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 out for three effective reasons:

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 have actually been looking at.

Also: Apple scientists reveal the secret sauce behind DeepSeek AI

Given the US government’s concerns over TikTok and possible Chinese 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 problems in her short article Why China’s DeepSeek might rupture our AI bubble.

In this post, we’re avoiding politics. Instead, I’m putting both DeepSeek V3 and DeekSeek R1 through the exact same set of AI coding tests I have actually thrown at 10 other big language models. According to DeepSeek itself:

Choose V3 for jobs needing depth and accuracy (e.g., solving innovative math issues, creating intricate code).

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

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

The short response is this: remarkable, 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 programming prowess, method back in the day. My other half 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 requirements were relatively simple. It needed to take in a list of names, one name per line. It then had to arrange the names, and if there were replicate names, different them so they weren’t listed side-by-side.

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

Ever since, it’s been my very first test for AIs when evaluating their programming skills. It needs the AI to understand how to set up code for the WordPress structure and follow triggers clearly enough to create both the user interface and program logic.

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

DeepSeek V3 created both the interface and program reasoning exactly as defined. When It Comes To 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 various, with much wider input locations. However, both the UI and reasoning worked, so R1 also passes this test.

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

Test 2: Rewriting a string function

A user complained that he was not able to get in dollars and cents into a donation entry field. As written, my code only allowed dollars. So, the test includes providing the AI the routine that I composed and asking it to rewrite it to permit both dollars and cents

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Usually, this results in the AI generating some routine expression validation code. DeepSeek did generate code that works, although there is space for improvement. The code that DeepSeek V2 wrote was needlessly long and repetitious while the thinking before creating the code in R1 was likewise really long.

My greatest concern is that both models of the DeepSeek recognition ensures recognition up to 2 decimal locations, but if a really large number is gone into (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 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 provide a very good list of tests to validate against:

So here, we have a split decision. I’m offering the point to DeepSeek V3 due to the fact that neither of these problems its code produced would cause the program to break when run by a user and would produce the anticipated outcomes. On the other hand, I have to provide a fail to R1 because if something that’s not a string somehow enters into the Number function, a crash will ensue.

Which provides DeepSeek V3 two triumphes of 4, but DeepSeek R1 only one triumph of four up until now.

Test 3: Finding a frustrating bug

This is a test produced when I had an extremely annoying bug that I had difficulty locating. Once again, I decided to see if ChatGPT could handle it, which it did.

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

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

Both DeepSeek V3 and R1 passed this one with almost similar responses, bringing us to 3 out of 4 wins for V3 and 2 out of four wins for R1. That already 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 tough test because it needs the AI to comprehend the interaction between three environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unreasonable test due to the fact that Keyboard Maestro is not a traditional shows tool. But ChatGPT dealt with the test easily, comprehending precisely what part of the problem is managed 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 understood that it needed to divide the job in between directions to Keyboard Maestro and Chrome. It also had relatively weak knowledge of AppleScript, composing customized regimens for AppleScript that are native to the language.

Weirdly, the R1 design stopped working too since it made a lot of inaccurate presumptions. It assumed that a front window constantly exists, which is certainly not the case. It also made the assumption 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 3 right tests and one stop working and DeepSeek R1 with two correct tests and 2 stops working.

Final ideas

I found that DeepSeek’s persistence on using a public cloud email address like gmail.com (rather than my regular email address with my corporate domain) was frustrating. It also had a number of responsiveness stops working 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 does not

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

DeepSeek’s online services have recently faced large-scale destructive attacks. To guarantee continued service, registration is temporarily restricted to +86 telephone number. Existing users can log in as usual. Thanks for your understanding and assistance.

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

DeepSeek appears to be excessively chatty in terms of the code it generates. The AppleScript code in Test 4 was both incorrect and exceedingly long. The routine expression code in Test 2 was appropriate in V3, however it could have been written in a manner in which made it much more maintainable. It failed in R1.

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

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 enhancement. I was dissatisfied with the outcomes for the R1 design. Given the option, I ‘d still select ChatGPT as my programming code helper.

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 tried DeepSeek? Are you using any AIs for programs assistance? Let us know in the remarks listed below.

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