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

DeepSeek exploded into the world’s consciousness this past 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 facilities than the big AI tools we’ve been looking at.

Also: Apple researchers reveal the secret sauce behind DeepSeek AI

Given the US federal 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 problems in her short article Why China’s DeepSeek might rupture 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 large language designs. According to DeepSeek itself:

Choose V3 for jobs needing depth and precision (e.g., fixing advanced math problems, producing intricate code).

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

You can select 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: impressive, however clearly not perfect. Let’s dig in.

Test 1: Writing a WordPress plugin

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

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

Her requirements were relatively simple. It needed to take in a list of names, one name per line. It then needed to arrange the names, and if there were duplicate 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 give the AI the obstacle on a whim. To my substantial surprise, it worked.

Ever since, it’s been my first test for AIs when assessing their shows abilities. It needs the AI to understand how to set up code for the WordPress structure and follow prompts plainly sufficient to create both the interface and program reasoning.

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

DeepSeek V3 created both the interface and program logic exactly as specified. As for DeepSeek R1, well that’s a fascinating case. The “reasoning” 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 locations. However, both the UI and reasoning worked, so R1 also passes this test.

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

Test 2: Rewriting a string function

A user complained that he was not able to enter dollars and cents into a contribution entry field. As written, my code just enabled dollars. So, the test involves giving the AI the routine that I composed and asking it to reword it to permit both dollars and cents

Also: My favorite ChatGPT function just got method more powerful

Usually, this results in the AI creating some routine expression validation code. DeepSeek did produce code that works, although there is room for enhancement. The code that DeepSeek V2 wrote was unnecessarily long and repetitive while the thinking before generating the code in R1 was likewise very long.

My most significant issue is that both designs of the DeepSeek recognition ensures validation approximately 2 decimal places, but if a large number is gotten in (like 0.30000000000000004), using parseFloat does not have specific rounding knowledge. The R1 design also utilized JavaScript’s Number conversion without looking for edge case inputs. If bad information comes back 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 great list of tests to confirm versus:

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

Which gives DeepSeek V3 2 wins out of 4, but DeepSeek R1 only one win out of 4 so far.

Test 3: Finding an annoying bug

This is a test produced when I had a very irritating bug that I had problem tracking down. Once once again, I decided to see if ChatGPT might manage it, which it did.

The obstacle is that the response isn’t apparent. Actually, the difficulty is that there is an apparent answer, based upon the error message. But the obvious answer is the wrong answer. This not just caught me, however it routinely captures a few of the AIs.

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

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

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

Will a crowning achievement 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 comprehend the interplay between 3 environments: AppleScript, the Chrome things design, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test since Keyboard Maestro is not a mainstream programs tool. But ChatGPT handled the test quickly, 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 understood that it needed to divide the job between directions to Keyboard Maestro and Chrome. It also had fairly weak knowledge of AppleScript, composing custom-made regimens for AppleScript that are native to the language.

Weirdly, the R1 design failed too due to the fact that it made a lot of inaccurate assumptions. It presumed that a front window constantly exists, which is certainly not the case. It likewise made the assumption 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 fail and DeepSeek R1 with 2 proper tests and 2 fails.

Final ideas

I discovered that DeepSeek’s insistence on utilizing a public cloud e-mail address like gmail.com (instead of my normal email address with my corporate domain) was bothersome. 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 compose code: What it does well and what it doesn’t

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

DeepSeek’s online services have just recently dealt with massive harmful attacks. To make sure ongoing service, registration is momentarily limited to +86 telephone number. Existing users can log in as usual. 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 creates. The AppleScript code in Test 4 was both wrong and exceedingly long. The regular expression code in Test 2 was right in V3, but it might have been written in a way that made it much more maintainable. It stopped working 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 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which suggests there’s certainly room for enhancement. I was dissatisfied with the results for the R1 model. Given the choice, I ‘d still select ChatGPT as my programs code helper.

That stated, for a new tool operating on much lower infrastructure than the other tools, this could be an AI to enjoy.

What do you believe? Have you tried DeepSeek? Are you utilizing any AIs for programs support? Let us understand in the remarks below.

You can follow my everyday task updates on social media. Make sure to subscribe to my weekly update newsletter, and follow me on Twitter/X at @DavidGewirtz, on Facebook at Facebook.com/ DavidGewirtz, on Instagram at Instagram.com/ DavidGewirtz, on Bluesky at @DavidGewirtz. com, and on YouTube at YouTube.com/ DavidGewirtzTV.

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