GPT-4o vs Claude 3.5 vs Gemini 2.0: Which AI Model Is Best for What? | Tech Tips India

The Three-Way Race That Actually Matters

Picking an AI model used to be simple: you used ChatGPT because there was nothing else worth using. That era is over. OpenAI's GPT-4o, Anthropic's Claude 3.5 Sonnet, and Google's Gemini 2.0 Flash and Pro are all genuinely excellent, and the "best" one depends entirely on what you are trying to do.

I have spent the past two months running hundreds of prompts through all three models across six categories: coding, writing, analysis, math, creativity, and multilingual tasks. Not synthetic benchmarks — real tasks that I actually needed to accomplish for work and personal projects. The results surprised me in several ways.

Here is the breakdown, with specific examples and honest assessments of where each model excels and where it falls short.

The Models at a Glance

Feature	GPT-4o	Claude 3.5 Sonnet	Gemini 2.0 Pro
Company	OpenAI	Anthropic	Google
Release	May 2024 (updated)	June 2024 (updated)	Dec 2024
Context Window	128K tokens	200K tokens	2M tokens
Max Output	~16K tokens	~8K tokens	~8K tokens
Multimodal	Text, image, audio, video	Text, image	Text, image, audio, video
Free Tier	Yes (limited GPT-4o)	Yes (limited)	Yes (generous)
Paid Price	$20/month (Plus)	$20/month (Pro)	$20/month (Advanced)
API Input Cost	$2.50/M tokens	$3.00/M tokens	$1.25/M tokens
API Output Cost	$10.00/M tokens	$15.00/M tokens	$5.00/M tokens

A few things jump out immediately. Gemini's 2 million token context window is astronomical — you can feed it entire codebases. Claude's 200K window is the largest among the two competitors, and it handles long contexts more reliably than GPT-4o in my testing. API pricing favors Gemini significantly, which matters if you are building applications.

Coding: The Developer's Litmus Test

I tested all three models on real coding tasks across different complexity levels: bug fixing, feature implementation, code review, system design, and explaining complex codebases.

Bug Fixing

I gave each model a React component with three intentional bugs — a stale closure in a useEffect, an incorrect dependency array, and a race condition in concurrent API calls.

GPT-4o identified all three bugs and provided correct fixes with clear explanations. The fix for the race condition used an AbortController pattern, which is the modern best practice. Response was well-structured with code blocks.

Claude 3.5 Sonnet also identified all three bugs. What stood out was the quality of explanation — Claude explained why the stale closure occurred (referencing JavaScript's closure semantics and React's render cycle) in a way that would teach you, not just fix the immediate problem. The code suggestions were clean and idiomatic.

Gemini 2.0 Pro caught two of the three bugs. It identified the stale closure and the dependency array issue but missed the race condition until I asked a follow-up question. The explanations were accurate but briefer.

Feature Implementation

I asked each model to implement a real-time search feature with debouncing, cancellation of previous requests, loading states, and error handling in TypeScript/React.

All three produced working code, but with different approaches:

GPT-4o wrote a custom hook using useCallback and useRef for the debounce. Clean and practical.
Claude wrote a custom hook with a more comprehensive approach — it included TypeScript generics for reusability, handled edge cases like empty search strings, and added JSDoc comments explaining each decision.
Gemini used a similar approach to GPT-4o but included an interesting optimization: it cached previous search results in a useRef Map to avoid re-fetching identical queries.

Code Review

I pasted a 200-line Python script with various issues (security vulnerabilities, performance problems, style inconsistencies) and asked for a code review.

This is where the models diverged significantly:

Claude produced the most thorough review. It caught a SQL injection vulnerability, identified an N+1 query problem, flagged a potential memory leak in a file handle, suggested using pathlib over string concatenation for file paths, and noted that several functions should be broken into smaller, testable units. The review read like it came from a thoughtful senior developer.

GPT-4o caught the security and performance issues but focused more on suggesting specific fixes (with code) rather than explaining the underlying principles. The review was practical and action-oriented.

Gemini provided a solid review but was less thorough on the subtle issues. It caught the SQL injection and suggested some style improvements but missed the N+1 query problem.

Coding Verdict

For coding tasks, Claude 3.5 Sonnet is my top choice. The code it writes is consistently cleaner, more idiomatic, and better documented. Its explanations teach you something rather than just solving the immediate problem. GPT-4o is a very close second and sometimes produces more creative solutions. Gemini is competent but a step behind the other two for complex coding tasks.

Writing: Different Strengths for Different Tasks

Long-Form Content

I asked each model to write a 1,500-word technical blog post about microservices architecture tradeoffs.

GPT-4o produced solid, well-structured content with good technical accuracy. The writing style was competent but had a "textbook" quality — correct but not particularly engaging. It used transitional phrases effectively and maintained logical flow.

Claude wrote the most engaging piece. The tone was conversational without being informal, with opinions clearly stated and nuanced counterarguments acknowledged. It included a specific anecdote about a hypothetical team migrating from a monolith that made the content feel grounded. The structure was excellent — clear headers, varied paragraph lengths, and a satisfying arc from problem statement to practical advice.

Gemini produced the shortest response (about 1,200 words despite the 1,500-word request) and focused heavily on factual content. The writing was accurate and well-organized but read more like documentation than a blog post.

Email and Professional Communication

For drafting professional emails — responding to a difficult client, writing a project update to stakeholders, composing a salary negotiation email — GPT-4o excels. It finds the right tone consistently, is appropriately diplomatic, and structures information for maximum clarity. Claude also does this well but sometimes errs on the side of being too thorough, producing emails that are longer than necessary. Gemini tends to be too terse for sensitive communications.

Creative Writing

I asked each model to write a short science fiction story (500 words) with a twist ending.

Claude wrote the best story. The prose was evocative, the characters felt distinct, and the twist was genuinely surprising while feeling earned. GPT-4o wrote a competent story with a predictable twist. Gemini's story was the weakest — technically correct but lacking voice and emotional resonance.

Writing Verdict

Claude for long-form and creative writing. GPT-4o for professional communication and structured documents. Gemini is the weakest writer of the three, though it is improving rapidly.

Analysis and Reasoning

Data Analysis

I provided each model with a CSV of quarterly sales data (100 rows) and asked for insights about trends, anomalies, and recommendations.

Gemini 2.0 Pro dominated this category. Its analysis was the most comprehensive, identifying seasonal patterns, year-over-year growth rates, correlation between marketing spend and sales, and a subtle anomaly in Q3 where a pricing change affected conversion rates. Google's strength in data and analytics shows clearly here.

GPT-4o provided solid analysis with accurate calculations and useful visualizations (described in text). It caught the major trends but missed the Q3 pricing anomaly.

Claude identified similar patterns to GPT-4o and provided thoughtful strategic recommendations based on the data. It asked clarifying questions about the data context, which was actually helpful — it wanted to know the industry, company size, and goals before making recommendations.

Logical Reasoning

I tested with a series of increasingly complex logic puzzles, including syllogisms, constraint satisfaction problems, and probability questions.

All three models handled basic and intermediate logic correctly. At the hardest level (multi-step constraint satisfaction with 6+ variables), Claude was the most consistent, GPT-4o was correct about 70% of the time, and Gemini was correct about 60% of the time. However, Gemini's chain-of-thought reasoning was often the most transparent — you could follow its logic even when it reached an incorrect conclusion.

Document Summarization

I fed each model a 15,000-word academic paper on climate policy and asked for a structured summary.

Gemini handled this best, thanks to its massive context window and strong comprehension. The summary was accurate, well-organized, and faithfully represented the paper's arguments and evidence. Claude also produced an excellent summary — arguably more readable than Gemini's — but would occasionally paraphrase in ways that subtly shifted the original meaning. GPT-4o's summary was the shortest and most focused, which could be a feature or a bug depending on your needs.

Analysis Verdict

Gemini for data analysis and document summarization. Claude for logical reasoning and strategic thinking. GPT-4o is solid across the board but does not lead in any analysis sub-category.

Math and Quantitative Tasks

I tested with problems from calculus, linear algebra, statistics, and competition mathematics (AMC/AIME level).

Standard Math

All three models handle undergraduate-level math well. Derivative calculations, matrix operations, hypothesis testing — they all get these right consistently. The differences emerge at competition difficulty or when problems require creative problem-solving rather than applying known techniques.

Competition Mathematics

For AMC 12/AIME-level problems:

GPT-4o solved about 70% correctly, with clean step-by-step solutions
Claude solved about 65% correctly, with more detailed explanations of the reasoning process
Gemini solved about 75% correctly, showing the strongest raw mathematical capability

For Olympiad-level problems (where creative insight matters more than computation), all three models struggled, but GPT-4o showed the most creative problem-solving approaches.

Math Verdict

Gemini for raw mathematical computation and standard problems. GPT-4o for creative mathematical reasoning. All three are reliable for everyday math needs.

Multilingual Capabilities and Indian Languages

This is particularly relevant for Indian users who frequently switch between English and regional languages.

Hindi

I tested all three models with Hindi text — comprehension, generation, translation from English, and code-switching (mixing Hindi and English, which is how many Indians naturally communicate).

Gemini was the strongest for Hindi. Its responses in Hindi were more natural and grammatically correct, likely because Google has invested heavily in Indian language training data. It handled Hinglish (Hindi-English code-switching) naturally — understanding "mujhe ek Python script chahiye jo CSV file read kare" without any confusion.

GPT-4o handled Hindi well but occasionally produced overly formal, Sanskritized Hindi that no Indian actually speaks in conversation. Translation quality was good but not as natural as Gemini.

Claude was the weakest in Hindi. It understood Hindi inputs correctly but sometimes responded in English unless specifically asked to respond in Hindi. When it did respond in Hindi, the grammar was correct but the phrasing felt translated rather than native.

Tamil and Telugu

For South Indian languages, Gemini again led, followed by GPT-4o. Claude's performance dropped more noticeably for Dravidian languages. If you primarily work in Indian languages, Gemini is the clear choice.

Translation Quality (English to Hindi)

Test Text	GPT-4o	Claude	Gemini
Technical documentation	Good	Adequate	Excellent
Conversational text	Good	Good	Excellent
Legal/formal text	Very Good	Good	Very Good
Idiomatic expressions	Moderate	Moderate	Good

Multilingual Verdict

Gemini is the best model for Indian language tasks, and it is not particularly close. If multilingual capability matters to you, this should weigh heavily in your decision.

Privacy Considerations

This matters more than most users think about, especially for professional use.

OpenAI (GPT-4o): By default, your conversations may be used to train future models. You can opt out via Settings > Data Controls > "Improve the model for everyone." API usage is not used for training. OpenAI stores conversations for 30 days for abuse monitoring.

Anthropic (Claude): Similar to OpenAI — free-tier conversations may be used for training. You can opt out. API usage is not used for training. Anthropic has been more transparent about their data practices and publishes usage policies clearly.

Google (Gemini): Google's data practices are more complex because of their advertising business. Free-tier Gemini conversations are reviewed by human raters and may be used for training. With a paid Google One AI Premium plan, Google states they do not use your data for training. However, if you use Gemini through Google Workspace, different policies apply.

For sensitive professional work — proprietary code, confidential business data, legal documents — the safest approach is using API access with data retention disabled, regardless of which model you choose. The free consumer tiers of all three models should be treated as public inputs.

Free Tiers Compared

Feature	ChatGPT Free	Claude Free	Gemini Free
Model Access	GPT-4o (limited), GPT-3.5	Claude 3.5 Sonnet (limited)	Gemini 2.0 Flash, Gemini Pro (limited)
Daily Limits	~15 GPT-4o messages	~20 messages	Generous (varies)
File Upload	Yes (images)	Yes (images, docs)	Yes (images, docs, audio, video)
Code Execution	Yes (Code Interpreter)	No	Yes (via Google Colab integration)
Web Search	Yes	No (usually)	Yes (grounded in Google Search)
Context Window	128K	200K	1M+

Gemini offers the most generous free tier. You get a lot of usage before hitting limits, and the integration with Google services (Drive, Gmail, Docs) adds practical value if you are in the Google ecosystem.

ChatGPT's free tier is more restricted on GPT-4o messages but includes Code Interpreter, which is genuinely useful for data analysis and coding tasks.

Claude's free tier is the most limited in terms of daily message count, but when you do use it, you get the full power of Claude 3.5 Sonnet.

API Pricing for Developers

If you are building applications that use AI, pricing matters enormously. Here is the cost comparison for processing 1 million tokens (roughly 750,000 words):

Model	Input Cost/M	Output Cost/M	Speed
GPT-4o	$2.50	$10.00	Fast
GPT-4o mini	$0.15	$0.60	Very Fast
Claude 3.5 Sonnet	$3.00	$15.00	Fast
Claude 3.5 Haiku	$0.25	$1.25	Very Fast
Gemini 2.0 Flash	$0.10	$0.40	Very Fast
Gemini 2.0 Pro	$1.25	$5.00	Fast

Gemini 2.0 Flash is absurdly cheap and fast, making it the obvious choice for high-volume applications where top-tier quality is not critical. For applications that need the best possible output, Claude 3.5 Sonnet is the most expensive but produces the highest quality for coding and writing tasks.

For most applications, the smart play is to use a cheaper model (GPT-4o mini, Claude Haiku, or Gemini Flash) for routine tasks and route complex queries to the more capable (and expensive) models. This hybrid approach can reduce costs by 70-80% while maintaining quality where it matters.

Real Test Results: Head-to-Head Comparison

Here is a summary of my testing across all categories, rated on a 1-10 scale based on output quality:

Task Category	GPT-4o	Claude 3.5	Gemini 2.0 Pro
Bug Fixing	9	9.5	7.5
Feature Implementation	8.5	9	8
Code Review	8	9.5	7
Long-Form Writing	8	9	7
Professional Emails	9	8.5	7.5
Creative Writing	8	9	6.5
Data Analysis	8	8	9.5
Logical Reasoning	8	9	7.5
Summarization	8	8.5	9
Math (Standard)	8.5	8	9
Math (Competition)	8	7.5	8.5
Hindi/Indian Languages	7.5	6.5	9.5
Conversation/Chat	9	8.5	8

My Recommendations

For Indian Developers

Primary model: Claude 3.5 Sonnet. The coding assistance is the best available. Code reviews, bug fixes, architectural advice, and explanation quality are consistently superior. Use it as your daily coding companion.

Secondary model: GPT-4o. Keep ChatGPT around for its Code Interpreter (useful for quick data analysis and prototyping), web browsing (for current information), and professional writing tasks.

For Indian language tasks: Gemini. If you work with Hindi, Tamil, Telugu, or other Indian languages regularly, Gemini is the clear choice.

For Students

Use Gemini's free tier as your primary AI tool. It is the most generous, handles research well, integrates with Google services you already use, and excels at Indian languages.

For Businesses Building AI Products

Use Gemini 2.0 Flash for high-volume, cost-sensitive applications. Route complex queries to Claude 3.5 Sonnet or GPT-4o based on the specific task.

For Casual Users

ChatGPT remains the most well-rounded and easiest to use. The conversational experience is polished, the free tier is useful, and the ecosystem of plugins and integrations is the largest.

The Honest Bottom Line

There is no single "best" AI model. Anyone telling you otherwise is either selling something or has not tested them properly. The three models have distinct personalities and strengths:

GPT-4o is the well-rounded generalist — good at everything, best at nothing specific
Claude 3.5 Sonnet is the thoughtful specialist — best for coding, writing, and nuanced reasoning
Gemini 2.0 is the data powerhouse — best for analysis, multilingual tasks, and cost efficiency

The smartest approach is to use all three strategically. They are tools, not religions. Pick the right tool for each job, and you will get dramatically better results than sticking with one model out of loyalty or habit. The AI landscape is evolving rapidly — the rankings in this comparison may shift within months as each company ships updates. Stay flexible, keep experimenting, and let the quality of the output guide your choices.