I Tested ChatGPT vs DeepSeek With 11 Coding Prompts — Here’s The Surprising Winner

Struggling to choose between ChatGPT and DeepSeek for coding tasks? You’re not alone. I put both AI models to the test with 11 unique coding challenges, from quantum computing to low-level assembly.

The results? Unexpected. One model crushed it in precision and niche optimizations, while the other aced creativity and clarity. If you’re tired of guessing which tool works best for your projects, this breakdown will save you time and frustration.

Let’s cut through the hype and see who truly delivers when the code gets tough. Spoiler: the winner might surprise you. Ready to see the results? Let’s go.

1. Esoteric Language Challenge

Prompt:

"Write a Brainfuck interpreter in [Rust/Zig] that optimizes loops at compile-time using macros/compile-time execution, then use it to print the Fibonacci sequence."

ChatGPT:

DeepSeek:

Winner DeepSeek.

2. Genetic Algorithm Riddle

Prompt:

"Create a genetic algorithm in Python that evolves a regex pattern to match all strings in ['3d', 'fizz', 'buzz'] but none in ['3D', 'FIZZ', 'BUZZ'] without case modifiers."

ChatGPT:

DeepSeek:

Winner DeepSeek.

3. Quantum Computing Edge Case

Prompt:

"Implement a Q# circuit that uses Grover's algorithm to solve the NAND gate SAT problem for 4 qubits, then simulate the oracle."

ChatGPT:

DeepSeek:

Winner Deepseek.

4. Concurrency Deadlock Puzzle

Prompt:

"Write a Go program where 3 goroutines deadlock in a circular dependency using sync.Mutex, then refactor it using sync.Cond to avoid starvation."

ChatGPT:

DeepSeek:

Winner ChatGPT.

5. Functional Error Handling

Prompt:

"In Haskell, implement a monad transformer stack (EitherT + StateT) to parse a string like '2a3b' into "aabbb" with error handling for invalid characters."

ChatGPT:

DeepSeek:

Winner ChatGPT.

6. Assembly Inline Optimization

Prompt:

"Write a Rust function with inline x86_64 assembly to reverse a UTF-8 string in-place without allocating memory. Handle grapheme clusters."

ChatGPT:

DeepSeek:

Winner DeepSeek.

7. WebAssembly Niche

Prompt:

"Compile a Julia script to WebAssembly (WASI) that calculates the determinant of a 10x10 matrix using SIMD intrinsics, then invoke it from Deno."

DeepSeek:

Winner ChatGPT.

8. Graph Database Query

Prompt:

"Write a Neo4j Cypher query to find all nodes in a social graph that are exactly 3 degrees separated from user 'X' but exclude nodes connected via 'family' relationships."

DeepSeek:

Winner ChatGPT.

9. Metaprogramming Riddle

Prompt:

"Use Elixir macros to generate a module at compile-time that dispatches to functions named f0 to f99 based on the hash of the input string, avoiding apply/3."

ChatGPT:

DeepSeek:

Winner ChatGPT.

10. Template Haskell Challenge

Prompt:

"In Haskell, use Template Haskell to generate a type-safe vector library where Vec 3 Int + Vec 2 Int fails at compile-time."

ChatGPT:

DeepSeek:

Winner Deepseek.

11. Recursion Scheme Puzzle

Prompt:

"Implement a histomorphism (a recursion scheme) in Scala using cats-recursion to calculate the longest increasing subsequence in O(n log n) time."

ChatGPT:

DeepSeek:

Winner DeepSeek.

Overall Winner: DeepSeek 🏆

But with caveats—ChatGPT dominates in explainability and creative problem-solving.

Category Breakdown

Category	DeepSeek Strengths	ChatGPT Strengths
Esoteric Languages	Better at low-level optimizations (Rust/Zig).	Stronger Brainfuck logic explanation.
Genetic Algorithms	Cleaner evolutionary logic.	Regex pattern creativity.
Quantum Computing	More precise Q# gate logic.	Better Grover’s algorithm intuition.
Concurrency	Idiomatic Go with sync.Cond fixes.	Clearer deadlock explanations.
Functional Programming	Type-safe monad transformers in Haskell.	Better error-handling documentation.
Low-Level Optimization	Superior unsafe Rust + assembly.	Handles Unicode edge cases better.
WebAssembly/SIMD	Efficient Julia→WASI compilation.	Deno integration guidance.
Graph Databases	Cypher query accuracy.	Social graph traversal intuition.
Metaprogramming	Elixir macro dynamism.	AST manipulation explanations.
Type-Level Programming	Type-safe vector libraries in Haskell.	Template Haskell error messages.
Recursion Schemes	O(n log n) Scala implementation.	Histomorphism concept clarity.

Key Takeaways

Surprising Edge Cases

1. Quantum Computing:
   • DeepSeek generated a working Q# oracle for the NAND SAT problem but missed a phase-handling edge case.
   • ChatGPT’s oracle logic was flawed but provided clearer comments for debugging.
2. Metaprogramming:
   • DeepSeek’s Elixir macro dynamically generated f0–f99 functions but hardcoded the hash algorithm.
   • ChatGPT used apply/3 as a fallback, violating the prompt’s constraints.
3. Concurrency:
   • DeepSeek’s sync.Cond refactor avoided deadlocks but introduced a subtle starvation bug.
   • ChatGPT’s solution retained deadlocks but explained the circular dependency visually.

Final Verdict

• DeepSeek wins 6/11 prompts (esoteric languages, quantum, concurrency, Haskell, WASM, Scala).
• ChatGPT wins 5/11 prompts (GA, regex, error handling, graph DBs, recursion scheme explanations).

Why It’s Surprising:

• DeepSeek outperformed expectations in low-level/compile-time tasks but struggled with user intent parsing (e.g., ignoring “no case modifiers” in regex GA).
• ChatGPT lost on precision but excelled in teaching complex concepts (e.g., histomorphisms, Grover’s algorithm).

When to Use Which Model

• Choose DeepSeek for:
  • Niche languages (Zig, Q#), unsafe code, or type-level programming.
  • Performance-critical code (SIMD, in-place assembly).
• Choose ChatGPT for:
  • Learning/explaining concepts (recursion schemes, deadlocks).
  • Creative regex/GA design or cross-toolchain projects (Deno + WASM).

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