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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 past weekend. It stands apart for three effective reasons:

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

2. It’s open source.

3. It utilizes significantly less infrastructure than the huge AI tools we have actually been looking at.

Also: Apple researchers expose the secret sauce behind DeepSeek AI

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 produce attention. ZDNET’s Radhika Rajkumar did a deep dive into those concerns in her article Why China’s DeepSeek could burst our AI bubble.

In this short 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 big language models. According to DeepSeek itself:

Choose V3 for jobs requiring depth and precision (e.g., resolving sophisticated mathematics problems, creating intricate code).

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

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

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

Test 1: Writing a WordPress plugin

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

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

Her needs were fairly simple. It needed to take in a list of names, one name per line. It then needed to sort the names, and if there were replicate names, different 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 obstacle on an impulse. To my big surprise, it worked.

Ever since, it’s been my first test for AIs when assessing their programming abilities. It requires the AI to know how to establish code for the WordPress framework and follow prompts plainly adequate to produce both the interface and program reasoning.

Only about half of the AIs I have actually evaluated can completely 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 specified. When It Comes To DeepSeek R1, well that’s an interesting case. The “reasoning” 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 locations. However, both the UI and reasoning worked, so R1 likewise passes this test.

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

Test 2: Rewriting a string function

A user grumbled that he was not able to get in dollars and cents into a donation entry field. As composed, my code only allowed dollars. So, the test involves offering the AI the regular that I wrote and asking it to rewrite it to permit both dollars and cents

Also: My favorite ChatGPT feature just got way more powerful

Usually, this leads to the AI creating some regular expression recognition code. DeepSeek did generate code that works, although there is space for enhancement. The code that DeepSeek V2 composed was needlessly long and repetitious while the reasoning before producing the code in R1 was also really long.

My biggest concern is that both models of the DeepSeek validation makes sure validation as much as 2 decimal places, however if a large number is entered (like 0.30000000000000004), making use of parseFloat does not have specific rounding knowledge. The R1 model likewise used JavaScript’s Number conversion without looking for edge case inputs. If bad data returns from an earlier part of the routine expression or a non-string makes it into that conversion, the code would crash.

It’s odd, due to the fact that R1 did present a really nice list of tests to verify versus:

So here, we have a split choice. I’m giving the point to DeepSeek V3 because neither of these issues its code produced would trigger the program to break when run by a user and would create the expected outcomes. On the other hand, I have to provide a stop working to R1 due to the fact that if something that’s not a string somehow enters the Number function, a crash will ensue.

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

Test 3: Finding an annoying bug

This is a test created when I had a very frustrating bug that I had difficulty finding. Once again, I decided to see if ChatGPT might handle it, which it did.

The challenge is that the answer isn’t obvious. 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 captured me, but it regularly catches 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 requires understanding how specific API calls within WordPress work, having the ability to see beyond the 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 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 discover.

Test 4: Writing a script

And another one bites the dust. This is a difficult test since it requires the AI to comprehend the interaction in between three environments: AppleScript, the Chrome things design, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unreasonable test since Keyboard Maestro is not a mainstream shows tool. But ChatGPT dealt with the test easily, comprehending exactly what part of the issue is handled 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 required to split the task between guidelines to Keyboard Maestro and Chrome. It likewise had fairly weak understanding of AppleScript, composing custom-made routines for AppleScript that are native to the language.

Weirdly, the R1 design failed also since it made a bunch of inaccurate assumptions. It assumed that a front window always exists, which is absolutely not the case. It also 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 3 correct tests and one fail and DeepSeek R1 with 2 appropriate tests and 2 fails.

Final ideas

I found that DeepSeek’s insistence on utilizing a public cloud e-mail address like gmail.com (instead of my typical e-mail address with my business domain) was irritating. It likewise had a variety 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 does not

I wasn’t sure I ‘d be able to write this post due to the fact that, for many of the day, I got this mistake when trying to sign up:

DeepSeek’s online services have recently faced large-scale destructive attacks. To make sure ongoing service, registration is temporarily limited to +86 telephone number. Existing users can visit as typical. Thanks for your understanding and support.

Then, I got in and had the ability to run the tests.

DeepSeek appears to be extremely loquacious in regards to the code it produces. 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 written in a method that made it a lot more maintainable. It stopped working in R1.

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

I’m definitely satisfied that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which implies there’s definitely room for improvement. I was disappointed with the results for the R1 model. Given the option, I ‘d still select ChatGPT as my programs code helper.

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

What do you think? Have you tried DeepSeek? Are you using any AIs for shows support? Let us know in the remarks below.

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