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 stands apart for 3 powerful factors:

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

2. It’s open source.

3. It utilizes greatly less facilities than the big AI tools we’ve been taking a look at.

Also: Apple scientists reveal the secret sauce behind DeepSeek AI

Given the US government’s concerns over TikTok and possible Chinese federal government participation in that 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 might break 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 thrown at 10 other big language designs. According to DeepSeek itself:

Choose V3 for jobs requiring depth and accuracy (e.g., resolving innovative mathematics issues, producing complicated code).

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

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

The short answer is this: outstanding, however clearly not perfect. 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 required a plugin for WordPress that would assist her run an involvement device for her online group.

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

Her requirements were relatively basic. 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 actually have time to code it for her, so I chose to provide the AI the obstacle on a whim. To my big surprise, it worked.

Ever since, it’s been my very first test for AIs when assessing their shows skills. It requires the AI to know how to establish code for the WordPress structure and follow triggers plainly adequate to produce both the user interface and program logic.

Only about half of the AIs I’ve evaluated can completely pass this test. Now, however, we can add one more to the winner’s circle.

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

The UI looked different, with much broader input areas. However, both the UI and logic worked, so R1 also passes this test.

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

Test 2: Rewriting a string function

A user complained that he was unable to enter dollars and cents into a contribution entry field. As composed, my code just enabled dollars. So, the test involves providing the AI the regular that I composed and asking it to rewrite it to permit both dollars and cents

Also: My preferred ChatGPT function simply got method more powerful

Usually, this leads to the AI generating some regular expression validation code. DeepSeek did create code that works, although there is space for enhancement. 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 concern is that both designs of the DeepSeek validation makes sure validation as much as 2 decimal locations, but if a really big number is entered (like 0.30000000000000004), making use of parseFloat does not have specific rounding knowledge. The R1 design likewise used JavaScript’s Number conversion without examining 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 really good list of tests to verify against:

So here, we have a split choice. I’m offering the indicate DeepSeek V3 because neither of these issues its code produced would trigger the program to break when run by a user and would generate the anticipated outcomes. On the other hand, I need to give a stop working to R1 because if something that’s not a string in some way enters into the Number function, a crash will ensue.

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

Test 3: Finding an annoying bug

This is a test developed when I had an extremely annoying bug that I had trouble tracking down. Once once again, I chose to see if ChatGPT could handle it, which it did.

The difficulty is that the response isn’t obvious. Actually, the difficulty is that there is an apparent answer, based upon the mistake message. But the obvious response is the incorrect answer. This not only captured me, but it frequently catches a few of the AIs.

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

Both DeepSeek V3 and R1 passed this one with nearly similar answers, bringing us to three out of four wins for V3 and two out of 4 wins for R1. That currently 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 challenging test due to the fact that it needs the AI to comprehend the interplay between 3 environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unfair test because Keyboard Maestro is not a traditional programming tool. But ChatGPT handled the test quickly, understanding exactly what part of the issue 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 understanding. Neither design knew that it required to split the job between guidelines to Keyboard Maestro and Chrome. It likewise had fairly weak knowledge of AppleScript, writing customized routines for AppleScript that are native to the language.

Weirdly, the R1 design failed too due to the fact that it made a bunch of incorrect assumptions. It assumed that a front window constantly exists, which is absolutely not the case. It also made the assumption that the presently front running program would constantly be Chrome, instead of clearly inspecting to see if Chrome was running.

This leaves DeepSeek V3 with three right tests and one fail and DeepSeek R1 with two appropriate tests and two stops working.

Final thoughts

I found that DeepSeek’s persistence on utilizing a public cloud email address like gmail.com (rather than my normal e-mail address with my business domain) was annoying. It likewise had a number of responsiveness fails that made doing these tests take longer than I would have liked.

Also: How to utilize ChatGPT to compose code: What it does well and what it doesn’t

I wasn’t sure I ‘d have the ability to write this post because, for many of the day, I got this error when trying to register:

DeepSeek’s online services have just recently dealt with large-scale harmful attacks. To ensure ongoing service, registration is momentarily limited to +86 contact 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 appears to be overly loquacious in terms of the code it produces. The AppleScript code in Test 4 was both wrong and excessively long. The routine expression code in Test 2 was correct in V3, but it could have been written in a manner in which made it a lot more maintainable. It failed in R1.

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

I’m certainly impressed that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which means there’s definitely room for enhancement. I was disappointed with the results for the R1 design. Given the choice, I ‘d still select ChatGPT as my programs code helper.

That stated, for a brand-new tool working on much lower facilities than the other tools, this could be an AI to view.

What do you think? Have you attempted DeepSeek? Are you any AIs for programs assistance? Let us understand in the comments listed below.

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