Gemini (Google)

Gemini is Google’s family of multimodal large language models, developed by Google DeepMind and first announced in December 2023. The name covers both the models (offered in larger and smaller sizes, such as Pro and Flash) and the consumer Gemini app, which replaced Bard in 2024. Gemini models can read and generate text and understand images, audio, video and code.

How it works

Like other LLMs, Gemini is a transformer model trained on very large datasets to predict and generate tokens, then tuned to follow instructions. Being multimodal means different input types are processed by the same model, so you can send a screenshot, a PDF or a video alongside a text prompt. Google exposes Gemini in several places:

• The Gemini app and Gemini in Google Workspace (Docs, Sheets, Gmail).
• The Gemini API through Google AI Studio for developers, and through Vertex AI on Google Cloud for production and enterprise use.
• Google Search: AI features such as AI Overviews, AI Mode and multi-object Circle to Search are powered by custom Gemini models.

How it’s used in SEO and marketing

• Content transformation: summarising, rewriting, translating and extracting insights from transcripts, reviews or research.
• Brainstorming and data preparation: MLforSEO’s keyword universe lessons use ChatGPT or Gemini to draft brand-name regex lists and audience category terms.
• Image work: drafting alt text and classifying images through the Gemini API (with human review).
• Crawling workflows: Screaming Frog can send extracted page content to Gemini with a prompt during a crawl.
• AI search visibility: tracking whether and how Gemini-powered experiences mention and cite your brand.

Example

In MLforSEO’s benchmark of NLP APIs against generative AI for entity extraction, the same text and prompt were given to ChatGPT, Gemini, DeepSeek R1 and Qwen. Gemini returned no entities: both models tried read the text as a question about a person and declined. The lesson is less about Gemini specifically and more that chatbots are inconsistent for structured extraction, whereas a dedicated API such as the Google Cloud Natural Language API returns predictable fields every time.

Limitations and things to watch

• Outputs vary between runs and model versions, so pin a model version and use structured outputs for anything you repeat at scale.
• It can produce confident but wrong statements; verify facts, numbers and citations.
• Safety filters can refuse harmless tasks, as in the entity benchmark.
• Models and names change quickly; check Google’s current model list and pricing before building on a specific version.
• Gemini in Search draws on Google’s index and ranking systems, so being cited still depends on content that is crawlable, clear and trustworthy.

Learn it on MLforSEO

• Comparing NLP APIs for Entity and Sentiment Analysis (vs Generative AI)
• Image SEO in 2026: The Complete Guide to Image Optimisation, Advanced Tactics and Multimodal Search
• Web Scraping of Page Content in 3 Easy Steps with Screaming Frog
• Introduction to Machine Learning for SEO (course)