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

DeepSeek exploded into the world’s consciousness this previous weekend. It sticks out for three effective reasons:

1. It’s an AI chatbot from China, rather than the US

2. It’s open source.

3. It uses significantly less infrastructure than the huge AI tools we’ve been looking at.

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

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

Choose V3 for tasks requiring depth and precision (e.g., solving sophisticated math problems, generating complex code).

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

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

The short answer is this: impressive, however clearly not ideal. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was actually my first test of ChatGPT’s shows prowess, method back in the day. My spouse needed a plugin for WordPress that would assist her run a participation device for her online group.

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

I didn’t really have time to code it for her, so I decided to provide the AI the difficulty on an impulse. To my substantial surprise, it worked.

Ever since, it’s been my very first test for AIs when assessing their programming abilities. It needs the AI to know how to set up code for the WordPress framework and follow prompts clearly sufficient to develop both the user interface and program reasoning.

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

DeepSeek V3 produced both the user interface and program logic exactly as defined. As for DeepSeek R1, well that’s an interesting case. The “thinking” aspect of R1 caused the AI to spit out 4502 words of analysis before sharing the code.

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

Up until now, DeepSeek V3 and R1 both passed one of 4 tests.

Test 2: Rewriting a string function

A user complained that he was unable to get in dollars and cents into a donation entry field. As written, my code just allowed dollars. So, the test involves giving the AI the routine that I composed and asking it to rewrite it to enable both dollars and cents

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Usually, this results in the AI producing some regular expression recognition code. DeepSeek did produce code that works, although there is room for improvement. The code that DeepSeek V2 composed was unnecessarily long and repetitious while the reasoning before producing the code in R1 was likewise long.

My most significant issue is that both models of the DeepSeek recognition guarantees validation up to 2 decimal places, but if a huge number is entered (like 0.30000000000000004), the use of parseFloat doesn’t have explicit rounding knowledge. The R1 model also used JavaScript’s Number conversion without looking for edge case inputs. If bad data comes back from an earlier part of the routine expression or a non-string makes it into that conversion, the code would crash.

It’s odd, since R1 did present a very great list of tests to confirm versus:

So here, we have a split decision. I’m providing the indicate DeepSeek V3 because neither of these problems its code produced would cause the program to break when run by a user and would generate the expected outcomes. On the other hand, I need to provide a fail to R1 due to the fact that if something that’s not a string somehow enters the Number function, a crash will ensue.

And that offers DeepSeek V3 2 wins out of 4, but DeepSeek R1 just one win out of 4 up until now.

Test 3: Finding a frustrating bug

This is a test produced when I had an extremely annoying bug that I had problem tracking down. Once once again, I decided to see if ChatGPT might handle it, which it did.

The obstacle is that the answer isn’t obvious. Actually, the challenge is that there is an apparent answer, based upon the mistake message. But the obvious response is the incorrect answer. This not just caught me, however it routinely captures some of the AIs.

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

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

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

Test 4: Writing a script

And another one bites the dust. This is a tough test due to the fact that it requires the AI to understand the interaction in between 3 environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unreasonable test because Keyboard Maestro is not a traditional programs tool. But ChatGPT dealt with the test quickly, comprehending precisely what part of the problem is handled by each tool.

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Unfortunately, neither DeepSeek V3 or R1 had this level of knowledge. Neither model knew that it needed to split the task in between directions to Keyboard Maestro and Chrome. It likewise had fairly weak knowledge of AppleScript, writing custom-made regimens for AppleScript that are belonging to the language.

Weirdly, the R1 design stopped working too because it made a lot of inaccurate presumptions. It presumed that a front window always exists, which is absolutely not the case. It also made the presumption that the currently front running program would always be Chrome, rather than explicitly examining to see if Chrome was running.

This leaves DeepSeek V3 with three correct tests and one stop working and DeepSeek R1 with 2 correct tests and two stops working.

Final thoughts

I found that DeepSeek’s insistence on using a public cloud e-mail address like gmail.com (rather than my typical email address with my business domain) was annoying. It also 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 does not

I wasn’t sure I ‘d be able to compose this post due to the fact that, for many of the day, I got this error when trying to register:

DeepSeek’s online services have actually just recently dealt with massive destructive attacks. To guarantee continued service, registration is briefly restricted to +86 telephone number. Existing users can visit 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 creates. The AppleScript code in Test 4 was both incorrect and excessively long. The routine expression code in Test 2 was right in V3, but it could have been written in a method that made it much more maintainable. It stopped working in R1.

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

I’m certainly satisfied that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which suggests there’s definitely room for improvement. I was dissatisfied with the results for the R1 design. Given the choice, I ‘d still select ChatGPT as my programming code assistant.

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

What do you believe? Have you attempted DeepSeek? Are you utilizing any AIs for programming support? Let us understand in the comments listed below.

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