Qwen: Qwen2.5 7B Instruct Turbo
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Qwen2.5 7B Instruct Turbo is a 7-billion-parameter instruction-tuned chat model from Alibaba's Qwen team, served as a fast, low-cost Turbo endpoint via Together AI.
It is known for strong coding and math performance relative to its size, scoring 84.8 on HumanEval and 75.5 on MATH, and it excels at instruction following, structured-data understanding, and reliable JSON output. The model supports tool/function calling and handles 29+ languages.
Published comparisons show it outperforming similarly sized open models such as Llama 3.1 8B Instruct and Gemma 2 9B across most tasks.
Choose it when you want a cheap, capable small model for coding assistants, structured extraction, multilingual chat, and high-throughput API workloads.
Context Window 33K
tokens
Max Output 31K
tokens
Input Cost $0.3
per million tokens
Output Cost $0.3
per million tokens
Input text
modalities
Tool Use Yes
Knowledge Cutoff Sep 2024
Release Date Sep 19, 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 Flash
Qwen3.8 Flash is a multimodal model from Alibaba's Qwen team, released August 26, 2026, as the fast, lower-cost tier of the Qwen3.8 family alongside Qwen3.8 Max and Qwen3.8 27B. It uses a mixture-of-experts architecture with 125B total parameters and 6B active per token, an early preview of the architecture planned for Qwen4. It accepts text, image, and video input and returns text, with a 1,000,000 token context window and output capped at 128,000 tokens. The API supports tool calling, structured outputs via JSON schema, and prompt caching, with cached input billed at $0.016 per million tokens. Pricing is $0.14 per million input tokens and $0.42 per million output tokens, about one-twelfth the cost of Qwen3.8 Max. Alibaba says it was trained at roughly one-ninth the cost of Qwen3.7-Plus and reports higher scores on benchmarks including SWE-bench Pro and CoWorkBench, an agentic office-task benchmark.
ChatQwen3.8 27B
Qwen3.8 27B is a dense, open-weight multimodal model from Alibaba's Qwen team, released August 14, 2026 as a smaller member of the Qwen3.8 family alongside the flagship Qwen3.8 Max. It combines Gated DeltaNet linear attention with standard gated attention across 64 layers, giving a 27 billion parameter dense model a native 262K token context window, extendable to 1M tokens. It accepts text, image, and video input, including hour-scale video and STEM diagrams. Alibaba reports 61.7 on SWE-bench Pro and 73.0 on Terminal Bench 2.1, both improvements over the earlier Qwen3.6 27B, and 89.2 on GPQA Diamond. Released under Apache 2.0, it gives developers an open-weight alternative to Qwen3.8 Max for coding and agentic tasks, at a fraction of the parameter count.
ChatQwen3.8 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.
Frequently Asked Questions
You can access Qwen2.5 7B Instruct Turbo 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 7B Instruct Turbo 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 | $0.3 |
| Output | $0.3 |
Qwen2.5 7B Instruct Turbo was created by Qwen and released on Sep 19, 2024.
Qwen2.5 7B Instruct Turbo supports a context window of 33K tokens. For reference, that is roughly equivalent to 66 pages of text.
Qwen2.5 7B Instruct Turbo can generate up to 31K tokens in a single response.
Qwen2.5 7B Instruct Turbo has a knowledge cutoff date of Sep 2024. This means the model was trained on data available up to that date.
Qwen2.5 7B Instruct Turbo accepts the following input types: text. It produces: text.
Yes, Qwen2.5 7B Instruct 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 Qwen2.5 7B Instruct 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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