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 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:

The flowchart provided by V3 is more effective than GPT-4o’s because it clearly outlines the actions to take based on whether the result is satisfactory or not.

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-4 for the given resistor network, GPT-4’s calculation of 1.29 Ω is correct while DeepSeek V3’s result of 3.59 Ω is incorrect. GPT-4 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:

DeepSeek V3’s explanation is more thorough and technically precise, covering preprocessing, indexing, and LangGraph integration, along with specific tool recommendations like FAISS and Pinecone. GPT-4’s response, while clear and well-structured, omits critical technical elements and simplifies complex processes. DeepSeek V3’s comprehensive coverage and technical depth make it more valuable for practical implementation, though GPT-4 excels in presenting information in an accessible format.

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

Also Read:

End Note

DeepSeek V3 proves that open-source models can compete with or even surpass commercial models like GPT-4o, while costing significantly less to train ($5.5M vs $100M+). Its strong performance and free accessibility make it an excellent choice for both developers and organizations seeking powerful AI capabilities without commercial constraints.

I’m really excited to use DeepSeek V3 and explore its full range of features. What about you? 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.