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.
Table of contents
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 patter –
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:
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.
DeepSeek V3 Output:
Putting this response on the website:
GPT 4o Output:
Putting this solution on the website:
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 5: 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.
DeepSeek V3 Output:
GPT 4o Response:
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:
GPT 4o Output:
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.
DeepSeek V3 Output:
GPT 4o Output:
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!
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.
