Qwen 2.5 72B
Alibaba's powerful open-source model. Excellent at coding, math, and multilingual tasks.
Qwen 2.5 72B is text & chat AI model from Alibaba / Qwen, priced at โฌ12.00 per 1M input tokens with a 131.1K tokens context window.
0.7
Examples
See what Qwen 2.5 72B can generate
Given monthly sales data [Jan: 12000, Feb: 15000, Mar: 14500, Apr: 18000, May: 22000, Jun: 19500], identify the trend and forecast July
The data shows a general upward trend with month-over-month growth averaging about 12.5%. There's a notable spike in May (+22.2%) followed by a correction in June (-11.4%), suggesting possible seasonality or a promotional event in May. Using linear regression on the six data points, the projected July sales are approximately 21,200. However, given the May-June pullback pattern, a more conservative estimate of 20,000-21,500 accounts for potential volatility.
Write a regex pattern that validates email addresses and explain each part
Pattern: `^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$` Breakdown: - `^` โ Start of string - `[a-zA-Z0-9._%+-]+` โ One or more valid local-part characters (letters, digits, dots, underscores, percent, plus, hyphen) - `@` โ Literal @ symbol - `[a-zA-Z0-9.-]+` โ Domain name (letters, digits, dots, hyphens) - `\.` โ Literal dot before TLD - `[a-zA-Z]{2,}` โ TLD must be at least 2 letters - `$` โ End of string Note: This covers most common emails but won't match every valid RFC 5322 address.
Pricing
API Integration
Use our OpenAI-compatible API to integrate Qwen 2.5 72B into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple โ just pass a string
const reply = await rw.run("qwen-2-5-72b", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("qwen-2-5-72b", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("qwen-2-5-72b", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive โ Alibaba Cloud (Qwen team)'s Qwen 2.5 72B
The Qwen (้ไนๅ้ฎ, Tongyi Qianwen) team is the LLM research group inside Alibaba Cloud, the cloud-computing arm of Alibaba Group founded in 2009. Alibaba started large-language-model research at the Damo Academy and Tongyi Lab; the Qwen series became publicly available in 2023. Releases include Qwen-7B (Aug 2023, first Chinese open-weight foundation model from a major cloud), Qwen-14B/72B (late 2023), Qwen 1.5 (Feb 2024), Qwen2 family (Jun 2024), Qwen2.5 family (Sep 2024 with 0.5B/1.5B/3B/7B/14B/32B/72B sizes plus Qwen2.5-Coder, Qwen2.5-Math, Qwen2.5-VL), Qwen3 family in 2025 introducing thinking mode, and Qwen2.5-Max as the closed-weight flagship. The Qwen team is led by Junyang Lin and has published over a dozen widely cited technical reports. Models are released under the Tongyi Qianwen LICENSE (commercial-friendly for most use cases but with a >100M MAU clause requiring a separate license). Qwen has become the dominant open-weight model family for Chinese-language tasks and has the largest derivative-model ecosystem on HuggingFace by download volume.
Visit Alibaba Cloud (Qwen team) โQwen2.5-72B was released by the Alibaba Qwen team in September 2024 as the flagship dense model of the Qwen2.5 family. It is a decoder-only Transformer with 72.7B parameters, 80 layers and Grouped Query Attention (GQA) with 64 query heads and 8 KV heads. The model was pretrained on a 18-trillion-token multilingual corpus emphasising Chinese, English and 27 other languages, plus heavy concentrations of math, code and long-form documents. Compared to Qwen2-72B (7T tokens), the 18T-token pretraining and improved data filtering pipeline meaningfully lifted knowledge, math and code performance. Qwen2.5-72B supports a 128K token (131,072) context window with YaRN scaling extension and ships in both base and Instruct variants. Post-training applied a multi-stage SFT pipeline on over 1M curated examples followed by Direct Preference Optimisation (DPO) and Group Relative Policy Optimisation (GRPO) for reasoning data. Qwen2.5-Coder-32B and Qwen2.5-Math-72B specialised siblings push code and math frontiers, and Qwen2.5-VL adds vision. Weights are released under the Tongyi Qianwen LICENSE (free commercial use unless >100M MAU). The Qwen ecosystem also publishes GGUF, AWQ, GPTQ quantised variants and is supported by vLLM, SGLang, llama.cpp, Ollama, MLX and Apple MLX.
- Parameters
- 72.7B (dense)
- Context
- 131.1K tokens
- 72.7B dense parameters with Grouped Query Attention
- Pretrained on 18T multilingual tokens
- 128K context window with YaRN scaling
- Strong multilingual performance: Chinese, English, Japanese, Korean and 27 more
- Specialised siblings: Qwen2.5-Coder, Qwen2.5-Math, Qwen2.5-VL
- Function calling and JSON mode supported
- Open weights under Tongyi Qianwen LICENSE (commercial-friendly)
- Massive ecosystem: vLLM, SGLang, llama.cpp, Ollama, MLX, HuggingFace
- GGUF/AWQ/GPTQ quantised variants officially published
- Strong math performance (Qwen2.5-Math variant beats GPT-4o on competition math)
- Best for: open-weight Chinese/English chat, coding, on-prem enterprise, RAG.
Pretrained on 18 trillion tokens of multilingual web text, code, books and scientific papers, with strong Chinese and English coverage. Post-training uses 1M+ curated SFT examples followed by DPO and GRPO. Data cutoff approximately mid-2024.
License: Tongyi Qianwen LICENSE: free commercial use for products with fewer than 100M monthly active users. >100M MAU requires a separate commercial license from Alibaba.
Known limitations
- Filters Chinese political topics
- Tongyi Qianwen LICENSE adds a >100M MAU clause
- Vision requires the separate Qwen2.5-VL checkpoint
- Knowledge cutoff mid-2024
- Long context recall quality degrades beyond ~64K
Frequently asked questions
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