Deepseek vs. OpenAI: What’s the Real Story?

In the past few weeks, I’ve had at least a dozen people ask me about Deepseek and how it compares to OpenAI. There’s a lot of hype, particularly around its cost-effectiveness and open-source nature, but here’s the truth: it feels like a less polished or less advanced alternative version of OpenAI
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Deepseek is unreliable, and when it is available, accuracy is hit or miss. In many ways, it reminds me of a bad relationship—sometimes impressive, but just as often unavailable or giving you the wrong information at the worst moment. If you’re considering it, your best bet is using it inside Perplexity rather than feeding data directly to China.

That said, it’s worth understanding the technical differences between Deepseek and OpenAI, because depending on your needs, there may be cases where Deepseek works well enough. Let’s break it down.

Architecture & Training: Cutting Costs vs. Scaling Up
Deepseek
• Uses a mixture-of-experts approach, meaning only a fraction of its 671 billion parameters activate per token. This makes it far cheaper to run but sometimes less robust.
• Reportedly trained for about $6 million—a fraction of what OpenAI spends on model training.
• Its reasoning models (Deepseek-R1) use pure reinforcement learning, which sounds advanced but sometimes leads to unpredictable results.
• Open-source under an MIT license, meaning developers can modify it, but that doesn’t mean it’s inherently better.
OpenAI
• GPT-4, GPT-4o, and the new o-series are built on massive transformer architectures, with fine-tuning from human feedback (RLHF) for consistency.
• Uses a “brute force” approach—all parameters are active at once, making it more expensive but also significantly more reliable.
• OpenAI’s models are closed-source, so you don’t get to see under the hood, but you also don’t get surprise hallucinations as often.

Performance & Reasoning: Who Actually Thinks Better?
Deepseek
✅ Good at logic, math, and coding—when it works.
❌ Struggles with general knowledge, nuanced conversation, and stability.
❌ Notoriously unavailable at random times (seriously, don’t depend on it).
✅ Provides visible reasoning steps (chain-of-thought), which some users like.
OpenAI
✅ Better for general knowledge, creative tasks, and conversation.
✅ The new o-series is designed for complex reasoning, but internal processes remain hidden.
✅ Multimodal capabilities (text, image, voice) that Deepseek doesn’t match.
❌ More expensive, especially for API use.
If you’re doing high-stakes work, OpenAI is the only real choice. Deepseek is more of a backup plan—one that might not even be available when you need it.

Ecosystem & Use Cases: Who Should Use What?
Deepseek
• Best inside Perplexity (where you can mitigate data risks).
• Good for developers who want an open-source model to tweak.
• If cost is your biggest concern, it’s a low-budget alternative—but expect trade-offs.
OpenAI
• The gold standard for enterprise use, where reliability and security matter.
• Ideal for creative work, advanced reasoning, and multimodal applications.
• Comes with extensive safety and compliance layers, making it usable in regulated industries.

Cost & Resource Efficiency: Deepseek Wins Here, But At What Cost?
Deepseek is objectively cheaper to train and run. It claims to offer OpenAI-level reasoning for a fraction of the cost—but cheaper doesn’t always mean better.
OpenAI’s higher compute costs result in a more reliable and consistent model, whereas Deepseek feels more like an experimental lab project that still needs work.

Regulatory & Privacy Considerations: The Elephant in the Room
One of the biggest concerns with Deepseek is data security. As a China-based company, it operates under strict regulations that may require compliance with government oversight and censorship policies.
This is why using Deepseek inside Perplexity is the safest bet—it adds a layer of protection between your data and any potential surveillance.
On the other hand, OpenAI operates with strict safety measures, content moderation, and compliance with global regulations, making it the preferred choice for sensitive applications.

Final Verdict: Stick With OpenAI for Serious Work
Deepseek is an interesting experiment—it’s open-source, cost-effective, and good at reasoning-based tasks when it works. But that’s the problem: it doesn’t always work. It’s inconsistent, sometimes unavailable, and accuracy is questionable.

If you’re looking for an AI model you can depend on for critical work, OpenAI remains the better choice. Deepseek is fine as a secondary option—just don’t expect it to show up when you need it most.
For those asking me if Deepseek is a real OpenAI competitor? Not yet.