In a year marked by OpenAI’s notable releases like GPT-4o, Sora, and o1, the open-source community has been steadily advancing with its own impressive models. Among them is DeepSeek V3, an open-source model developed on a budget of just $5.5M—significantly lower than GPT-4o’s ($100M+) training cost. With 685 billion parameters and a Mixture-of-Experts (MoE) architecture, DeepSeek V3 competes strongly in areas like coding and translation, while offering cost efficiency and open-source flexibility. Let’s explore how DeepSeek V3 compares to GPT-4o and what it brings to the table for AI development.

Task 1: Create a Flow Chart

Prompt: Create a comprehensive flow chart illustrating the Reflection Pattern in Agentic AI.
These are the steps involved in reflection pattern –

Generate Initial Output
Self-Review/Critique the Output
Identify Errors, Gaps, or Improvement Areas
Develop Improvement Suggestions
Revise/Refine the Output
Repeat Steps 2-5 Until Satisfactory Result is Achieved
Create a new flow chart now

DeepSeek V3 Output:

+---------------------+
| Generate Initial Output |
+---------------------+
 |
 v
+---------------------+
| Self-Review/Critique the Output |
+---------------------+
 |
 v
+---------------------+
| Identify Errors, Gaps, or Improvement Areas |
+---------------------+
 |
 v
+---------------------+
| Develop Improvement Suggestions |
+---------------------+
 |
 v
+---------------------+
| Revise/Refine the Output |
+---------------------+
 |
 v
+---------------------+
| Is the Result Satisfactory? |
+---------------------+
 |
 +--------+--------+
 | |
 v v
Yes No
 | |
 v v
+---------------------+
| Final Output |
+---------------------+
 |
 v
+---------------------+
| End |
+---------------------+

GPT 4o Output:

👁 GPT 4o Output

Observation:

GPT-4o’s flowchart is more effective than DeepSeek V3’s since it implements a proper feedback loop. While V3’s flowchart attempts to show decision paths based on satisfactory results, it has a logical flaw where both ‘yes’ and ‘no’ outcomes lead to the same output. In contrast, GPT-4o’s design correctly shows how unsatisfactory results loop back into the process, better representing the iterative nature of refinement.

Verdict:

DeepSeek V3 ❌ | GPT 4o✅

Task 2: Zebra Puzzle

The first task for this GPT 4o vs DeepSeek V3 guide, I have a zebra puzzle from this website.

Prompt: Solve this zebra puzzle and give me a table of final result.

👁 Zebra Problem Prompt.webp

DeepSeek V3 Output:

👁 DeepSeek V3 Output

Putting this response on the website:

👁 Image

GPT 4o Output:

👁 Image

Putting this solution on the website:

👁 Image

Observation:

While both models assigned random names to elements where information was unavailable, V3 correctly resolved the problem, whereas GPT-4o failed to do so.

Verdict:

DeepSeek V3 ✅ | GPT 4o ❌

Task 3: Physics Circuit Problem

Prompt: Figure shows part of a circuit. It consists of resistors combined in both parallel and series configurations. Find the equivalent resistance.

👁 Image

DeepSeek V3 Output:

👁 Image

GPT 4o Response:

👁 Image

Observation:

When comparing the solutions from DeepSeek V3 and GPT-4o for the given resistor network, GPT-4o’s calculation of 1.29 Ω is correct while DeepSeek V3’s result of 3.59 Ω is incorrect. GPT-4o properly identified the circuit’s structure with three parallel branches: (R1+R2=3Ω), R3=3Ω, and (R4+R5=9Ω), then accurately applied the parallel resistance formula (1/Rt = 1/3 + 1/3 + 1/9 = 7/9) to arrive at the final result. DeepSeek V3 made critical errors by incorrectly grouping resistors, misidentifying series and parallel combinations, which led to its inaccurate final calculation.

Verdict:

DeepSeek V3 ❌ | GPT 4o ✅

Task 4: Article Summary

Prompt: Read the article at https://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/ to understand the process of creating a vector database for Wikipedia data. Then, provide a concise summary of the key steps.

DeepSeek V3 Output:

👁 Image

GPT 4o Output:

👁 Image

Observation:

Both DeepSeek V3 and GPT-4o provide technically sound explanations, but GPT-4o’s response aligns more precisely with the original query about vector DB creation. While DeepSeek V3 offers broader technical context covering preprocessing, indexing, and LangGraph integration, GPT-4o focuses specifically on ChromaDB implementation, which directly addresses the task at hand. Both approaches have their merits, but for the specific question asked, GPT-4o’s targeted response proves more immediately applicable.

Verdict:

DeepSeek V3 ❌ | GPT 4o ✅

Task 5: Finding Differences

Prompt: The image is divided into two parts that are nearly identical. However, there are three elements present in the left image that are missing in the right one. Your task is to identify these missing elements.

👁 Find Difference

DeepSeek V3 Output:

👁 Image

GPT 4o Output:

👁 Image

Observation:

V3 was unable to analyze the image directly and provided a generic response. GPT-4 identified one correct difference, but the remaining differences it suggested were incorrect.

Verdict:

DeepSeek V3 ❌ | GPT 4o ❌

GPT 4o vs DeepSeek V3: Final Result

Task	Winner
Flow Chart	GPT-4o
Zebra Puzzle	DeepSeek V3
Physics Circuit Problem	GPT-4o
Article Summary	GPT-4o
Finding Differences	Neither

Also Read:

End Note

It is evident that GPT-4o outperformed DeepSeek V3 in the tasks mentioned above. However, its success in the puzzle-solving task highlights its particular strength in that area.

That being said, DeepSeek V3 proves that open-source models can compete with commercial models like GPT-4o, all while being significantly more cost-effective to train ($5.5M vs. $100M+).

I’m genuinely excited to dive into DeepSeek V3 and explore its full range of features. What about you? Have you tried both models? Whose response did you like better? Share your thoughts in the comments below!

Learn DeepSeek from Scratch! Join our “Getting Started with DeepSeek” course and explore its features to boost your AI skills.

Frequently Asked Questions

Q1. Which model is better for coding and technical tasks?

A. DeepSeek V3 outperforms GPT-4o in coding, mathematics, and reasoning benchmarks. It achieved higher scores in evaluations like MMLU and HumanEval, making it ideal for developers and technical applications .

Q2. How do DeepSeek V3 and GPT-4o differ in architecture and capabilities?

A. DeepSeek V3 utilizes a Mixture-of-Experts (MoE) architecture with 671 billion parameters, focusing on text-based tasks. In contrast, GPT-4o employs a dense architecture supporting multimodal inputs, including text, images, and audio, offering versatility for various applications .

Q3. Which model is more cost-effective for API usage?

A. DeepSeek V3 is approximately 9 times more cost-effective than GPT-4o. DeepSeek V3’s input costs range from $0.07 to $0.27 per million tokens, while GPT-4o’s input costs are $2.50 per million tokens. For output, DeepSeek V3 charges around $1.10 per million tokens, while GPT-4o’s cost is $10.00 per million tokens. Thus, DeepSeek V3 offers a more affordable solution for users compared to GPT-4o.

Q4. Can DeepSeek V3 and GPT-4o be used together in applications?

A. Yes, integrating both models can leverage their strengths. Use DeepSeek V3 for specialized tasks like data analysis and GPT-4o for general-purpose tasks such as customer interactions, combining cost efficiency with versatility

👁 Nitika Sharma

Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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