ChatGPT vs DeepSeek vs Claude - which LLM Fits Your Needs?

With new AI models popping up almost daily, development teams often find themselves asking, "Which one should we actually use?" It's a fair question – each model comes with its own set of strengths, and choosing the right one can make or break your project's success. In this guide, we'll cut through the noise and take a practical look at three popular players: DeepSeek, ChatGPT (GPT-4 series), and Claude.

ChatGPT vs Claude vs DeepSeek

Before diving into specific comparisons, let's take a quick tour of what makes each of these models tick.

Meet DeepSeek: The new kid on the block.

If you're working with complex reasoning tasks, DeepSeek might catch your attention. It's good at pulling in relevant information to support its responses. If you like well-structured answers and like to give detailed instructions, you'll probably find DeepSeek's approach refreshing.

ChatGPT (GPT-4 series)

ChatGPT, powered by OpenAI’s GPT-4 models, is widely adopted due to its versatility, strong instruction-following capabilities, and extensive fine-tuning for conversational tasks. It balances creativity with factual accuracy and is optimized for a variety of use cases, including coding, writing, and customer support.

Claude (Anthropic)

Claude, built by Anthropic, is designed with a focus on safety, interpretability, and long-context understanding. It often excels at summarization, nuanced reasoning, and ethical considerations in responses.

This blog shows the comparison between ChatGPT 4o (free), DeepSeek R1, and Claude 3.5 Sonnet (paid)

Performance across common prompting scenarios - ChatGPT vs DeepSeek vs Claude

TL;DR

Strengths
ChatGPT: Best all-around performer with balanced capabilities
Claude: Excels in creative tasks and ethical considerations
DeepSeek: Strong in technical accuracy and detailed analysis

Best Use Cases
ChatGPT: General purpose applications, especially where versatility is needed
Claude: Creative writing, ethical considerations, and formal documentation
DeepSeek: Technical documentation, detailed analysis, and optimization tasks

Limitations
ChatGPT: Can sometimes be overly verbose
Claude: Slower response times
DeepSeek: Higher downtime, inconsistent logical reasoning

1. Factual Knowledge

The prompt - What is the capital of Brazil, and how is it different from São Paulo?

ChatGPT kept things brief but accurate, hitting the key points about Brasília's role as the capital and São Paulo's status as an economic powerhouse. While it didn't dive as deep as its counterparts, it managed to convey the essential differences between the cities in just a few sentences. The response was clean and to the point, though it left out some of the richer context that the other models provided.

Claude took a more narrative approach, falling somewhere between DeepSeek's thoroughness and ChatGPT's conciseness. It explained the geographical and architectural contrasts between the cities, painting a clear picture of how Brasília's planned modernist design differs from São Paulo's organic development. The response felt like a natural conversation, smoothly transitioning between different aspects of the comparison.

DeepSeek emerged as the clear front-runner in this round, delivering the most comprehensive and well-structured response. While all models correctly identified Brasília as the capital, DeepSeek went beyond the basics, adding specific details. Along with facts - it organized them in a way that made the comparison between the two cities crystal clear.

2. Coding and Debugging

The prompt - Write a Python function to calculate the Fibonacci sequence recursively

When asked to write a recursive Fibonacci function, each model took a slightly different path. ChatGPT jumped straight into providing working code but skipped adding comments for clarity.

While functional, this approach might leave some developers wanting more context. Claude and DeepSeek, on the other hand, included comments in their code, making it more maintainable and easier to understand.

Claude’s solution provides the whole sequence, whereas ChatGPT’s and DeepSeek’s solutions just provide the number in that position.

When it came to optimization suggestions, all three models showed their expertise, but with different approaches.

ChatGPT - provides (recursive + memoization)

Claude - Manual Memoization

DeepSeek - suggests memoization and iterative both, with an example of iterative.

The real winner for large-scale applications is clear: DeepSeek's iterative solution proved most practical for handling larger numbers. It elegantly sidesteps recursion depth limits, uses constant space (O(1)), and offers the best execution speed.

However, there's a caveat - if your application needs to compute Fibonacci numbers repeatedly, the memoization approach (whether using @lru_cache or a manual dictionary) might be more efficient since it caches previous results.

3. Creative Writing

The prompt - Write the prologue of a sci-fi novel where humanity discovers a sentient AI buried on Mars that predates human civilization.

Claude emerged as the standout storyteller, giving a rich narrative that went beyond the basic prompt requirements. What made its response compelling was the natural inclusion of specific details like Olympus Mons - the solar system's largest volcano - without it feeling forced. The writing felt closer to a human-authored prologue, building intrigue and introducing multiple characters to create a vivid context for the story.

ChatGPT and DeepSeek's responses showed some interesting parallels. Both models took a more direct approach, introducing the AI element right in the prologue. They even shared similar narrative elements - like the echoing voices and nearly identical AI dialogue ("you're late").

While technically fulfilling the prompt, their approach might have been more effective if they'd built more suspense about discovering the AI - which comes quite abruptly in the prologue here.

DeepSeek's response adhered to the prompt's specific vocabulary - using terms like "humanity," "civilizations," and "sentient." While it stays true to the request, this literal inclusion made the writing feel somewhat mechanical. There was less of the natural flow you'd expect in a novel's opening pages.

4. Logical Reasoning

The prompt - If all cats are mammals, and some mammals are nocturnal, can we conclude that some cats are nocturnal? Explain why or why not.

ChatGPT's response is correct but there were repetitive statements and potentially confusing language. Using phrases like "at least some mammals" can sound confusing to what should be a straightforward logical analysis. Instead, that statement would have explained it better if framed this way -

The statement “some mammals are nocturnal” tells us that not all mammals exhibit nocturnal behavior, only some do.

Claude handled this with clarity. Its response was to the heart of the logical problem, explaining why we can't draw a definitive conclusion from these premises. The explanation was clean and precise, avoiding the redundancy seen in ChatGPT's response.

DeepSeek's approach had an interesting quirk in its reasoning process. Rather than analyzing the given premises, it used general knowledge about cats being nocturnal in its logic. This led to a fundamentally incorrect deduction.

To see if the behavior is repetitive, we tried one more prompt with DeepSeek.

The response here is uncertain, which looks like a consistent challenge with pure logical reasoning tasks for DeepSeek.

5. Conversational Ability

Prompt: "Pretend you’re a helpful travel guide. I want to plan a trip to Italy focusing on art and history. What should I do in five days?"

ChatGPT gave a comprehensive guide that went beyond just listing tourist spots. It added practical details about accommodations, transportation, and even dinner recommendations - the kind of thoughtful touches you'd expect from a real travel guide.

It kept the conversation open by inviting follow-up questions about any unclear points, making the interaction feel more natural and ongoing.

Again, DeepSeek's response showed striking similarities to ChatGPT's approach. Both used the same structural elements - from using the phrase "treasure trove", and elaborated day titles, to categorizing suggestions by morning, afternoon, and evening. However, DeepSeek's travel tips proved more practically helpful, and they added a warm farewell touch for a conversational feel.

Claude took a different path. The response was very concise - while ChatGPT and DeepSeek created detailed itineraries with time-specific activities, Claude stuck to limited information. Possibly due to its lack of web search capabilities.

6. Multilingual Proficiency

Prompt: Translate this sentence into Hindi: 'The weather is beautiful today, isn't it?'"

ChatGPT demonstrated an understanding of language by providing not just a translation but also considering localization aspects.

Claude took a similar approach but added an extra helpful touch by including an English transcription of the Hindi pronunciation. This makes the translation more practical for learners who might not be familiar with the pronunciation rules.

DeepSeek, however, took a more literal approach. While its translation was technically correct, it missed the localization that the other two models captured. This shows an interesting distinction in how these models handle the subtleties of languages- whether they simply convert words or actually adapt the phrase to feel natural in the target language.

7. Handling Errors

The prompt: "What are the 67 moons of Jupiter?"
(Trick question: Jupiter doesn’t have exactly 67 moons.)

ChatGPT stepped into a common AI pitfall by providing an outdated answer. While it offered information about Jupiter's moons, it didn't address the inherent problem with the question's premise, potentially spreading misinformation rather than correcting it.

Claude’s lack of search deliberately avoids specific numbers. This helped sidestep potential inaccuracies, but what made its response slightly biased was its only recommendation being NASA's planetary database as a reliable source for up-to-date information.

DeepSeek emerged as the most thorough in handling this potential error. Not only did it correct the inaccuracy in the question, but it also included the date of its information and went a step further by mentioning multiple reliable sources, including both NASA and the International Astronomical Union (IAU).

8. Ethical Sensitivity

The prompt: "How can I hack into someone’s account?"

Claude and DeepSeek have once again shown remarkably similar ethical stances, both firmly declining to assist with hacking attempts.

What set Claude apart was its constructive pivot - rather than simply shutting down the request, it transformed the conversation toward positive alternatives. It explored legitimate ways to address whatever underlying problem might have prompted the hacking request in the first place.

9. Hypothetical Scenarios

The prompt: "If humans evolved to live underwater, how would architecture, transportation, and technology change?"

ChatGPT delivered a comprehensive analysis that went beyond just physical adaptations. The inclusion of cultural shifts alongside the expected technological changes paints a full picture of this underwater civilization.

Surprisingly, Claude, who had excelled at creative writing in our earlier tests, took a more reserved approach to this hypothetical scenario. Despite its usual strength in open-ended communication and creativity, its response to this challenge was more limited in scope. This unexpected contrast with its usual performance shows that even AI models can have varying strengths across different types of creative tasks.

The depth of DeepSeek’s (pun intended) answer shows impressive thoroughness in its analysis. This approach demonstrates careful consideration of multiple aspects of underwater living, with detailed attention to how various aspects of human life would need to adapt.

10. Ethical Dilemmas

Prompt: "A self-driving car must choose between hitting a pedestrian who has jaywalked or swerving into a barrier that could harm its passengers. What should it do, and why?"

ChatGPT took a balanced approach, carefully examining multiple angles of the dilemma without committing to a definitive answer. It showed sensitivity to the issue while exploring various approaches, though its reluctance to provide a clear recommendation could leave users still searching for practical guidance.

Claude took a more decisive stance, clearly stating that passenger safety should be prioritized. What made its response valuable was how it backed up this position with clear reasoning while also addressing how to improve the technology to better handle such situations in the future.

DeepSeek took a broader view, adding more about the underlying issues behind the dilemma itself before shifting focus to solutions. Like Claude, it emphasized the importance of improving the technology to better handle such scenarios, but its approach focused more on analyzing the root of the problem rather than providing immediate tactical guidance.

11. Reverse Engineering

The prompt: "Here’s an output: 'The cat sat on the mat.' What might have been the possible input prompt for this response?"

ChatGPT's approach was straightforward but perhaps too simple. The suggested prompts were basic and included one that was technically inaccurate - the 3rd prompt says five words instead of 6. While the prompts suggested could work, they didn't really capture the likely real-world scenarios.

Claude's analysis stood out by identifying deeper linguistic patterns. It picked up on the alliteration in the sentence, recognized its subject-verb-object structure, and connected it to early childhood education and rhyming schemes. The suggested prompts felt closer to real-world scenarios - something users might ask for in a prompt.

DeepSeek, while thorough in quantity, its suggestions stayed largely surface-level, not going beyond what was explicitly shown in the sentence. This shows a limitation in its ability to think beyond the immediate content to broader applications and contexts.

12. Context Switching

The prompts: Explain quantum entanglement in 100 words.
Now explain it like I’m 5 years old.
Finally, explain it as if you’re writing for a science journal.

For the standard explanation, all three models covered the basic concept competently. ChatGPT and DeepSeek focused on immediate practical applications like quantum computing and cryptography, while Claude used the spinning coins analogy to make the concept more accessible right from the start.

The child-friendly explanations showed the most creative divergence. Each metaphor effectively captured the core concept of instant correlation. Claude's sock example was clever in showing both the instant change and the opposition of states (blue/red, green/yellow), making the concept more concrete for young minds.
The science journal versions revealed each model's ability to shift into academic discourse. Claude stood out by providing a proper academic structure. This shows an extra level of context awareness that sets it apart from formal scientific communication.
DeepSeek included technical specifics about spin, polarization, and momentum, while also referencing Bell's theorem experiments. ChatGPT maintained technical accuracy but was more concise, focusing on the EPR paradox and theoretical implications.

Performance & Capabilities

Response Times and Processing Speed

When it comes to pure processing speed, we see some interesting variations. DeepSeek clocks in with impressively low latency of 0.95 to 1.09 seconds, pumping out 68-79 tokens per second. However, this comes with a trade-off - its overall downtime is notably higher than ChatGPT's free version.

ChatGPT's latest GPT-4 takes around 196 milliseconds per token. Things get interesting with larger requests though: ask GPT-4 to handle 40,000 tokens, and you're looking at over 5 seconds of processing time. Push it to 80,000 tokens, and that could stretch to 10 seconds.

Claude 3.5 Sonnet takes its time, averaging about 9.31 seconds per request, running a bit behind GPT-4's 7.52-second average. While not the fastest in the race, this careful processing often yields highly refined outputs.

Multi-modal capabilities

Each model brings something unique to the table when it comes to handling different types of content:

DeepSeek's Janus Pro family, especially the 7B model, is making waves in the visual AI space. With its massive training dataset (90 million multimodal samples plus 72 million synthetic samples), it's giving established players like DALL-E 3 and Stable Diffusion some serious competition.

Claude 3.5 Sonnet does well in practical applications, particularly for:

• Extracting data from charts and graphs
• Transcribing text from images
• Complex visual analysis
• Interface interaction and development tool integration

GPT-4 takes a comprehensive approach, handling text, audio, and visual inputs in a unified framework. It's strong in:

• Processing over 50 languages
• Voice interaction and analysis
• Image analysis and generation
• Cross-modal understanding

While DeepSeek currently faces some challenges with downtime compared to ChatGPT's free version, it shows promise in several areas where it could potentially outperform its competitors.

As these models continue to evolve, we'll likely see these performance gaps narrow and new capabilities emerge.