What Is DeepSeek and How Does It Compare to GPT-4?

When DeepSeek-R1 landed in January 2025, it did something no open model had managed before. It matched the reasoning quality people expected from GPT-4, shipped the weights under an open license, and priced its API far below what OpenAI charged. Within days the r/LocalLLaMA subreddit filled with people running it on their own hardware, and the release held the top of Hacker News for the better part of a week. Eighteen months later the interest has not faded. On HookFlow, DeepSeek holds a heat score of 87 and is still rising, sitting just behind Claude (92) and ahead of ChatGPT (70) among the AI models we track.

This guide explains what DeepSeek actually is, where it beats GPT-4, and where GPT-4 still holds the lead.

What Is DeepSeek?

DeepSeek is an AI lab based in China whose models are released as open weights, meaning anyone can download them, inspect them, fine-tune them, and run them without paying for API access. Its breakout model, DeepSeek-R1, is a reasoning model. Instead of answering immediately, it works through a problem step by step before committing to a response, the same technique behind OpenAI's o1 series.

Two things made R1 spread so quickly. The first was quality. On math, logic, and coding tasks it traded blows with the frontier closed models rather than trailing them by a generation. The second was economics. Because the weights are public, a developer can run DeepSeek on a rented GPU or, for the smaller distilled versions, on a capable laptop, and the hosted API is priced at a small fraction of GPT-4. For teams whose bills scale with token volume, that gap is the whole story.

DeepSeek vs GPT-4: The Honest Comparison

On reasoning and coding, the two are close enough that the choice rarely comes down to raw capability. R1 is strong at the kind of structured, multi-step problems where showing its work pays off: algorithm design, math proofs, debugging logic. GPT-4, in its GPT-4o form, is the more rounded generalist, with better handling of images, audio, and the long tail of everyday tasks that are not strictly reasoning problems.

The clearest separation is openness and cost. DeepSeek can run inside your own infrastructure, which matters for anyone with data that cannot leave the building, and its pricing removes the anxiety of a runaway API bill. GPT-4 gives you none of that, but it gives you the largest tooling network in the industry, a mature API, function calling that libraries assume by default, and a support relationship if you are an enterprise.

There is also the question of trust and jurisdiction. DeepSeek is a Chinese model, and some organizations have policy constraints that rule out the hosted API or the company entirely. Running the open weights yourself sidesteps the hosted-service concern, which is one reason the self-hosted route is so popular for this particular model.

Where GPT-4 Still Wins

If your product depends on multimodal input, GPT-4o remains the safer default. If your engineers rely on a deep bench of integrations and third-party tools that were built OpenAI-first, switching costs are real. And if you want a single vendor to answer for reliability, ChatGPT and the OpenAI API are the more established choice. Notably, ChatGPT itself still carries a heat score of 70 and is rising on our tracker, so reports of its decline are overstated.

Reading the Heat Signal

Heat scores are worth watching here because they capture attention, not marketing. DeepSeek at 87 and rising tells you the developer conversation around open reasoning models has not cooled off, which is unusual eighteen months after a launch. Claude leading at 92 is a reminder that the frontier is a three-way race, not a duel. If you are making a build-versus-buy decision, that momentum is a signal about where community tooling and tutorials will keep accumulating.

Which One Should You Use?

Reach for DeepSeek when cost per token dominates your economics, when you need to run the model on your own hardware, or when your workload is reasoning-heavy and you want open weights you can fine-tune. Reach for GPT-4 when you need multimodal features, when your stack is already built around OpenAI, or when vendor accountability matters more than the invoice. Many teams end up using both: DeepSeek for the high-volume reasoning work and GPT-4 for the general-purpose surface that touches users.

Compare the live momentum on the DeepSeek, ChatGPT, and Claude pages before you commit.

FAQ

Is DeepSeek really free?

The open weights are free to download and run, so your only cost is the hardware or cloud GPU you run them on. DeepSeek also offers a hosted API, which is paid but priced well below GPT-4.

Can DeepSeek run on a normal computer?

The full R1 model needs serious GPU memory, but the smaller distilled versions run on a high-end laptop or a single consumer GPU, which is how most people try it locally.

Is DeepSeek better than GPT-4 at coding?

They are close. DeepSeek-R1 is very strong on structured, logic-heavy coding problems. GPT-4 is more consistent across the messy, general tasks that make up a lot of day-to-day development.

Should I worry about data privacy with DeepSeek?

If you use the hosted API, treat it like any third-party service and check your data policy. If that is a concern, running the open weights on your own infrastructure keeps the data in-house, which is a large part of the model's appeal.