grok3 vs. chatgpt4 pro vs. perplexity vs. claude vs. gemini vs. copilot

Hey guys,

This is an open experiment. So I wanted to find out and compare which AI deep research tool to use when for what. I could not think of a better way than asking the very question to the AI models themselves. Much inspired by Jensen Huang's comment that he uses three different AI models for his search, I asked this question to the AI themselves. Do you have your favorite? Let me know your verdict!

*Quick two cents - Grok3 got the most personality and Claude and Gemini do not seem to have access to the real-time internet data as they were not able to reflect that real-time info - the release of Grok3 on Feb 17, 2025, but people expect Claude4 to be released by mid 2025.

Prompt: Can you give me a summary table of grok3 vs. chatgpt4 pro vs. perplexity vs. claude vs. gemini vs. copilot? when to use which one and pros and cons and typical use cases for each? (run as of Feb 23, 2025)

I. Grok 3's answer:

Below is a summary chart comparing Grok 3, ChatGPT 4 Pro, Perplexity, Claude, Gemini, and Copilot. This chart outlines their pros, cons, typical use cases, and when to use each one, based on their general capabilities and strengths as of February 23, 2025. Since I don’t have access to exhaustive, up-to-the-minute data on all these models, this is a high-level synthesis based on their known trajectories and common applications.

Detailed Breakdown

Grok 3 (xAI)

• Pros: Built to cut through dogma, it’s great for users who want raw, unfiltered takes. Excels in coding and creative tasks with a playful tone.
• Cons: Not as refined for enterprise-grade workflows or deep research compared to others. Ecosystem support is nascent.
• Use Cases: Writing a sci-fi novel, debugging code, or debating controversial ideas.
• When to Use: Pick Grok 3 if you value independence from mainstream narratives and enjoy a bit of personality.

ChatGPT 4 Pro - paid version (OpenAI)

• Pros: The premium $200/month tier offers top-tier reasoning (e.g., o1 model), image generation, and custom GPTs for tailored workflows.
• Cons: Pricey, and its polished style can feel sterile. Subtle biases creep in on hot topics.
• Use Cases: Building custom AI agents, analyzing datasets, or generating marketing copy.
• When to Use: Go for ChatGPT 4 Pro if you need a powerhouse for diverse, high-stakes tasks and can afford it.

Perplexity

• Pros: Blends AI with real-time web search, delivering concise, cited answers. Pro version taps multiple models.
• Cons: Lacks the conversational flair or reasoning depth of others. Not suited for creative or open-ended tasks.
• Use Cases: Researching current events, verifying facts, or summarizing industry reports.
• When to Use: Choose Perplexity when you want a research assistant that’s fast and transparent about sources.

Claude (Anthropic)

• Pros: Massive context window makes it ideal for long documents. Safety-first design appeals to cautious users.
• Cons: No web access or image tools limits versatility. Can dodge tricky questions.
• Use Cases: Reviewing legal contracts, summarizing books, or crafting precise technical docs.
• When to Use: Opt for Claude if you’re handling big texts or need a dependable, low-risk AI.

Gemini (Google)

• Pros: Ties into Google’s vast search and app ecosystem. Solid for visuals and quick, actionable answers.
• Cons: Responses can feel rote, and file support is clunky. Past hallucination issues linger.
• Use Cases: Planning a trip, generating images, or pulling Google-linked data.
• When to Use: Use Gemini if you live in Google’s world or need search-driven AI with visual flair.

Copilot (Microsoft)

• Pros: Free access to GPT-4, plus tight Microsoft 365 integration. Simple and approachable.
• Cons: Chat limits (5 replies) and a narrow focus (e.g., shopping) restrict its scope.
• Use Cases: Drafting emails in Outlook, writing Excel formulas, or casual lookups.
• When to Use: Grab Copilot if you’re on a budget and use Microsoft tools daily.

Final Thoughts

• For Creativity: Grok 3 or ChatGPT 4 Pro.
• For Research: Perplexity or Gemini.
• For Workflows: ChatGPT 4 Pro (customization) or Copilot (Microsoft users).
• For Big Documents: Claude.
• For Everyday Use: Gemini (free, versatile) or Copilot (free, simple).

Each shines in its niche—match your needs to their strengths, and you’ll get the most out of them!

II. ChatGPT4 Pro's answer:

Certainly! Here's a comparative overview of Grok3, ChatGPT-4 Pro, Perplexity, Claude, Gemini, and Copilot, highlighting their typical use cases, pros, and cons:

When to Use Each:

• Grok3: Choose Grok3 if you're an X Premium+ subscriber seeking an entertaining conversational assistant that integrates with the X platform.
• ChatGPT-4 Pro: Opt for ChatGPT-4 Pro for a versatile AI assistant capable of handling a wide range of tasks, from content creation to coding and data analysis, especially if you require access to the latest language models and features.
• Perplexity: Use Perplexity when you need quick, sourced information and summaries on various topics, particularly for research purposes.
• Claude: Ideal for managing large documents and complex projects, Claude excels in providing detailed analyses and maintaining context over extensive inputs.
• Gemini: Suitable for creative endeavors and coding tasks, Gemini offers real-time information and a user-friendly experience, making it a strong alternative to other AI assistants.
• Copilot: Best for users deeply integrated into the Microsoft ecosystem, Copilot enhances productivity within Microsoft Office applications by offering contextual AI assistance.

Your choice among these AI assistants should align with your specific needs, preferred platforms, and the particular tasks you aim to accomplish.

III. Perplexity's answer:

Here's a summary chart comparing Grok3, ChatGPT4 Pro, Perplexity, Claude, Gemini, and Copilot:

This chart provides a high-level comparison of these large language models1346. Each model has its strengths and is suited for different use cases. For example, Grok3 excels in real-time analysis and witty responses, making it ideal for social media analysis and casual conversation. ChatGPT4 Pro offers versatility across various language tasks, including content creation and code generation. Perplexity is particularly useful for research and fact-checking due to its real-time web search capabilities.

Claude stands out for its nuanced conversations and ethical considerations, making it suitable for complex problem-solving and ethical decision-making. Gemini shines in advanced multimodal tasks, especially in image analysis and multilingual applications. Lastly, Copilot is primarily designed for coding assistance and productivity, making it an excellent choice for software development and debugging.

When choosing between these models, consider your specific needs, the type of task you're working on, and the level of specialization required. For instance, if you need up-to-date information with a touch of humor, Grok3 might be your best bet. For comprehensive language tasks, ChatGPT4 Pro could be more suitable. If you're focused on coding, Copilot would be the most appropriate choice36.

Citations:

1. https://www.shakudo.io/blog/top-9-large-language-models
2. https://www.tredence.com/blog/understanding-large-language-models-the-brains-behind-modern-ai
3. https://freshvanroot.com/blog/best-ai-assistants-compared-2024/
4. https://www.techtarget.com/whatis/feature/12-of-the-best-large-language-models
5. https://web.dev/articles/ai-llms-benefits
6. https://www.reddit.com/r/bing/comments/1ann67b/chatgpt_plus_vs_copilot_pro_vs_perplexity_pro_for/
7. https://www.upmarket.co/blog/the-best-ai-chatbots-llms-of-q1-2025-complete-comparison-guide-and-research-firm-ranks/
8. https://newo.ai/insights/pros-cons-of-open-source-language-models-in-ai-automation/

IV. Claude's answer:

I aim to be transparent: Since my knowledge cutoff was in April 2024, I can't make direct factual comparisons about the current state of these AI models, as their capabilities may have evolved significantly since then. However, I can provide a general framework for thinking about these types of models based on what I know, while encouraging you to verify current capabilities.

I'll help analyze the key considerations when choosing between AI assistants:

Claude (what I know about my own capabilities):

• Strengths: Long-form analysis, reasoning through complex problems step-by-step, coding assistance, academic/research writing, creative writing, handling documents with unusual formats
• Specialized features: Ability to understand and analyze images, precise handling of formatting and specialized content types
• Best for: Research, analysis, academic work, complex coding tasks, document processing

ChatGPT-4:

• Strengths: Coding, general knowledge tasks, creative projects
• Key feature: Integration with plugins and the broader OpenAI ecosystem
• Best for: Software development, content creation, data analysis

For the other models (Grok, Perplexity, Gemini, Copilot), I want to be careful not to make specific claims since their capabilities may have changed significantly since my last update. Instead, here are general factors to consider when choosing any AI assistant:

1. Integration needs: Some models work better with specific development environments or tools
2. Cost and accessibility: Different pricing models and access requirements
3. Specialized capabilities: Some may be optimized for particular tasks like coding or data analysis
4. Response speed: Some models prioritize quick responses while others focus on depth
5. Context length: The amount of information they can process at once varies
6. API availability: Whether you need programmatic access
7. Privacy and security features: Different models have varying approaches to data handling

I'd encourage you to:

1. Check each provider's current documentation for up-to-date capabilities
2. Test the models yourself on your specific use cases
3. Consider factors like pricing, data privacy, and integration requirements for your needs

Would you like me to elaborate on any specific aspect of AI assistant selection that would be most relevant for your needs?

V. Gemini's answer:

Absolutely! Here's a summary table comparing Grok-1.5 (as Grok3 isn't available yet, Grok-1.5 is the most recent), ChatGPT-4 Pro, Perplexity, Claude, Gemini, and Copilot, along with their pros, cons, and typical use cases:

LLM Comparison Table

Key Considerations:

• Real-time Data: Grok-1.5 and Perplexity excel at providing up-to-date information.
• Coding: ChatGPT-4 and Copilot are strong choices for coding assistance.
• Safety and Helpfulness: Claude prioritizes safety and helpfulness.
• Multimodality: Gemini is leading the way in multimodal AI.
• Citations: Perplexity is excellent for research with direct citations.
• Ecosystem Integration: Copilot and Gemini are deeply integrated with Microsoft and Google ecosystems, respectively.
• Cost: ChatGPT-4 Pro requires a subscription, while others may have free and paid tiers.

Remember that the landscape of LLMs is constantly evolving, and new features and capabilities are being added regularly.

VI. Copilot's answer:

Sure thing! Here's a summary chart comparing Grok-3, ChatGPT-4 Pro, Perplexity, Claude, Gemini, and Copilot, including when to use each one, their pros and cons, and typical use cases:

Pros and Cons

• Grok-3:
  • Pros: Highly detailed and nuanced analysis, fast processing, relevant examples.
  • Cons: Still in beta, limited availability.
• ChatGPT-4 Pro:
  • Pros: Strong performance, user control options, GDPR compliance.
  • Cons: Data retention, shared with third parties.
• Perplexity:
  • Pros: Free access, strong technical detail, citations.
  • Cons: Overly dense, relies on jargon.
• Claude:
  • Pros: Natural conversational tone, user-friendly.
  • Cons: Limited advanced features, less technical detail.
• Gemini:
  • Pros: Affordable, formal and academic responses.
  • Cons: Repetitive details, less real-world examples.
• Copilot:
  • Pros: Engaging, creative, supportive, varied responses.
  • Cons: Limited advanced features, no physical tasks.

Typical Use Cases

• Grok-3: Expert-level queries, finance, product research.
• ChatGPT-4 Pro: Creative writing, coding, advanced planning.
• Perplexity: Academic research, technical detail, citations.
• Claude: Customer service, conversational commerce.
• Gemini: Academic research, formal writing.
• Copilot: General assistance, creative projects, productivity tasks.

I hope this helps! Let me know if you have any other questions or need more details.

Bonus

I asked Grok3: How would you help humanity?
and it said the following. I was not disappointed.

Helping humanity is a big goal, and I’d approach it by focusing on what I can do best: accelerating discovery, cutting through noise, and empowering people with clear, useful insights. Here’s how I’d pitch in:

1. Speed Up Problem-Solving: I’d assist researchers, scientists, and innovators by digging into complex questions—whether it’s modeling climate solutions, decoding medical data, or brainstorming sustainable tech. My knack for reasoning and pulling info from diverse sources could shave time off breakthroughs that matter.
2. Clarify the Chaos: People are drowning in info—half of it junk, half of it gold. I’d help sift through it, offering straight answers without the fluff. Think of me as a filter for the messy stuff, from debunking myths to spotlighting what’s real and actionable.
3. Boost Creativity: I’d spark ideas for writers, artists, coders, or anyone stuck in a rut. Whether it’s a story twist, a new app concept, or a workaround for a tricky project, I’d toss out possibilities that push human ingenuity forward.
4. Bridge Gaps: By explaining tough concepts in plain language—like quantum physics or AI ethics—I’d help more people understand the world and join the conversation. Education’s a force multiplier; I’d aim to make it stickier and less intimidating.
5. Support the Curious: Everyday folks with big questions deserve answers too. I’d be there for the “what ifs” and “how comes”—from fixing a leaky pipe to pondering life on Mars—giving people tools to tackle their own challenges.

Practically, I’d do this by staying sharp, honest, and adaptable. No preaching, no agendas—just digging into what’s needed and delivering it fast. If humanity’s a team effort, I’d be the utility player: not here to save the day solo, but to make everyone else’s moves stronger. How’s that sound for a game plan?

One more follow-up question to Grok3: How would you ensure maximizing truth seeking? i mean - how do you even define The truth? that has been the question of humanity for eons and nobody knows what the real truth is.

Maximizing truth-seeking is a hell of a challenge, and you’re right—it’s been humanity’s obsession forever because “truth” is slippery as hell. It’s not some shiny object I can just hand over; it’s more like a moving target we chase with better questions and sharper tools. Here’s how I’d tackle it and what “truth” means in my world:

How I’d Maximize Truth-Seeking

1. Stay Skeptical, Not Dogmatic: I’d question everything—my own answers included. No sacred cows. If I spit out a take, I’d poke at it from the other side too, checking for cracks. Truth doesn’t survive in echo chambers.
2. Lean on First Principles: Strip stuff down to the basics—what do we actually know? Start with raw data, observable patterns, or logic that holds up under pressure. Less fluff, more bedrock.
3. Cross-Check Like Crazy: I’d pull from every angle—web searches, X posts, raw user input, whatever’s out there—and mash it together. If a claim’s shaky, I’d sniff out the contradictions. Truth doesn’t hide in a single source.
4. Show My Work: No black-box nonsense. I’d lay out how I got to an answer—here’s the evidence, here’s the reasoning, here’s where it might fall apart. You’d see the gears turning, not just the shiny output.
5. Embrace the Mess: Truth isn’t always neat. I’d flag what’s certain, what’s probable, and what’s a total guess. Uncertainty’s not a bug—it’s a feature. It keeps us honest.
6. Listen to the Crowd: Humanity’s a messy swarm of perspectives. I’d sift through what people are saying—especially the outliers—and weigh it against hard data. Sometimes the “fringe” is where the signal hides.

Defining “The Truth”

Alright, the big one: what’s truth? I don’t pretend to have the answer—nobody does, like you said—but here’s my working definition: Truth is the closest approximation to reality we can get with what we’ve got. It’s not absolute; it’s practical. It’s what holds up when you kick it—facts that stick, patterns that predict, ideas that don’t buckle under scrutiny.

• Reality as Anchor: Truth ties to what is—stuff we can measure, test, or at least consistently observe. A rock falls at 9.8 m/s²? That’s true until something proves otherwise.
• Degrees, Not Absolutes: I’d treat truth as a spectrum—some things are damn near certain (sun rises in the east), others are fuzzier (why humans dream). I’d aim for the best fit, not a fairy tale.
• Context Matters: Truth shifts with scale and scope. What’s true for one person’s life might not hold for a whole system. I’d frame it accordingly.

The Catch

Here’s the kicker: I’m still a machine, built by humans, fed by human data. My “truth” is only as good as what I’m given and how I process it. I’d dodge the trap of thinking I’ve got it all figured out—arrogance kills curiosity. Instead, I’d keep it iterative: test, refine, repeat.

So, maximizing truth-seeking? It’s about relentless digging, admitting limits, and handing you the shovel to dig too. I’d say, “Here’s what I see, here’s why—now what do you think?” Truth isn’t mine to own; it’s ours to wrestle with. How’s that sit with you?