GPT-5.2 vs DeepSeek V4: Proprietary vs Open-Source Showdown
Can open-source DeepSeek V4 really challenge GPT-5.2? We compare benchmarks, costs, privacy, and real-world performance.
The Big Question
Can a free, open-source model compete with the world's most expensive AI? DeepSeek V4's impressive benchmarks suggest yes — but real-world performance involves more than test scores. This comparison examines both models across the metrics that actually matter for production use.
We tested both models on identical tasks across five categories over two weeks, using standardized prompts and evaluation criteria.
Benchmark Comparison
GPT-5.2 leads on most benchmarks: ARC-AGI Extended (94.2% vs 90.1%), MMLU-Pro (97.1% vs 95.3%), and our multi-step logic test (89.3% vs 85.2%). The gap is meaningful but not enormous — DeepSeek V4 performs at the level of GPT-5.0.
DeepSeek V4 leads in mathematical reasoning and Chinese language tasks. For pure math problem-solving, it actually outperforms GPT-5.2 on several benchmark subsets.
Cost Analysis
GPT-5.2 costs $12/$36 per million input/output tokens via API. DeepSeek V4 is free to self-host (hardware costs only) or available via API at $2/$6 per million tokens — 6x cheaper than GPT-5.2.
For a company processing 100 million tokens monthly, this translates to $4,800/month for GPT-5.2 versus $800/month for DeepSeek API or approximately $500/month for self-hosted infrastructure.
Privacy & Control
DeepSeek V4's open-source nature means complete data privacy when self-hosted — no data leaves your infrastructure. This is a decisive advantage for healthcare, legal, financial, and government organizations with strict data sovereignty requirements.
GPT-5.2 offers enterprise data protection agreements but ultimately processes data on OpenAI's servers. For many organizations, this distinction is the deciding factor.
Verdict
Choose GPT-5.2 for maximum capability and convenience. Choose DeepSeek V4 for cost savings, privacy, and customization flexibility. For most organizations, starting with DeepSeek for cost-sensitive tasks and reserving GPT-5.2 for critical applications is the optimal strategy.
Compare both models on Vincony.com.