DeepSeek-V4-Pro vs GPT-4o: Full Benchmark Comparison

Choosing a large model is never a one-line answer. We benchmarked DeepSeek V4 Pro against GPT-4o across six standard tests (temperature=0, top_p=1) and added three production-style tasks to guide real-world selection.

1. Benchmark Overview

Benchmark	Dimension	DeepSeek V4 Pro	GPT-4o
MMLU	Knowledge	89.2%	86.4%
GPQA Diamond	Expert reasoning	90.1%	85.2%
HumanEval	Code generation	87.5%	84.1%
MATH-500	Mathematics	85.3%	76.6%
Codeforces	Competitive programming	3206	2800
LiveCodeBench	Live coding	93.5%	88.2%

V4 Pro leads all six benchmarks. The largest gaps are in math (+8.7 pts) and competitive programming (+406 Elo).

2. Real-World Tasks

We designed three production-like scenarios:

1. Refactor a 500-line legacy Python module: V4 Pro passed all unit tests in one pass; GPT-4o needed two revision rounds.
2. Parse an 80-page financial PDF into structured summary: V4 Pro ingested the full document via 1M context; GPT-4o required chunking and manual stitching.
3. Multi-step Agent tool chain (query → compute → write file): V4 Pro scored 73.6% on MCPAtlas with more stable chains.

3. Cost Comparison

Per million output tokens (approximate):

• DeepSeek V4 Pro: ~¥6
• GPT-4o: ~¥60 (public pricing equivalent)

At comparable quality, V4 Pro costs roughly 1/10 of GPT-4o — a major advantage for high-frequency enterprise workloads.

4. How to Choose

• Best value, long context, strong coding → DeepSeek V4 Pro
• Deep OpenAI ecosystem lock-in → Start with V4's OpenAI-compatible endpoint for a low-friction A/B test

Good news: DeepSeek API is fully OpenAI-compatible. Change base_url to https://api.deepseek.com/v1 and compare with zero refactor.

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY", base_url="https://api.deepseek.com/v1")

prompt = "Implement a thread-safe LRU cache in Python with unit tests"
for model in ["deepseek-v4-pro", "gpt-4o"]:
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )
    print(f"=== {model} ===")
    print(resp.choices[0].message.content[:500])

3 Comparison Prompts

Solve the same LeetCode Hard problem with V4 Pro and GPT-4o; compare approach and code quality.

Given a 50-page PDF summary task, compare information completeness under long context.

Multi-step Agent: weather → outfit → email; compare tool-call stability.

FAQ

Q: How do you control variables?

A: Same temperature=0, system prompt, and max_tokens; three runs each, median reported.

Q: Where is V4 Pro strongest?

A: Math (+8.7), competitive programming (+406 Elo), and long documents (1M context).

Q: Do I need to rewrite code to migrate?

A: With the OpenAI SDK, change base_url and model only — other parameters stay compatible.

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📅 Updated July 2, 2026. Sources: DeepSeek 2026 technical report and public benchmark leaderboards.