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

DeepSeek exploded into the world’s awareness this past weekend. It stands apart for three powerful factors:

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

2. It’s open source.

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

Also: Apple researchers expose the secret sauce behind DeepSeek AI

Given the US government’s issues over TikTok and possible Chinese government involvement 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 post Why China’s DeepSeek could burst our AI bubble.

In this post, 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 large language models. According to DeepSeek itself:

Choose V3 for tasks requiring depth and precision (e.g., fixing innovative math problems, creating intricate code).

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

You can pick 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 answer is this: remarkable, but clearly not best. Let’s dig in.

Test 1: Writing a WordPress plugin

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

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

Her requirements were fairly 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 listed side-by-side.

I didn’t truly have time to code it for her, so I decided to offer the AI the challenge on a whim. To my big surprise, it worked.

Since then, it’s been my very first test for AIs when examining their shows skills. It needs the AI to understand how to set up code for the WordPress framework and follow triggers plainly sufficient to produce both the interface and program reasoning.

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

DeepSeek V3 produced both the interface and program reasoning precisely as specified. As for DeepSeek R1, well that’s an intriguing 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 larger 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 4 tests.

Test 2: Rewriting a string function

A user complained that he was unable to enter dollars and cents into a donation entry field. As written, my code only allowed dollars. So, the test includes providing the AI the regular that I wrote and asking it to rewrite it to permit both dollars and cents

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Usually, this results in the AI creating some routine expression recognition code. DeepSeek did create code that works, although there is space for enhancement. The code that DeepSeek V2 composed was needlessly long and repetitious while the thinking before generating the code in R1 was likewise long.

My biggest issue is that both designs of the DeepSeek recognition guarantees recognition approximately 2 decimal places, but if a huge number is gone into (like 0.30000000000000004), using parseFloat does not have specific rounding knowledge. The R1 model likewise used JavaScript’s Number conversion without inspecting 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, because R1 did present an extremely good list of tests to verify against:

So here, we have a split decision. I’m providing the point to DeepSeek V3 due to the fact that neither of these concerns its code produced would cause the program to break when run by a user and would produce the expected outcomes. On the other hand, I need to give a stop working to R1 since if something that’s not a string somehow enters the Number function, a crash will take place.

Which offers DeepSeek V3 2 wins out of 4, but DeepSeek R1 just one triumph of four up until now.

Test 3: Finding an irritating bug

This is a test produced when I had an extremely frustrating bug that I had trouble locating. Once once again, I decided to see if ChatGPT might handle it, which it did.

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

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Solving this bug requires understanding how particular API calls within WordPress work, having the ability to see beyond the error message to the code itself, and after that 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 2 out of 4 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 challenging test because it needs the AI to comprehend the interaction between three environments: AppleScript, the Chrome object design, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unreasonable test because Keyboard Maestro is not a traditional programming tool. But ChatGPT handled the test quickly, understanding precisely what part of the issue is handled 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 model knew that it required to divide the task in between directions to Keyboard Maestro and Chrome. It likewise had fairly weak knowledge of AppleScript, composing custom regimens for AppleScript that are native to the language.

Weirdly, the R1 model failed as well since it made a bunch of inaccurate presumptions. It presumed that a front window constantly exists, which is absolutely not the case. It likewise made the presumption that the currently front running program would constantly be Chrome, instead of clearly examining to see if Chrome was running.

This leaves DeepSeek V3 with three appropriate tests and one stop working and DeepSeek R1 with 2 proper tests and 2 stops working.

Final thoughts

I found that DeepSeek’s persistence on utilizing a public cloud email address like gmail.com (rather than my typical e-mail address with my corporate domain) was annoying. It likewise had a variety 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 short article since, for the majority of the day, I got this mistake when trying to sign up:

DeepSeek’s online services have just recently faced large-scale malicious attacks. To ensure continued service, registration is temporarily limited to +86 phone numbers. Existing users can log in as typical. Thanks for your understanding and assistance.

Then, I got in and was able to run the tests.

DeepSeek seems to be 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 correct in V3, but it might have been written in a manner in which 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 come from?

I’m absolutely amazed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which implies there’s absolutely space for enhancement. I was dissatisfied with the results for the R1 model. Given the choice, I ‘d still pick ChatGPT as my programs code helper.

That said, for a new tool operating 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 using any AIs for programming support? Let us understand in the comments listed below.

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