Qwen: Qwen2.5-Omni 7B
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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
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
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B is Alibaba's open-weight release of its Qwen3.8 Max flagship, a sparse mixture-of-experts model with 2.4 trillion total parameters and 95 billion active per token, routed across 512 experts. It uses a hybrid attention design (Gated DeltaNet and Gated Attention layers) across 92 layers, with a native 262K context window and thinking mode enabled for every response. Alibaba reports 93.0 on PaperBench (ahead of GPT-5.6 Sol's 90.5), 92.6 on GPQA Diamond, 86.6 on Terminal-Bench 2.1, and 67.7 on SWE-bench Pro, positioning it for coding, research, and long-horizon agentic work. It gives developers access to Qwen-Max-class capability under open weights, useful for teams that want frontier-level coding and agentic performance without a closed API.
ChatQwen3.8 Max
Qwen3.8 Max is Alibaba's flagship large language model, released August 3, 2026 as the most capable model in the Qwen family to date. It uses a mixture-of-experts architecture with 2.4 trillion total parameters and about 95 billion active per request, and accepts text, image, and video input with a context window of up to 1 million tokens. Alibaba positions it for coding and long-horizon agentic work: in testing the model ran autonomously for over 10 days building a self-evolving software harness. Reported benchmarks include 93.0 on PaperBench, 82.8 on IFBench, 86.6 on Terminal-Bench 2.1, and 86.1 on OSWorld-Verified, ahead of Claude Opus 4.8 on several coding and agent tasks and roughly matching Claude Fable 5 and GPT-5.6 Sol, though it trails both on some evaluations. On the Arena.AI leaderboard it ranks as the top Chinese model for text tasks. Alibaba plans to open-source the weights on Hugging Face and ModelScope.
ChatQwen3.7 Flash
Qwen3.7 Flash is the low-cost, fast tier of Alibaba's Qwen3.7 family, released in July 2026. It is a vision-language model that accepts text, image, and video input across a 1 million-token context window, with reasoning enabled by default and a 262K-token thinking budget. Alibaba positions it as an upgrade over Qwen3.6 Flash in multimodal understanding and agent execution, with better object recognition and spatial intelligence. It supports function calling and structured outputs. Pricing is tiered by prompt length. Requests under 32K input tokens cost $0.03/$0.13 per million, rising to $0.20/$0.80 above 256K. Alibaba published no benchmarks at launch. An independent vision evaluation by Roboflow measured strong object identification (84.4%) but weak OCR and object detection, so it fits high-volume multimodal tasks (classification, visual agents, lightweight extraction) better than document-heavy 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.
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