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

DeepSeek took off into the world’s awareness this previous weekend. It stands apart for three effective reasons:

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 big AI tools we have actually been looking at.

Also: Apple researchers reveal the secret sauce behind DeepSeek AI

Given the US government’s issues over TikTok and possible Chinese government involvement because code, a new AI emerging from China is bound to produce attention. ZDNET’s Radhika Rajkumar did a deep dive into those concerns in her short article Why China’s DeepSeek could burst our AI bubble.

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

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

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

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

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

Test 1: Writing a WordPress plugin

This test was in fact my first test of ChatGPT’s programming expertise, method back in the day. My spouse needed a plugin for WordPress that would assist her run a participation gadget for her online group.

Also: The finest AI for coding in 2025 (and what not to use)

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

I didn’t really have time to code it for her, so I chose to provide the AI the difficulty on an impulse. 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 know how to establish code for the WordPress structure and follow triggers plainly adequate to create both the user interface and program logic.

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

DeepSeek V3 created both the user interface and program logic precisely as defined. As for DeepSeek R1, well that’s an intriguing case. The “reasoning” aspect of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.

The UI looked various, with much wider input locations. However, both the UI and logic 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 complained that he was not able to enter dollars and cents into a donation entry field. As composed, my code only enabled dollars. So, the test involves offering the AI the routine that I composed and asking it to rewrite it to enable both dollars and cents

Also: My favorite ChatGPT function just got way more powerful

Usually, this results in the AI creating some regular expression recognition code. DeepSeek did create code that works, although there is room for improvement. The code that DeepSeek V2 wrote was needlessly long and repetitious while the thinking before generating the code in R1 was also long.

My most significant issue is that both models of the DeepSeek recognition ensures validation up to 2 decimal locations, however if a large number is gone into (like 0.30000000000000004), making use of parseFloat doesn’t have specific rounding understanding. The R1 design likewise utilized JavaScript’s Number conversion without looking 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, since R1 did present an extremely good list of tests to verify versus:

So here, we have a split choice. I’m giving the point to DeepSeek V3 since neither of these issues its code produced would trigger the program to break when run by a user and would create the expected results. On the other hand, I need to provide a stop working to R1 since if something that’s not a string in some way enters into the Number function, a crash will ensue.

Which offers DeepSeek V3 2 wins out of 4, but DeepSeek R1 just one triumph of four so far.

Test 3: Finding a bothersome bug

This is a test created when I had a really bothersome bug that I had trouble finding. Once once again, I decided to see if ChatGPT might handle it, which it did.

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

Also: Are ChatGPT Plus or Pro worth it? Here’s how they compare to the complimentary version

Solving this bug needs understanding how specific API calls within WordPress work, being able to see beyond the error message to the code itself, and after that knowing where to discover the bug.

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

Will DeepSeek score a crowning achievement for V3? Let’s find out.

Test 4: Writing a script

And another one bites the dust. This is a tough test due to the fact that it needs the AI to comprehend the interplay between three environments: AppleScript, the Chrome object model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test since Keyboard Maestro is not a mainstream programming tool. But ChatGPT handled the test easily, comprehending exactly what part of the problem is managed 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 model knew that it needed to split the task in between guidelines to Keyboard Maestro and Chrome. It also had relatively weak knowledge of AppleScript, writing customized regimens for AppleScript that are belonging to the language.

Weirdly, the R1 model failed also since it made a bunch of inaccurate presumptions. It presumed that a front window constantly exists, which is certainly not the case. It also made the presumption that the front running program would always be Chrome, instead of clearly checking to see if Chrome was running.

This leaves DeepSeek V3 with 3 correct tests and one stop working and DeepSeek R1 with two proper tests and two fails.

Final ideas

I found that DeepSeek’s insistence on using a public cloud email address like gmail.com (instead of my regular email address with my business domain) was irritating. It likewise had a number of responsiveness fails that made doing these tests take longer than I would have liked.

Also: How to use ChatGPT to write code: What it succeeds and what it doesn’t

I wasn’t sure I ‘d have the ability to compose this article due to the fact that, for the majority of the day, I got this mistake when trying to sign up:

DeepSeek’s online services have just recently dealt with massive destructive attacks. To guarantee ongoing service, registration is briefly restricted to +86 telephone number. Existing users can log in as typical. Thanks for your understanding and assistance.

Then, I got in and had the ability to run the tests.

DeepSeek seems to be excessively loquacious in regards to the code it produces. The AppleScript code in Test 4 was both incorrect and excessively long. The regular expression code in Test 2 was proper in V3, but it could have been written in a way that made it a lot more maintainable. It stopped working in R1.

Also: If ChatGPT produces AI-generated code for your app, who does it actually belong to?

I’m absolutely impressed that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which indicates there’s definitely room for improvement. I was dissatisfied with the outcomes for the R1 model. Given the choice, I ‘d still pick ChatGPT as my programs code assistant.

That said, for a new tool running on much lower facilities than the other tools, this might be an AI to see.

What do you believe? Have you attempted DeepSeek? Are you using any AIs for shows assistance? Let us know in the comments below.

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