Qwen: Qwen2.5 32B Instruct
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Qwen2.5 32B Instruct is a general-purpose language model from Alibaba's Qwen team, sitting at the practical sweet spot between the 14B and 72B variants in the Qwen2.5 series — delivering stronger reasoning and language understanding than the 14B while remaining far more cost-efficient than the 72B.
Trained on 18 trillion tokens, the model scores 57.7 on MATH and outperforms Qwen2-72B on comprehensive evaluations despite having fewer parameters. It excels at instruction following, multi-step reasoning, mathematics, coding assistance, and multilingual tasks across 29+ languages, with a 131K token context window and full tool-call support.
A well-rounded choice for developers who need reliable general-purpose performance — complex enough for demanding workflows, light enough to keep inference costs manageable.
Context Window 131K
tokens
Max Output 8K
tokens
Input Cost $0.7
per million tokens
Output Cost $2.8
per million tokens
Input text
modalities
Tool Use Yes
Knowledge Cutoff Apr 2024
Release Date Sep 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
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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 32B Instruct 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 32B Instruct 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 | $0.7 |
| Output | $2.8 |
Qwen2.5 32B Instruct was created by Qwen and released on Sep 2024.
Qwen2.5 32B Instruct supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Qwen2.5 32B Instruct can generate up to 8K tokens in a single response.
Qwen2.5 32B Instruct has a knowledge cutoff date of Apr 2024. This means the model was trained on data available up to that date.
Qwen2.5 32B Instruct accepts the following input types: text. It produces: text.
Yes, Qwen2.5 32B Instruct supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Qwen2.5 32B Instruct scores 7.2 on the Artificial Analysis Intelligence Index, outperforming 23% of tracked models.
Yes — the Qwen2.5 32B Instruct 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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