Qwen (Alibaba)

Qwen 2.5 Plus (written Qwen2.5-Plus or Qwen-Plus by Alibaba) is a large language model from Alibaba Cloud’s Qwen family. It is a hosted model, available through the Qwen Chat interface and Alibaba Cloud’s Model Studio API, and sits alongside smaller open-weight Qwen 2.5 models that anyone can download. MLforSEO included it in a 2025 benchmark of NLP APIs against generative AI for entity extraction.

How it works

Like other LLMs, Qwen generates text by predicting the next token, after pre-training on a very large multilingual corpus and further training to follow instructions. The Qwen 2.5 generation was announced in September 2024 with open-weight models from 0.5 to 72 billion parameters, mostly under the Apache 2.0 licence, plus the API-only Qwen-Plus and Qwen-Turbo. Alibaba followed with Qwen2.5-Max in January 2025 and the Qwen3 family in April 2025, so Qwen 2.5 Plus is no longer the newest option.

You can use it as a chatbot or call it programmatically for tasks such as extraction, classification and summarisation, typically by asking for output in a table or JSON.

How it’s used in SEO and marketing

• Extracting entities, attributes and facts from text with a prompt.
• Classifying and labelling keywords or content at scale, often at a lower price than better-known models.
• Multilingual work, since the Qwen family is trained on many languages, including Chinese.
• Checking how a different family of AI assistants describes your brand.

Example

In MLforSEO’s comparison, a 221-word bio was given to three NLP APIs and four chatbots with the same prompt. Qwen2.5-Plus returned 25 entities across six types and found all 13 core named entities. Its typing was inconsistent: organisations appeared as Organization, Company or University, and MLforSEO Academy was labelled a Product. Twelve entities were industry concepts grouped as “Field”. When the same prompt was run a second time, Qwen returned 24 entities with different ordering and grouping, the clearest example of the repeatability problem in the test.

Limitations and things to watch

• Outputs vary between runs; fix the prompt and output format, and test consistency before relying on it.
• Unlike purpose-built NLP APIs, it returns no salience, confidence or knowledge-base links unless you ask, and then it estimates them.
• Model names and versions change quickly; record which model and date you tested.
• Check data residency and terms before sending client or personal data to any hosted model.

Learn it on MLforSEO

• Comparing NLP APIs for Entity and Sentiment Analysis (vs Generative AI)
• Entity & Sentiment Analysis APIs: Comparative Review
• Introduction to Machine Learning for SEO and AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands courses