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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 consciousness this past weekend. It stands apart for three effective reasons:

1. It’s an AI chatbot from China, instead of 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 scientists expose 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 generate attention. ZDNET’s Radhika Rajkumar did a deep dive into those issues in her post Why China’s DeepSeek might rupture our AI bubble.

In this article, we’re preventing politics. Instead, I’m both DeepSeek V3 and DeekSeek R1 through the same set of AI coding tests I’ve thrown at 10 other large language models. According to DeepSeek itself:

Choose V3 for tasks requiring depth and accuracy (e.g., fixing sophisticated mathematics issues, producing intricate code).

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

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

Test 1: Writing a WordPress plugin

This test was really my first test of ChatGPT’s shows expertise, way back in the day. My better half needed 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 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 listed side-by-side.

I didn’t actually have time to code it for her, so I decided to provide the AI the difficulty on an impulse. To my big surprise, it worked.

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

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

DeepSeek V3 created both the user interface and program reasoning exactly as defined. As for DeepSeek R1, well that’s an intriguing case. The “thinking” 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.

So far, DeepSeek V3 and R1 both passed among four tests.

Test 2: Rewriting a string function

A user complained that he was not able to enter dollars and cents into a donation entry field. As composed, my code only permitted dollars. So, the test involves providing the AI the routine that I wrote and asking it to reword it to enable both dollars and cents

Also: My preferred ChatGPT feature simply got way more powerful

Usually, this results in the AI generating some regular expression validation code. DeepSeek did generate code that works, although there is space for enhancement. The code that DeepSeek V2 wrote was unnecessarily long and repetitious while the reasoning before producing the code in R1 was also really long.

My biggest issue is that both designs of the DeepSeek recognition makes sure recognition as much as 2 decimal locations, however if a very large number is gotten in (like 0.30000000000000004), the usage of parseFloat doesn’t have specific rounding knowledge. The R1 model likewise used JavaScript’s Number conversion without looking for edge case inputs. If bad information 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 provide an extremely great list of tests to verify against:

So here, we have a split decision. I’m providing the indicate DeepSeek V3 since neither of these problems its code produced would cause the program to break when run by a user and would create the anticipated results. On the other hand, I have to provide a fail to R1 because if something that’s not a string somehow enters the Number function, a crash will occur.

And that gives DeepSeek V3 2 triumphes of 4, however DeepSeek R1 just one win out of 4 so far.

Test 3: Finding an irritating bug

This is a test produced when I had a very bothersome bug that I had problem tracking down. Once again, I chose to see if ChatGPT could manage it, which it did.

The difficulty is that the answer isn’t obvious. Actually, the obstacle is that there is an apparent answer, based on the error message. But the obvious response is the wrong response. This not only caught me, but it routinely catches some of the AIs.

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

Solving this bug needs comprehending how particular API calls within WordPress work, being able to see beyond the error message to the code itself, and after that understanding where to discover the bug.

Both DeepSeek V3 and R1 passed this one with nearly identical responses, 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 discover.

Test 4: Writing a script

And another one bites the dust. This is a difficult test due to the fact that it needs the AI to understand the interaction in between three 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 traditional shows tool. But ChatGPT dealt with the test quickly, comprehending exactly what part of the problem 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 design understood that it required to split the task in between directions to Keyboard Maestro and Chrome. It also had fairly weak knowledge of AppleScript, composing custom regimens for AppleScript that are belonging to the language.

Weirdly, the R1 model stopped working as well due to the fact that it made a bunch of incorrect assumptions. It presumed that a front window constantly exists, which is absolutely not the case. It also made the assumption that the currently front running program would constantly be Chrome, instead of explicitly examining to see if Chrome was running.

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

Final thoughts

I discovered 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 utilize ChatGPT to compose code: What it succeeds and what it doesn’t

I wasn’t sure I ‘d be able to compose this post because, for the majority of the day, I got this error when attempting to sign up:

DeepSeek’s online services have just recently faced massive malicious attacks. To guarantee ongoing service, registration is temporarily limited to +86 telephone number. Existing users can visit as normal. Thanks for your understanding and assistance.

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

DeepSeek seems to be extremely loquacious in regards to the code it generates. The AppleScript code in Test 4 was both incorrect and excessively long. The routine expression code in Test 2 was proper in V3, however 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 really come from?

I’m absolutely pleased 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 absolutely room for enhancement. I was dissatisfied with the outcomes for the R1 model. Given the option, I ‘d still pick ChatGPT as my programs code assistant.

That said, for a brand-new tool running on much lower facilities than the other tools, this might be an AI to enjoy.

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

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