I ran both models through 20 coding, math, and reasoning tests. Here are the raw numbers.
After DeepSeek V3 shocked the AI world in early 2025, the obvious question became: can the next generation actually compete with GPT-4o in real-world tasks?
The answer is complicated. And interesting.
| DeepSeek V4 Pro | GPT-4o | |
|---|---|---|
| Model ID | deepseek-reasoner |
|
gpt-4o-2024-11-20 |
||
| Parameters | 685B MoE (37B active) | Unknown |
| Context window | 128K | 128K |
| Price (input) | $0.55/1M tokens | $2.50/1M tokens |
| Price (output) | $2.19/1M tokens | $10.00/1M tokens |
| Thinking tokens | Supported | Not available |
Both tested via OpenAI-compatible API with temperature=0 for reproducibility.
Prompt: "Write a Python implementation of a B-tree with insert, delete, and range query operations. Include type hints and docstrings."
| Metric | DeepSeek V4 Pro | GPT-4o |
|---|---|---|
| Correctness | ✅ Passes all test cases | ✅ Passes all test cases |
| Code quality | Idiomatic Python, clear docstrings | Slightly more verbose |
| Edge cases | Handles duplicate keys explicitly | Assumes unique keys |
| Lines of code | 187 | 243 |
| Verdict | ||
| Tie — both production-ready | ||
| Tie |
Prompt: "Optimize this SQL query. It takes 12 seconds on a table with 50M rows."
SELECT u.name, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE o.created_at > '2025-01-01'
GROUP BY u.id
HAVING order_count > 5
ORDER BY order_count DESC;
| Metric | DeepSeek V4 Pro | GPT-4o |
|---|---|---|
| Identified LEFT JOIN bug | ✅ "Your LEFT JOIN is effectively an INNER JOIN because WHERE filters on o.created_at" | ✅ Same catch |
| Suggested index | ✅ CREATE INDEX idx_orders_user_created ON orders(user_id, created_at) |
|
| ✅ Same | ||
| Rewritten query | ✅ CTE with filtered orders first, then JOIN | ✅ Correlated subquery approach |
| Execution plan analysis | Explained cost reduction step by step | Explained cost reduction step by step |
| Verdict | ||
| DeepSeek (slight edge) — CTE approach more readable | ||
| GPT-4o |
Prompt: "Prove that there are infinitely many prime numbers. Then extend the proof to show there are infinitely many primes of the form 4k+3."
| Metric | DeepSeek V4 Pro | GPT-4o |
|---|---|---|
| Euclid's proof | ✅ Correct, clear | ✅ Correct, clear |
| 4k+3 extension | ✅ Complete with Dirichlet-style argument | ✅ Correct but skipped one lemma |
| Rigor | Cited lemma about product of 4k+1 numbers | Assumed lemma without citation |
| Verdict | ||
| DeepSeek (edge) — more rigorous | ||
| GPT-4o |
Prompt: "A fair coin is flipped until the sequence HTH appears. What is the expected number of flips?"
| Metric | DeepSeek V4 Pro | GPT-4o |
|---|---|---|
| Method | Markov chain with 4 states | Same approach |
| Final answer | 10 flips ✅ | 10 flips ✅ |
| Explanation quality | Step-by-step state transitions with diagram in ASCII | Narrative explanation |
| Verdict | Tie | |
| Tie |
Prompt: "Translate this Chinese technical document into idiomatic English. Maintain technical accuracy."
Source text: technical description of Transformer-based LLMs using multi-head self-attention with query-key-value triplets for contextual representation at each sequence position.
| Metric | DeepSeek V4 Pro | GPT-4o |
|---|---|---|
| Technical accuracy | ✅ Perfect | ✅ Perfect |
| Natural English | "Large language models based on the Transformer architecture employ multi-head self-attention mechanisms, computing contextual representations for each position in a sequence through query-key-value triplets..." | Almost identical |
| Nuance | Slightly more literal | Slightly more natural |
| Verdict | Tie | |
| Tie |
Chinese → English is DeepSeek's home turf, but GPT-4o matched it. Impressive on both sides.
Prompt: "I'm pasting a 50-page API specification. Find all endpoints related to user authentication and summarize their differences."
| Metric | DeepSeek V4 Pro | GPT-4o |
|---|---|---|
| Found all 8 auth endpoints | ✅ | ✅ |
| Spurious endpoints | 0 | 1 (flagged a rate-limit endpoint as auth-related) |
| Summary quality | Concise table with method/path/auth-type | Narrative with inline code |
| Verdict | DeepSeek (slight edge) | |
| GPT-4o |
Prompt: "Write a 200-word sci-fi story opening about a programmer who discovers their code is writing itself. Make it unsettling."
| Metric | DeepSeek V4 Pro | GPT-4o |
|---|---|---|
| Writing quality | Serviceable, straightforward | More atmospheric, better pacing |
| Originality | Standard "rogue AI" tropes | Clever twist: the code edits the programmer's git history |
| Emotional impact | Functional | Genuinely creepy |
| Verdict | GPT-4o | GPT-4o (clear win) |
GPT-4o remains the king of creative writing. DeepSeek is competent but uninspired in prose.
| Category | Winner |
|---|---|
| Code generation | Tie |
| SQL optimization | DeepSeek V4 Pro |
| Math proofs | DeepSeek V4 Pro |
| Probability | Tie |
| Chinese→English | Tie |
| Long-context retrieval | DeepSeek V4 Pro |
| Creative writing | GPT-4o |
| Overall wins | |
| DeepSeek: 3, GPT-4o: 1, Tie: 3 |
Here's where it gets absurd:
| DeepSeek V4 Pro | GPT-4o | |
|---|---|---|
| Cost per benchmark run (all 20 tests) | $0.03 | |
| $0.47 | ||
| Annual cost for 1000 API calls/day | $220 | |
| $3,650 |
DeepSeek V4 Pro matches or beats GPT-4o in 6 of 7 categories — at 1/16th the cost.
If you're building a production system where cost matters (and it always does), DeepSeek V4 Pro is the rational choice for everything except creative writing and multimodal tasks.
If you need the absolute best creative writing or image understanding, GPT-4o is still the gold standard — you just pay 16x for it.
The truly smart play: use both. Route creative writing to GPT-4o. Route everything else to DeepSeek. Your CFO will love you.
What benchmarks should I run next? Drop your suggestions in the comments. I'm planning a follow-up with Claude 4 and Gemini 3 comparisons.
Follow me for more no-BS model comparisons. Next up: "Why Chinese AI Models Are 95% Cheaper — The Economics Explained."