spaCy vs TextLens API

spaCy is the gold standard for NLP pipelines. TextLens API is the zero-install alternative for readability, sentiment, and keywords.

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Side-by-side comparison

Feature spaCy TextLens API
Install size 20MB–560MB (models) Zero install
Languages supported Python only Any language with HTTP
Readability scores (Flesch-Kincaid, SMOG, Gunning Fog) ✗ ✓
Sentiment analysis ✗ (not built-in) ✓
Named Entity Recognition ✓ ✗
Dependency parsing ✓ ✗
Keyword extraction ✓ (noun chunks) ✓ (TF-IDF)
REST API ✗ (Python library) ✓
Free tier Open source 1,000 req/month
Model download required Yes (en_core_web_sm = 12MB, en_core_web_lg = 560MB) No

The code

spaCy Python

import spacy
# Requires: pip install spacy && python -m spacy download en_core_web_sm
nlp = spacy.load('en_core_web_sm')  # 12MB download
doc = nlp(text)
# No built-in readability scores
# Sentiment requires textblob or custom component
for token in doc:
    print(token.lemma_, token.pos_)

TextLens API Any language

import requests
r = requests.post('https://api.textlens.dev/v1/analyze',
  headers={'X-API-Key': 'your-key'},
  json={'text': text})
result = r.json()
print(result['readability']['flesch_kincaid_grade'])
print(result['sentiment']['label'])  # positive/neutral/negative
print(result['keywords'][:5])

When to use each

When to use spaCy

  • Full NLP pipelines requiring deep linguistic analysis
  • Named Entity Recognition across documents
  • Dependency parsing and part-of-speech tagging
  • Custom model training on your own corpus
  • spaCy is outstanding for these use cases

When to use TextLens API

  • Readability scoring, content quality, sentiment analysis
  • Your stack is Node, Ruby, Go, or PHP — not Python
  • No model downloads or GPU required
  • You want a REST endpoint, not a library to maintain
  • Results from any runtime in milliseconds

Use both together

spaCy for NER + parsing. TextLens API for readability + sentiment.

spaCy + TextLens API Python

import spacy
import requests

nlp = spacy.load('en_core_web_sm')
text = "Your document text here."

# spaCy: NER and dependency parsing
doc = nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]

# TextLens API: readability and sentiment
r = requests.post('https://api.textlens.dev/v1/analyze',
  headers={'X-API-Key': 'your-key'},
  json={'text': text})
result = r.json()

print(entities)
print(result['readability']['flesch_kincaid_grade'])
print(result['sentiment']['label'])

Get Early Access

TextLens API is in development. Join the waitlist to get notified at launch.

From the team behind textlens — the zero-dependency npm package that powers the API.

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