I Tested DeepSeek’s R1 and V3 Coding Skills – and we’re not All Doomed (Yet).

DeepSeek blew up into the world’s awareness this previous weekend. It stands out for three effective factors:

1. It’s an AI chatbot from China, instead of the US

2. It’s open source.

3. It uses vastly less infrastructure than the huge AI tools we’ve been taking a look at.

Also: Apple researchers reveal the secret sauce behind DeepSeek AI

Given the US federal government’s concerns over TikTok and possible Chinese government involvement in that code, a new AI emerging from China is bound to create attention. ZDNET’s Radhika Rajkumar did a deep dive into those concerns in her post Why China’s DeepSeek might break our AI bubble.

In this article, we’re avoiding politics. Instead, I’m putting both DeepSeek V3 and DeekSeek R1 through the very same set of AI coding tests I have actually thrown at 10 other large language designs. According to DeepSeek itself:

Choose V3 for tasks requiring depth and accuracy (e.g., fixing sophisticated mathematics problems, producing complex code).

Choose R1 for latency-sensitive, high-volume applications (e.g., customer support automation, standard text processing).

You can pick between R1 and V3 by clicking the little button in the chat interface. If the button is blue, you’re utilizing R1.

The short response is this: outstanding, however clearly not best. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was really my very first test of ChatGPT’s shows expertise, way back in the day. My partner needed a plugin for WordPress that would help her run an involvement device for her online group.

Also: The very best AI for coding in 2025 (and what not to utilize)

Her needs were fairly easy. It needed to take in a list of names, one name per line. It then had to sort the names, and if there were duplicate names, different them so they weren’t noted side-by-side.

I didn’t truly have time to code it for her, so I chose to give the AI the challenge on a whim. To my big surprise, it worked.

Since then, it’s been my first test for AIs when assessing their programming abilities. It requires the AI to understand how to set up code for the WordPress structure and follow triggers plainly adequate to develop both the user interface and program reasoning.

Only about half of the AIs I have actually checked can completely pass this test. Now, however, we can include another to the winner’s circle.

DeepSeek V3 created both the interface and program reasoning exactly as defined. When It Comes To DeepSeek R1, well that’s an intriguing case. The “reasoning” element of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.

The UI looked various, with much larger input locations. However, both the UI and reasoning worked, so R1 also passes this test.

So far, DeepSeek V3 and R1 both passed among 4 tests.

Test 2: Rewriting a string function

A user grumbled that he was unable to go into dollars and cents into a contribution entry field. As written, my code only enabled dollars. So, the test involves providing the AI the regular that I wrote and asking it to reword it to enable both dollars and cents

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Usually, this leads to the AI producing some routine expression validation code. DeepSeek did create code that works, although there is space for improvement. The code that DeepSeek V2 wrote was unnecessarily long and repetitive while the thinking before creating the code in R1 was also extremely long.

My greatest issue is that both models of the DeepSeek validation makes sure recognition up to 2 decimal locations, however if a huge number is gone into (like 0.30000000000000004), using parseFloat doesn’t have specific rounding . The R1 design likewise utilized JavaScript’s Number conversion without examining for edge case inputs. If bad data returns from an earlier part of the regular expression or a non-string makes it into that conversion, the code would crash.

It’s odd, due to the fact that R1 did present a really good list of tests to confirm against:

So here, we have a split choice. I’m providing the indicate DeepSeek V3 since neither of these issues its code produced would cause the program to break when run by a user and would produce the expected outcomes. On the other hand, I need to provide a fail to R1 because if something that’s not a string in some way gets into the Number function, a crash will occur.

Which provides DeepSeek V3 two wins out of 4, but DeepSeek R1 just one triumph of 4 up until now.

Test 3: Finding an irritating bug

This is a test developed when I had a really irritating bug that I had difficulty tracking down. Once again, I decided to see if ChatGPT could manage it, which it did.

The obstacle is that the response isn’t obvious. Actually, the challenge is that there is an obvious answer, based on the error message. But the obvious response is the wrong answer. This not just caught me, but it regularly catches some of the AIs.

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Solving this bug requires understanding how particular API calls within WordPress work, having the ability to see beyond the mistake message to the code itself, and then understanding where to discover the bug.

Both DeepSeek V3 and R1 passed this one with almost similar answers, bringing us to 3 out of four wins for V3 and 2 out of 4 wins for R1. That currently puts DeepSeek ahead of Gemini, Copilot, Claude, and Meta.

Will DeepSeek score a home run for V3? Let’s discover.

Test 4: Writing a script

And another one bites the dust. This is a challenging test because it needs the AI to understand the interplay in between three environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test because Keyboard Maestro is not a traditional programs tool. But ChatGPT managed the test easily, comprehending exactly what part of the problem is dealt with by each tool.

Also: How ChatGPT scanned 170k lines of code in seconds, conserving me hours of work

Unfortunately, neither DeepSeek V3 or R1 had this level of knowledge. Neither design knew that it needed to divide the job between instructions to Keyboard Maestro and Chrome. It likewise had fairly weak knowledge of AppleScript, composing custom routines for AppleScript that are native to the language.

Weirdly, the R1 design stopped working too due to the fact that it made a bunch of incorrect assumptions. It presumed that a front window always exists, which is certainly not the case. It likewise made the assumption that the currently front running program would always be Chrome, rather than clearly inspecting to see if Chrome was running.

This leaves DeepSeek V3 with 3 right tests and one fail and DeepSeek R1 with two proper tests and two fails.

Final ideas

I found that DeepSeek’s persistence on using a public cloud e-mail address like gmail.com (rather than my regular e-mail address with my corporate domain) was frustrating. It also had a number of responsiveness stops working that made doing these tests take longer than I would have liked.

Also: How to use ChatGPT to compose code: What it does well and what it does not

I wasn’t sure I ‘d be able to write this short article because, for the majority of the day, I got this error when attempting to sign up:

DeepSeek’s online services have actually recently dealt with large-scale harmful attacks. To ensure continued service, registration is temporarily restricted to +86 contact number. Existing users can log in as normal. Thanks for your understanding and assistance.

Then, I got in and was able to run the tests.

DeepSeek seems to be excessively chatty in regards to the code it produces. The AppleScript code in Test 4 was both incorrect and exceedingly long. The routine expression code in Test 2 was right in V3, but it might have been written in a method that made it much more maintainable. It failed in R1.

Also: If ChatGPT produces AI-generated code for your app, who does it truly come from?

I’m certainly amazed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which suggests there’s absolutely room for improvement. I was dissatisfied with the outcomes for the R1 design. Given the choice, I ‘d still pick ChatGPT as my programs code helper.

That said, for a new tool working on much lower facilities than the other tools, this could be an AI to enjoy.

What do you believe? Have you attempted DeepSeek? Are you utilizing any AIs for programs assistance? Let us know in the remarks below.

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