DeepSeek V3 vs LLaMA 4: Which Model Reigns Supreme? - Analytics Vidhya

In the ever-evolving landscape of large language models, DeepSeek V3 vs LLaMA 4 has become one of the hottest matchups for developers, researchers, and AI enthusiasts alike. Whether you’re optimizing for blazing-fast inference, nuanced text understanding, or creative storytelling, the DeepSeek V3 vs LLaMA 4 benchmark results are drawing serious attention. But it’s not just about raw numbers – performance, speed, and use-case fit all play a crucial role in choosing the right model. This DeepSeek V3 vs LLaMA 4 comparison dives into their strengths and trade-offs so you can decide which powerhouse better suits your workflow, from rapid prototyping to production-ready AI applications.

Table of contents

• What is DeepSeek V3?
• What is Llama 4?
• How to Access DeepSeek V3 & LLaMA 4
• DeepSeek vs LLaMA 4: Task Comparison Showdown
• Task 5: Explain Overfitting to a High School Student
• Benchmark Comparison: DeepSeek V3.1 vs Llama-4-Scout-17B-16E
• Conclusion

What is DeepSeek V3?

DeepSeek V3.1 is the latest AI model from the DeepSeek team. It is designed to push the boundaries of reasoning, multilingual understanding, and contextual awareness. With a massive 560B parameter transformer architecture and a 1 million token context window, it’s built to handle highly complex tasks with precision and depth.

Key Features

• Smarter Reasoning: Up to 43% better at multi-step reasoning compared to previous versions. Great for complex problem-solving in math, code, and science.
• Massive Context Handling: With a 1 million token context window, it can understand entire books, codebases, or legal documents without missing context.
• Multilingual Mastery: Supports 100 languages with near-native fluency, including major upgrades in Asian and low-resource languages.
• Fewer Hallucinations: Improved training cuts down hallucinations by 38%, making responses more accurate and reliable.
• Multi-modal Power: Understands text, code, and images built for the real-world needs of developers, researchers, and creators.
• Optimized for Speed: Faster inference without compromising quality.

Also Read: DeepSeek V3-0324: Generated 700 Lines Error-Free

What is Llama 4?

Llama 4 is Meta’s latest open-weight large language model, designed with a powerful new architecture called Mixture-of-Experts(MoE). It comes in two variants:

1. Llama 4 Maverick: A high-performance model with 17 billion active parameters out of ~400B total, using 128 experts.
2. Llama 4 Scout: A lighter, efficient version with the same 17B active parameters, drawn from a smaller pool of ~109B total and just 16 experts.

Both models use early fusion for native multimodality, which means they can handle text and image inputs together out of the box. They’re trained on 40 trillion tokens, covering 200 languages, and fine-tuned to perform well in 12 major ones, including Arabic, Hindi, Spanish, and German.

Key Features

• Multimodal by design: Understands both text and images natively.
• Massive training data: Trained on 40T tokens, supports 200 languages.
• Language specialization: Fine-tuned for 12 key global languages.
• Efficient MoE architecture: It uses only a subset of experts per task, boosting speed and efficiency.
• Deployable on low-end hardware: Scout supports on-the-fly int4/int8 quantization for single-GPU setups. Maverick comes with FP8/BF16 weights for optimized hardware.
• Transformer support: Fully integrated with the latest Hugging Face transformers library (v4.51.0).
• TGI-ready: High-throughput generation via Text Generation Inference.
• Xet storage backend: Speeds up downloads and fine-tuning with up to 40% data deduplication.

How to Access DeepSeek V3 & LLaMA 4

Since you’ve explored the features of DeepSeek V3 vs LLaMA 4, let’s now look at how you can start using them effortlessly, whether for research, development or just testing their capabilities.

How to Access the Latest DeepSeek V3?

• Website: Test the updated V3 at deepseek.com for free.
• Mobile App: Available on iOS and Android, updated to reflect the March 24 release.
• API: Use model=’deepseek-chat’ at api-docs.deepseek.com. Pricing remains $0.14/million input tokens (promotional until February 8, 2025, though an extension hasn’t been ruled out).
• HuggingFace: Download the “DeepSeek V3 0324” weights and technical report from here.

For step-by-step instructions, you can refer to this blog.

How to Access the Llama 4 Models?

• Llama.meta.com: This is Meta’s official hub for Llama models. 
• Hugging Face: Hugging Face hosts the ready-to-use versions of Llama 4. You can test models directly in the browser using inference endpoints or deploy them via the Transformers library.
• Meta Apps: The Llama 4 models also power Meta’s AI assistant available in WhatsApp, Instagram, Messenger, and Facebook.
• Web Page: You can directly access the latest Llama 4 models using the web interface.

DeepSeek vs LLaMA 4: Task Comparison Showdown

Both DeepSeek V3 and LLaMA 4 Scout are powerful multimodal models, but how do they perform when put head-to-head on real-world tasks?

To find out, I tested both models on five diverse and challenging tasks:

• Light and Energy Paradox: Evaluates scientific reasoning and clarity in explaining a physics concept.
• Count Palindromic Words in a Sentence: Tests coding ability, efficiency, and structuring.
• Summarize a Story: Assesses summarization and natural language understanding.
• Finance Problem (Simple vs Compound Interest): Looks at mathematical reasoning and teaching clarity.
• Explain Overfitting to a High School Student: Measures the ability to simplify a complex ML concept for beginners.

Each task highlights a unique strength or limitation, helping us understand which model excels in which domain.

Task 1: Light and Energy Paradox

Input Prompt: Light has no mass, so it also has no energy, according to Einstein. But how can sunlight warm the Earth without energy?

Output by Deepseek v3:

Output by LLaMA 4 Scout:

Comparison

Aspect	LLaMA 4 Scout	DeepSeekV3
Physics Explanation	Mentions E = mc² and E = hf; photons have no rest mass but carry energy.	Includes E = mc², E = hf, and E² = (mc²)² (pc)²; explains photon momentum (p = hν/c).
Photon Heating	Earth absorbs photons, leading to warming.	Absorption increases molecular vibration—explains heat transfer in detail.
Clarity on Misconception	Resolves by noting photons are massless yet energetic.	Clarifies energy comes from momentum, not rest mass—better scientific framing.
Tone & Style	Educational and simple, step-by-step.	More technical but still accessible, includes equations and summaries.

Verdict:

• LLaMA is ideal for learners looking for a clear, conceptual walkthrough.
• DeepSeek is better suited for readers wanting a more technical, physics-oriented explanation with richer scientific detail.

Result: LLaMA 4 Scout – 1 | DeepSeek V3 – 0

Task 2: Count Palindromic Words in a Sentence

Input prompt: Write a Python function that takes a sentence as input and returns the number of palindromic words in it. A palindromic word is a word that reads the same forwards and backwards, like “madam” or “level”. Ignore case and punctuation.

Output by Deepseek v3:

This is the code given by DeepSeek V3:

import re

def count_palindromic_words(sentence):

# Remove all punctuation and split into words

words = re.findall(r'\b\w \b', sentence.lower())

count = 0

for word in words:

if word == word[::-1]:

count  = 1

return count

# Example usage:

sentence = "Madam Anna and her civic racecar saw a level deed at noon in a kayak."

print(count_palindromic_words(sentence))

Output:

10

Output by LLaMA 4 Scout:

This is the code given by LLaMA 4 Scout:

import re

import string

def count_palindromic_words(sentence: str) -> int:

"""

Count the number of palindromic words in a sentence.

Args:

sentence (str): The input sentence.

Returns:

int: The number of palindromic words.

"""

# Convert to lower case and remove punctuation

sentence = sentence.lower()

sentence = re.sub('[' string.punctuation ']', '', sentence)

# Split into words

words = sentence.split()

# Initialize count

count = 0

# Check each word

for word in words:

# Compare word with its reverse

if word == word[::-1]:

count  = 1

return count

# Example usage

def main():

sentence = "Madam Anna and her civic racecar saw a level deed at noon in a kayak."

print(f"Number of palindromic words: {count_palindromic_words(sentence)}")

if __name__ == "__main__":

main()

Output:

Number of palindromic words: 10

Comparison

Aspect	LLaMA 4 Scout	DeepSeekV3
Word Processing	Uses .split(), string.punctuation, and re.sub for cleaning	Uses re.findall(r’\b\w \b’, …) for efficient word extraction
Code Style	Structured with main() and docstrings; more verbose	Compact, minimal, and function-focused
Imports & Efficiency	Uses both re and string; slightly longer due to extra steps	Uses only re; more efficient by combining cleaning and splitting
Best Use Case	Great for learning and well-documented codebases	Best suited for quick, efficient utility scripts

Verdict: Both implementations are correct and effective, where the choice depends on whether you prioritize readability or conciseness.

Result: LLaMA 4 Scout – 1 | DeepSeek V3 – 1

Task 3: Summarize a Story

Story: https://www.newyorker.com/humor/daily-shouts/a-short-story-written-with-thought-to-text-technology

Output by Deepseek v3:

Output by LLaMA 4 Scout:

Comparison

Aspect	DeepSeek Output	LLaMA Output
Tone & Focus	Abstract and emotional; focuses on themes like loneliness and self-doubt	Narrative and descriptive; paints a clearer picture of the character’s situation
Detail Level	Generalized (law firm, failed career)	Specific (Starbucks, names like Rebecca and Steve Bowman)
Style	Analytical and summary-like	Story-driven and expressive
Best Use Case	Quick emotional overview	Rich character snapshot with more context

Verdict: LLaMA provides a richer storytelling recap. DeepSeek is efficient and theme-oriented.

Result: LLaMA 4 Scout – 2 | DeepSeek V3 – 1

Task 4: Finance Problem (Simple vs Compound Interest)

Input Prompt: Sum of ₹10,000 is invested for 3 years. The interest rate is 8% per annum. If the interest is calculated annually (simple interest), what is the total amount after 3 years? Now, if the interest is calculated annually and added to the principal at the end of each year (compound interest), what is the total amount after 3 years? Which method yields a higher return?

Output by Deepseek v3:

Output by LLaMA 4 Scout:

Comparison

Aspect	DeepSeek V3	LLaMA 4
Style	Concise, formula-driven	Step-by-step, narrative
CI Calculation	Used direct formula A = P(1 r)^n	Used yearly additions with % growth
Best For	Quick math reference	Conceptual learning and clarity
Output Length	Shorter	Longer, explanatory

Verdict:

• Choose DeepSeek V3 for fast, formula-based answers.
• Choose LLaMA 4 if you want to understand the process with step-by-step clarity.

Result: LLaMA 4 Scout – 2 | DeepSeek V3 – 2

Task 5: Explain Overfitting to a High School Student

Input prompt: Explain the concept of overfitting in machine learning to a high school student using an example.

Output by Deepseek v3:

Output by LLaMA 4 Scout:

Comparison

Aspect	LLaMA 4	DeepSeek
Style	Numerical/graph analogy	Real-world story analogy
Accessibility	Medium (assumes some logical understanding)	High (relatable to everyday study habits)
Depth of Concept	Thorough with technical terms	Conceptually deep, language simplified
Best For	Visually/math-inclined learners	General audience and beginners

Verdict:

• For a high school student, DeepSeek’s analogy-based explanation makes the idea of overfitting more digestible and memorable.
• For someone with a background in Machine Learning, LLaMA’s structured explanation might be more insightful.

Result: LLaMA 4 Scout – 2 | DeepSeek V3 – 3

Overall Comparison

Aspects	DeepSeek V3	LLaMA 4 Scout
Style	Concise, formula-driven	Step-by-step, narrative
Best For	Fast, technical results	Learning, conceptual clarity
Depth	High scientific accuracy	Broader audience appeal
Ideal Users	Researchers, developers	Students, educators

Choose DeepSeek V3 for speed, technical tasks, and deeper scientific insights. Choose LLaMA 4 Scout for educational clarity, step-by-step explanations, and broader language support.

Benchmark Comparison: DeepSeek V3.1 vs Llama-4-Scout-17B-16E

Across all three benchmark categories, DeepSeek V3.1 consistently outperforms Llama-4-Scout-17B-16E, demonstrating stronger reasoning capabilities, mathematical problem-solving, and better code generation performance.

Conclusion

Both DeepSeek V3.1 and LLaMA 4 Scout showcase remarkable capabilities, but they shine in different scenarios. If you’re a developer, researcher, or power user seeking speed, precision, and deeper scientific reasoning, DeepSeek V3 is your ideal choice. Its massive context window, reduced hallucination rate, and formula-first approach make it perfect for technical deep dives, long document understanding, and problem-solving in STEM fields.

On the other hand, if you’re a student, educator, or casual user looking for clear, structured explanations and accessible insights, LLaMA 4 Scout is the way to go. Its step-by-step style, educational tone, and efficient architecture make it especially great for learning, coding tutorials, and multilingual applications.

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