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 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 greatly less infrastructure than the big AI tools we have actually been taking a look at.

Also: Apple scientists reveal the secret sauce behind DeepSeek AI

Given the US government’s issues over TikTok and possible Chinese federal government participation because code, a brand-new AI emerging from China is bound to produce attention. ZDNET’s Radhika Rajkumar did a deep dive into those problems 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 very 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 precision (e.g., resolving advanced math issues, creating complex code).

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

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

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

Test 1: Writing a WordPress plugin

This test was really 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 gadget for her online group.

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

Her needs were relatively easy. It required to take in a list of names, one name per line. It then had to sort the names, and if there were replicate names, separate 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 an impulse. To my big surprise, it worked.

Since then, it’s been my very first test for AIs when assessing their shows abilities. It needs the AI to understand how to establish code for the WordPress structure and follow triggers plainly enough to create both the interface and program logic.

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

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

The UI looked different, with much larger input areas. 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 unable to get in dollars and cents into a donation entry field. As written, my code only permitted dollars. So, the test includes giving 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 regular expression recognition code. DeepSeek did generate code that works, although there is room for improvement. The code that DeepSeek V2 composed was needlessly 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 validation makes sure validation up to 2 decimal places, but if a really large number is gotten in (like 0.30000000000000004), using parseFloat doesn’t have explicit rounding knowledge. The R1 model likewise used JavaScript’s Number conversion without checking for edge case inputs. If bad information 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 provide a really great list of tests to verify versus:

So here, we have a split decision. I’m providing the point to 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 anticipated results. On the other hand, I have to give a fail to R1 due to the fact that if something that’s not a string somehow gets into the Number function, a crash will occur.

And that gives DeepSeek V3 2 triumphes of 4, but DeepSeek R1 only one win out of four up until now.

Test 3: Finding an annoying bug

This is a test produced when I had a really bothersome 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 obstacle is that there is an obvious response, based upon the error message. But the apparent answer is the wrong response. This not just captured me, but it routinely catches some of the AIs.

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Solving this bug needs comprehending how particular API calls within WordPress work, having the ability 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 nearly identical responses, 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 home run for V3? Let’s discover.

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 between 3 environments: AppleScript, the Chrome object model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test due to the fact that Keyboard Maestro is not a mainstream shows tool. But ChatGPT handled the test quickly, understanding exactly what part of the problem is dealt with by each tool.

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

Unfortunately, neither DeepSeek V3 or R1 had this level of understanding. Neither design understood that it needed to divide the job between guidelines to Keyboard Maestro and Chrome. It likewise had relatively weak understanding of AppleScript, composing custom-made regimens for AppleScript that are belonging to the language.

Weirdly, the R1 model failed too due to the fact that it made a lot of incorrect presumptions. It presumed that a front window constantly exists, which is certainly not the case. It likewise made the presumption that the presently front running program would constantly be Chrome, instead of explicitly examining to see if Chrome was running.

This leaves DeepSeek V3 with three correct tests and one fail and DeepSeek R1 with two correct tests and 2 fails.

Final thoughts

I discovered that DeepSeek’s persistence on using a public cloud email address like gmail.com (rather than my regular e-mail address with my business 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 be able to write this article since, for many of the day, I got this mistake when trying to register:

DeepSeek’s online services have actually just recently faced large-scale destructive attacks. To ensure ongoing service, registration is temporarily restricted to +86 phone numbers. 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 overly chatty in terms of the code it creates. The AppleScript code in Test 4 was both incorrect and excessively long. The regular expression code in Test 2 was appropriate in V3, however it might have been composed 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 truly belong to?

I’m certainly satisfied that DeepSeek V3 beat out 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 outcomes for the R1 model. Given the choice, I ‘d still pick ChatGPT as my programming code assistant.

That said, for a brand-new tool working 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 understand in the remarks below.

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