2-5-omni-7b"
}).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", {
model: "qwen/qwen2-5-omni-7b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="qwen/qwen2-5-omni-7b",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "qwen/qwen2-5-omni-7b",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Qwen2.5-Omni 7B is Alibaba's end-to-end omni-modal model capable of perceiving text, images, audio, and video simultaneously while generating text and natural speech in real time.
Built on a Thinker-Talker architecture with TMRoPE (Time-aligned Multimodal RoPE) for synchronizing audio and video streams, the 7B model achieves strong benchmark results across all modalities. It ranked first on the MMAU audio understanding leaderboard, scored 59.2 on MMMU image reasoning (near GPT-4o-mini's 60.0), and achieved 64.3 on Video-MME for video understanding without subtitles. On OmniBench, which tests cross-modal integration, it reached 56.13%.
The model supports tool/function calling and targets developers building voice assistants, video analysis tools, and multimodal pipelines that require a single model to handle diverse input types.
Context Window 33K
tokens
Max Output 2K
tokens
Input Cost $0.1
per million tokens
Output Cost $0.4
per million tokens
Input text, image, audio, video
modalities
Tool Use Yes
Knowledge Cutoff Apr 2024
Release Date Dec 2024
Model Playground
Try Qwen2.5-Omni 7B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Qwen
Qwen3.6 Flash
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
You can access Qwen2.5-Omni 7B by Qwen through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Qwen2.5-Omni 7B to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.1 |
| Output | $0.4 |
Qwen2.5-Omni 7B was created by Qwen and released on Dec 2024.
Qwen2.5-Omni 7B supports a context window of 33K tokens. For reference, that is roughly equivalent to 66 pages of text.
Qwen2.5-Omni 7B can generate up to 2K tokens in a single response.
Qwen2.5-Omni 7B has a knowledge cutoff date of Apr 2024. This means the model was trained on data available up to that date.
Qwen2.5-Omni 7B accepts the following input types: text, image, audio, video. It produces: text.
Yes, Qwen2.5-Omni 7B supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Qwen2.5-Omni 7B 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.
Get started with Puter.js
Add Qwen2.5-Omni 7B to your app without worrying about API keys or setup.
Read the Docs View Tutorials