Keyword Extractor
Commercial use OK
380+ models
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No sign-up needed
Model:
+ GPT-5, Claude, Gemini
Extracted keywords
Advanced options
Result
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Extract keywords from any text with free AI. SEO and content analysis made easy.
How to Use Keyword Extractor
1
Enter your input
Type text, upload a file, or describe what you want. No account needed.
2
Click generate
Our AI processes your request in seconds using the best open-source models.
3
Download & share
Download, copy, or share your result. Free for personal and commercial use.
Use this tool via API
Automate this tool from your own code. OpenAI-compatible REST endpoint, Bearer-token auth, no extra SDK required. Token costs match the web interface.
curl -X POST https://api.free.ai/v1/chat/ \
-H "Authorization: Bearer sk-free-..." \
-H "Content-Type: application/json" \
-d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Summarize this: ..."}]}'Related Free AI Tools
Keyword Extractor — FAQ
Extracts the most meaningful words and phrases from any long-form text (article, blog post, transcript, product description). Five extraction modes: (1) SEO keywords with search-intent tagging, (2) high-level topics + themes, (3) named entities — people/places/organizations, (4) tag-style lowercase-hyphen-joined tags, (5) academic index terms. Results scored 0-100 by TF-IDF, raw frequency, or semantic relevance. Export as CSV, TXT, or JSON.
Yes — a 1,500-word article extracts in ~350 tokens on the default Qwen 3 30B model, comfortably inside the 2,500 anonymous or 10,000 signed-up daily pool. No sign-up required for your first extraction.
Those tools pull live search-volume data for keywords you give them ($99+/mo). Keyword Extractor does the opposite — it extracts candidate keywords FROM your content and classifies their search intent. Workflow: use this to find what phrases your article already emphasizes, then paste those into Ahrefs/Semrush to check monthly search volume. Pairs nicely with their free keyword-difficulty scores.
Three scoring modes: (1) TF-IDF (default) rewards words both frequent in THIS document AND rare in general English — best for SEO since these are the differentiating terms. (2) Raw frequency counts occurrences straight — best when you want to see what you say most. (3) Semantic relevance scores by closeness to the documents main thesis even if a phrase appears only once — best for topical coherence.
An n-gram is a contiguous sequence of N words. "climate change" is a 2-gram. "artificial intelligence model" is a 3-gram. Toggle the chips to extract only 1-grams (single words like "photosynthesis"), 2-grams (head phrases like "carbon footprint"), or 3-grams (long-tail like "carbon footprint calculator"). Most SEO workflows want 2+3-grams — single words are too generic.
Yes — the default stoplist removes "the", "and", "of", "is", etc. in 14 languages. You can add custom stopwords via the "Words to exclude" field — useful for suppressing your own brand name, product codes, or boilerplate legalese that would otherwise dominate the results.
Google classifies queries by user purpose: informational ("what is X"), navigational ("nike official store"), commercial ("best running shoes 2026"), transactional ("buy air max online"). Keyword Extractor tags your extracted keywords with the likely intent so you know whether your article should rank for researchers, shoppers, or direct-traffic lookups. Crucial for aligning content with SERP features.
99 languages via the underlying Qwen 2.5 model. Highest quality on English, Spanish, French, Portuguese, German, Chinese, Japanese. Lower-resource languages work but 3-gram extraction may be less reliable — fall back to 1+2-grams for Arabic, Hindi, Turkish, etc.
The UI runs one text at a time. For bulk extraction wire the /v1/chat/ API into a Python script — iterate over your CMS export, POST each article body, dump the JSON. Bearer auth, 1,000 calls/hour on free accounts, higher limits on pro. Docs at /api/.
RAKE and YAKE are classic lexical algorithms — fast but weak on semantic nuance (they miss "tech startup" if the document says "technology startup" but not "tech" alone). KeyBERT uses BERT embeddings — better semantic quality but slow and requires installation. TextRank is graph-based. Our approach uses a 7B-parameter LLM which does all of the above zero-shot plus search-intent classification — slower per call but zero setup, free, and no ML engineering required.
In frequency and TF-IDF modes, yes — every keyword is derivable from the source text verbatim or with minimal inflection change. In semantic-relevance mode the model may return a controlled-vocabulary version ("pulmonology" even if your text says "lung doctor visit"). The academic-terms mode intentionally prefers controlled vocabulary.
Yes — POST to /v1/chat/ with the same system prompt this page builds (inspect template source for the exact prompt). Returns structured JSON. Good for content-strategy dashboards or CMS plugins. Bearer auth, monthly limits. Docs at /api/.
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