ChatGPT vs Gemini: Which is Better in 2025?

Correctly solves: they meet at 8:18 AM, 78 km from Mumbai. Shows step-by-step working with clear equations.

Correctly solves with additional context about real Indian Railway stations and suggests checking live running status on NTES. Shows working but adds unnecessary narrative.

Sets up the puzzle correctly but occasionally makes errors in the deduction chain. Gets the right answer about 70% of the time.

Consistently solves correctly with a clear truth table. Tracks all constraints without error. Gets the right answer 95% of the time.

Provides a solid generic strategy but lacks Indian market specifics. Suggests Sephora and Nykaa partnerships without addressing Indian retail dynamics.

Provides a detailed strategy including specific Indian retail chains (New U, Health & Glow, local kirana partnerships), GST implications, and regional pricing strategies.

Uses a clear analogy (two magical dice) but does not relate to the Indian curriculum or mention NCERT topics. Good but generic.

Uses a relatable analogy (two rangoli patterns that match instantly) and references NCERT Class 8 Physics chapter on light. Culturally contextualised.

Produces complete, production-ready code with requests, BeautifulSoup, random user-agent rotation, exponential backoff, and CSV export. Includes error handling for 403/429 responses.

Produces functional code but uses deprecated libraries (urllib instead of requests). Misses rate-limiting nuances. Code works but needs significant refactoring for production.

Creates a clean, accessible React component with useState, form validation, and payment method selection. Includes mock UPI ID validation regex. TypeScript-ready.

Creates a working component but with older React patterns (class-based in some attempts). Form validation is basic. UPI regex is incorrect (misses @paytm and @phonepe patterns).

Clear explanation with Mumbai local train network analogy. Pseudocode is correct and well-commented. Includes time complexity analysis.

Uses Bangalore metro network analogy which is more current. Pseudocode is cleaner. Adds a visual ASCII diagram of the network. Time complexity explained with real-world example.

Identifies the error immediately: GST is 18% for most goods regardless of state (IGST vs CGST/SGST distinction). Explains the correct logic with state-wise IGST rules.

Also identifies the error but adds a detailed table of GST rates by product category (5%, 12%, 18%, 28%). References GST Council decisions. More comprehensive context.

Can process the image (Plus only) but occasionally misses fields or misreads the 12-digit Aadhaar number. Struggles with regional language text on the card.

Natively processes the image with near-perfect OCR. Correctly identifies all fields including the masked Aadhaar number. Reads Hindi/English mixed text accurately.

Reads the chart correctly but occasionally mislabels axes or misreads smaller bars. Good at trend identification but not precise on exact numbers.

Reads all values precisely. Identifies the top city (Delhi), notes Diwali spike in October/November, and correctly points out the post-festival dip. Includes data table extraction.

Provides a reasonable description but vendor counts are often inaccurate. Cannot reliably identify specific products from blurry images.

More accurate vendor counting. Better at identifying produce types (vegetables, textiles, street food). Provides a more structured breakdown of the scene.