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

DeepSeek exploded into the world’s awareness this previous weekend. It stands out for 3 powerful reasons:

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

2. It’s open source.

3. It uses significantly less facilities than the big 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 federal government involvement in that code, a brand-new AI emerging from China is bound to create attention. ZDNET’s Radhika Rajkumar did a deep dive into those problems in her post Why China’s DeepSeek could rupture our AI bubble.

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

Choose V3 for tasks needing depth and accuracy (e.g., solving sophisticated mathematics problems, producing complicated code).

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

The short answer is this: outstanding, however plainly not best. Let’s dig in.

Test 1: Writing a WordPress plugin

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

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

Her requirements were fairly basic. It needed to take in a list of names, one name per line. It then needed to arrange the names, and if there were replicate names, separate them so they weren’t listed side-by-side.

I didn’t really have time to code it for her, so I decided to give the AI the obstacle on a whim. To my substantial surprise, it worked.

Ever since, it’s been my first test for AIs when evaluating their shows abilities. It needs the AI to understand how to set up code for the WordPress structure and follow triggers plainly adequate to develop both the interface and program logic.

Only about half of the AIs I’ve checked can totally pass this test. Now, nevertheless, we can add one more 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 interesting case. The “reasoning” element of R1 triggered 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 logic worked, so R1 likewise 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 not able to get in dollars and cents into a donation entry field. As composed, my code just allowed dollars. So, the test includes offering the AI the regular that I wrote and asking it to reword it to permit both dollars and cents

Also: My preferred ChatGPT feature just got way more powerful

Usually, this leads to the AI producing some regular expression validation 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 thinking before producing the code in R1 was also really long.

My greatest issue is that both models of the DeepSeek recognition guarantees recognition approximately 2 decimal places, however if a huge number is gone into (like 0.30000000000000004), making use of parseFloat doesn’t have specific rounding knowledge. The R1 design also used JavaScript’s Number conversion without examining for edge case inputs. If bad data comes back from an earlier part of the regular 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 very nice list of tests to verify against:

So here, we have a split choice. I’m providing the indicate DeepSeek V3 since neither of these problems its code produced would trigger the program to break when run by a user and would generate the expected outcomes. On the other hand, I have to provide a stop working to R1 since if something that’s not a string in some way enters the Number function, a crash will take place.

Which provides DeepSeek V3 two triumphes of 4, but DeepSeek R1 just one win out of 4 so far.

Test 3: Finding an annoying bug

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

The obstacle is that the response isn’t obvious. Actually, the obstacle is that there is an obvious response, based on the mistake message. But the apparent answer is the incorrect response. This not only captured me, however it regularly captures a few of the AIs.

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Solving this bug requires understanding how specific API calls within WordPress work, being able to see beyond the error message to the code itself, and after that knowing where to find the bug.

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

I would have called this an unfair test due to the fact that Keyboard Maestro is not a traditional programming tool. But ChatGPT handled the test quickly, understanding 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 understanding. Neither model knew that it required to divide the job between instructions to Keyboard Maestro and Chrome. It also had fairly weak understanding of AppleScript, writing custom-made routines for AppleScript that are belonging to the language.

Weirdly, the R1 model stopped working too since it made a bunch of inaccurate assumptions. It presumed that a front window always exists, which is absolutely not the case. It likewise made the assumption that the currently front would constantly be Chrome, instead of explicitly inspecting to see if Chrome was running.

This leaves DeepSeek V3 with 3 correct tests and one stop working and DeepSeek R1 with 2 correct tests and two stops working.

Final ideas

I found that DeepSeek’s persistence on using a public cloud e-mail address like gmail.com (instead of my regular e-mail address with my business domain) was frustrating. It likewise had a number of responsiveness stops working 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 be able to write this post since, for the majority of the day, I got this mistake when attempting to sign up:

DeepSeek’s online services have just recently faced large-scale malicious attacks. To guarantee continued service, registration is temporarily restricted to +86 phone numbers. Existing users can visit as normal. Thanks for your understanding and support.

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

DeepSeek appears to be overly chatty in terms of the code it generates. The AppleScript code in Test 4 was both wrong and exceedingly long. The regular expression code in Test 2 was appropriate in V3, but it could have been written in a way that made it much more maintainable. It failed in R1.

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

I’m certainly pleased that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which implies there’s absolutely space for improvement. I was disappointed with the outcomes for the R1 model. Given the option, I ‘d still pick ChatGPT as my programming code assistant.

That stated, for a brand-new tool operating on much lower infrastructure than the other tools, this could be an AI to enjoy.

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

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