Qwen: Qwen2.5 7B Instruct 1M
qwen/qwen2.5-7b-instruct-1m
Access Qwen2.5 7B Instruct 1M from Qwen using the 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/qwen2.5-7b-instruct-1m"
}).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/qwen2.5-7b-instruct-1m"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
Qwen2.5 7B Instruct 1M is a 7-billion-parameter instruction-tuned chat model from Alibaba's Qwen team, released in January 2025 as the long-context variant of Qwen2.5 7B Instruct in the Qwen2.5-1M series.
It handles contexts up to 1 million tokens, enough to fit entire codebases, books, or large document collections in a single request. In Qwen's 1M-token passkey retrieval test it finds hidden information with near-perfect accuracy (minor errors reported for the 7B size), and on RULER, LV-Eval, and LongBench-Chat it outperforms its 128K predecessor, especially on sequences beyond 64K tokens.
On short-context academic benchmarks it performs about the same as the 128K version, which Qwen reports as comparable to GPT-4o-mini, while supporting a context eight times longer. A low-cost option for long-document question answering, retrieval, and analysis over very large inputs.
Context Window 1M
tokens
Max Output 8K
tokens
Input Cost $0.18
per million tokens
Output Cost $0.74
per million tokens
Input text
modalities
Release Date Jan 27, 2025
Model Playground
Try Qwen2.5 7B Instruct 1M 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 7B Instruct 1M 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.
Qwen2.5 7B Instruct 1M is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $0.18 |
| Output | $0.74 |
Qwen2.5 7B Instruct 1M was created by Qwen and released on Jan 27, 2025.
Qwen2.5 7B Instruct 1M supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,016 pages of text.
Qwen2.5 7B Instruct 1M can generate up to 8K tokens in a single response.
Qwen2.5 7B Instruct 1M accepts the following input types: text. It produces: text.
Yes — the Qwen2.5 7B Instruct 1M 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.
Add Qwen2.5 7B Instruct 1M to your app for free
Developers can integrate Qwen2.5 7B Instruct 1M for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.