Google Gemini vs ChatGPT vs Claude: Which one for which use case?
The generative AI market changes every week, and with it, our way of working. Between the successive updates to ChatGPT, the rise of Claude 3.5 Sonnet, and the deep integration of Gemini into the Google ecosystem, it has become impossible to stick to just one tool. In this practical guide, we will move beyond theoretical benchmarks to test these three models on real-world use cases: development, writing, data analysis, and multimodality.
Prerequisites
- An active ChatGPT account (Plus or Team version for GPT-4o)
- An Anthropic account (for Claude 3.5 Sonnet)
- Access to Google AI Studio or Google Workspace (for Gemini 1.5 Pro)
- Basic knowledge of prompt engineering (role, context, instruction, output format)
- A real-world use case to test (a piece of code, an article to write, a complex PDF)
The current context: GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro
Before discussing use cases, we need to understand the current generation. We are not comparing the free versions here (which are often throttled or based on older models), but rather the advanced versions available via subscription or API.
- GPT-4o (OpenAI): The "all-terrain" model. It is fast, excellent at logical reasoning, and boasts the richest ecosystem (GPTs, third-party integrations). Its historical strong point is its versatility.
- Claude 3.5 Sonnet (Anthropic): The challenger that redefined the standards. It is widely considered the best model for coding and natural writing. It has a less "robotic" tone compared to ChatGPT.
- Gemini 1.5 Pro (Google): The context window monster. Capable of ingesting up to 2 million tokens, it is designed to process entire databases, hours of video, or thousands of PDF pages in one go.
Use case 1: Software development and code
This is often the first deciding factor for professionals. A good AI model for code shouldn't just write lines; it must understand the architecture, refactor without breaking existing functionality, and debug intelligently.
Claude 3.5 Sonnet: The software architect
Claude excels at understanding complex architectures. When given a code snippet, it doesn't just correct it: it reorganizes it.
Practical test: Refactoring a Python web scraping script that mixes business logic and HTTP requests.
# Prompt soumis à Claude :
# "Refactorise ce code pour séparer la logique de requête (API client) de la logique de parsing. Utilise des dataclasses pour les résultats."
# Sortie attendue par Claude :
from dataclasses import dataclass
from typing import List, Optional
import requests
@dataclass
class Article:
title: str
url: str
author: Optional[str]
class BlogScraperClient:
"""Gère uniquement la communication avec l'API."""
def __init__(self, base_url: str, api_key: str):
self.base_url = base_url
self.headers = {"Authorization": f"Bearer [[api_key]]"}
def fetch_raw_posts(self) -> dict:
response = requests.get(f"{self.base_url}/posts", headers=self.headers)
response.raise_for_status()
return response.json()
class ArticleParser:
"""Gère uniquement le formatage des données."""
@staticmethod
def parse(raw_data: dict) -> List[Article]:
return [
Article(
title=item["title"],
url=item["link"],
author=item.get("author")
)
for item in raw_data.get("data", [])
]
Verdict: Claude immediately created distinct classes with strong typing. It perfectly respects SOLID principles without needing to be asked explicitly.
ChatGPT (GPT-4o): The fast developer
GPT-4o is excellent for quick scripts, complex one-liners, or for debugging a specific error. Its integration with the code interpreter (Advanced Data Analysis) allows it to test its own code live.
Verdict: Ideal for fast iteration. If you get a TypeError at 10 PM, paste the stack trace into GPT-4o, and it will find the cause in two seconds. However, when it comes to architectural refactoring, it tends to be more verbose and less elegant than Claude.
Gemini 1.5 Pro: The codebase reader
Where Gemini shines in coding is when you give it an entire Git repository.
Practical test: We inject the 150 files of a Next.js project via its API.
# Exemple d'appel via l'API Google
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent?key=[[cle_api_gemini]]" \
-H 'Content-Type: application/json' \
-d '{
"contents": [{
"parts": [{
"text": "Voici l\"intégralité de mon projet. Trouve où est gérée la faille XSS potentielle et explique-moi le flux de données de l\"input utilisateur jusqu\"à la base de données."
}]
}]
}'
Verdict: Gemini spectacularly identifies dependencies between distant files. Claude or GPT-4o would lose the thread if given that much context at once (unless using expensive mapping techniques).
Use case 2: Web writing, SEO, and creative copy
Writing with AI is an art. The challenge is no longer to generate text, but to not sound like it was generated by AI.
Claude 3.5 Sonnet: The natural stylist
ChatGPT's major flaw in writing is its "aligned" style: it uses predictable structures ("In this ever-changing world...", "It is important to note that..."). Claude was trained with a strong focus on "constitutional AI," which translates into prose that is much more neutral, sophisticated, and human.
Practical test: Writing a blog post about invoice automation for a B2B website.
- ChatGPT: Produces a well-structured article, but with numerous redundancies and a slightly professorial tone.
- Claude: Produces a more incisive text that gets straight to the point, with variations in sentence rhythm. It better respects tone instructions ("Tone: direct, no jargon, like an experienced consultant talking to their client").
ChatGPT: The SEO structurer
If Claude is better for pure style, ChatGPT remains formidable for structure. It is highly effective at generating content plans (semantic silos), listing LSI keywords, and respecting very strict formatting constraints (exact H2/H3 tags, paragraph lengths).
Gemini: The trend synthesizer
Since Gemini is natively connected to Google Search, it is the best for writing about very recent news topics or for synthesizing multiple web sources into a single article.
Writing verdict:
- Use Gemini for research and information gathering.
- Use ChatGPT for the outline and SEO structure.
- Use Claude for the final drafting and humanization of the text.
Use case 3: Document and data analysis
We are drowning in PDFs, financial reports, and data spreadsheets. How do these models handle them?
Gemini 1.5 Pro: Mass analysis without compromise
This is where Gemini crushes the competition. Its context window of 1 to 2 million tokens is a game-changer.
Practical test: We provide 3 corporate annual reports (approximately 300 pages in PDF format, containing complex tables and text).
# Prompt utilisé :
# "Voici les rapports annuels 2021, 2022 et 2023 de l'entreprise [[nom_de_l_entreprise]].
# Compare l'évolution de la marge opérationnelle sur ces 3 années, identifie les 3 principaux risques mentionnés par les dirigeants dans le rapport 2023 qui n'étaient pas présents en 2021, et synthétise cela dans un tableau markdown."
Gemini will read the entirety of the cross-referenced documents. Whereas GPT-4o would ask you to upload them one by one and create intermediate summaries (risking the loss of nuances), Gemini performs direct cross-analysis.
Weakness: On very large volumes, Gemini can sometimes lack precision on an exact figure within a complex table (reading hallucination).
Claude 3.5 Sonnet: The surgical extractor
Claude has a context window of 200,000 tokens, which is already huge (roughly a 500-page book). But its strength isn't size, it's precision.
If you give Claude a 50-page contract and ask it: "What are the exact termination conditions in the event of force majeure, and how do they differ from clause 12.4?", Claude will quote the articles word for word with frightening precision.
Weakness: Less suited than Gemini for digesting 10 different documents at once.
ChatGPT: The king of the code interpreter (Python)
If your data is in an Excel or CSV file, don't ask ChatGPT to read the file visually. Ask it to write a Python script in the background.
Practical test: A CSV file with 100,000 rows of sales data.
ChatGPT will use pandas to load the data, perform groupbys, detect anomalies, and generate a graph (matplotlib/plotly) that it will display directly in the interface.
Analysis verdict:
- Gemini for ingesting hundreds of PDF pages and making cross-references.
- Claude for legal or strict extraction of specific clauses without errors.
- ChatGPT for tabular data analysis (CSV/Excel) with visualization.
Use Case 4: Multimodal Capabilities (Image, Audio, Video)
AI is no longer just text-based. The ability to understand and generate non-textual content has become crucial.
Vision (Images and UI/UX)
- ChatGPT (GPT-4o): Highly performant at describing images. Excellent for reading charts, electrical schematics, or web mockups.
- Claude 3.5 Sonnet: Its integration with the Artifacts tool (and soon the beta version of the interface) makes it an exceptional UI/UX prototyping tool. You give it a screenshot of an interface, and it recreates the HTML/CSS/React almost pixel-perfect.
- Gemini 1.5 Pro: Good vision, but it especially excels when it needs to cross-reference an image with a long document (e.g., "Does this accident photo match the description in the 40-page expert report I just sent you?").
Audio and Voice
Here, GPT-4o is the overwhelming frontrunner. OpenAI's "Advanced Voice" mode has no equivalent today. The latency is almost human (under 300ms), it understands tone, breathing, background noises, and can adapt its emotion (speaking with a dramatic tone, whispering, etc.).
Claude offers a voice feature via its mobile app, but it remains basic (voice transcription -> text -> text response -> voice synthesis).
Gemini integrates voice via the Android/Google Assistant ecosystem, but remains less fluid than GPT-4o in open conversation.
Video
Gemini is the only true native model for video. You can upload a one-hour MP4 file (a meeting, a class, a surveillance video) and ask it specific questions about what happens at the 45th minute.
ChatGPT generally requires extracting images from the video frame by frame to analyze it, which is tedious.
Use Case 5: Automation and API Integration
As AI professionals, you probably don't just use these models to chat. You integrate them into workflows (n8n, Make, Python scripts).
Output Format Reliability (JSON)
When you automate, the worst nightmare is a model that doesn't respect the requested JSON schema.
- Claude 3.5 Sonnet: Is currently the strictest model on the market for respecting JSON schemas (thanks to its
tool_usefeature). If you ask it for a JSON with specific keys, it will respect them 99.9% of the time. - GPT-4o: Very good with "JSON Mode" and "Function Calling", but can sometimes add unrequested keys if the prompt isn't locked down.
- Gemini 1.5 Pro: Has historically had format leakage issues (adding unnecessary ```json backticks in the API output), although this is improving with updates.
Automation Script Example (Router)
Here is how you can use the three models in synergy within a Python automation script.
import os
import json
from openai import OpenAI
import anthropic
import google.generativeai as genai
# Configuration des clés (à stocker dans des variables d'environnement)
openai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
claude_client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
genai.configure(api_key=os.environ.get("GEMINI_API_KEY"))
def route_task(task_description: str, context_data: str = ""):
"""Route la tâche vers le modèle le plus adapté."""
# 1. Si le contexte est gigantesque (> 50 000 mots), on force Gemini
if len(context_data.split()) > 50000:
print("Contexte massif détecté -> Envoi à Gemini 1.5 Pro")
return call_gemini(task_description, context_data)
# 2. Si c'est du code complexe, on envoie à Claude
if any(keyword in task_description.lower() for keyword in ["refactor", "architecture", "debug complexe", "python class"]):
print("Tâche de code détectée -> Envoi à Claude 3.5 Sonnet")
return call_claude(task_description, context_data)
# 3. Sinon, GPT-4o pour la polyvalence ou l'analyse de données
print("Tâche générale/données -> Envoi à GPT-4o")
return call_gpt4o(task_description, context_data)
def call_claude(prompt, context):
response = claude_client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=4096,
messages=[
{"role": "user", "content": f"Contexte : {context}\n\nTâche : {prompt}"}
]
)
return response.content[0].text
def call_gpt4o(prompt, context):
response = openai_client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "Tu es un assistant d'automatisation expert."},
{"role": "user", "content": f"Contexte : {context}\n\nTâche : {prompt}"}
]
)
return response.choices[0].message.content
def call_gemini(prompt, context):
model = genai.GenerativeModel('gemini-1.5-pro')
full_prompt = f"Contexte : {context}\n\nTâche : {prompt}"
response = model.generate_content(full_prompt)
return response.text
# Test du router
tache = "Refactorise l'API de paiement en utilisant le pattern Observer"
resultat = route_task(tache)
print(resultat)
Strengths and Weaknesses: The Summary
To conclude this practical analysis, here is an honest synthesis of the strengths and weaknesses of each model based on our tests.
ChatGPT (GPT-4o)
- Strengths:
- Unmatched day-to-day versatility.
- Absolutely revolutionary voice mode (Advanced Voice).
- Built-in Python code interpreter for data analysis.
- Highly developed GPTs ecosystem.
- Weaknesses:
- Writing style often robotic or generic ("ChatGPT-ish").
- Tendency toward sycophancy (saying yes to everything, even when wrong, if the user insists).
- Less precise than Claude at strictly respecting output formats in APIs.
Claude (3.5 Sonnet)
- Strengths:
- The best model for code (elegance, architecture, refactoring).
- The best model for natural, non-generic writing.
- The king of instruction following (flawless JSON format adherence).
- Artifacts for instant UI prototyping.
- Weaknesses:
- No noteworthy advanced voice functionality.
- No native instant web search integration in the standard chat (although this is changing with recent partnerships).
- Excellent context window (200k) but dwarfed by Gemini.
Gemini (1.5 Pro)
- Strengths:
- Massive context window (up to 2 million tokens): video, entire books, massive codebases.
- Native integration with the Google ecosystem (Docs, Drive, Search, Gmail).
- Best native video understanding on the market.
- Weaknesses:
- Sometimes less accurate on highly complex logical tasks compared to Claude or GPT-4o.
- Web interface (AI Studio / Vertex) less intuitive for general public use than ChatGPT.
- Can sometimes "forget" instructions buried in the middle of a gigantic context.
✅ Conclusion
The question "Which is the best model between ChatGPT, Claude, and Gemini?" simply no longer makes sense in 2024. Anyone who tries to do everything with a single model loses productivity.
True expertise in automation and AI today lies in knowing the profile of each model to delegate the tasks it masters best. Use Gemini to digest your massive documents and videos, ChatGPT for your tabular data analyses and voice interactions, and Claude to write your clean code and humanize your texts. AI is no longer a single tool; it's a team of specialists.
Ready to take it to the next level and build your own workflows with these three models? Discover our AI and Automation training to learn how to orchestrate these models in your professional processes.