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

DeepSeek exploded into the world’s consciousness this previous weekend. It stands out for 3 effective 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 big AI tools we’ve been taking a look at.

Also: Apple scientists expose the secret sauce behind DeepSeek AI

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

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

Choose V3 for jobs needing depth and accuracy (e.g., solving innovative mathematics issues, creating complex 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 utilizing R1.

The brief answer is this: excellent, however clearly not best. 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 better half needed a plugin for WordPress that would assist her run a participation device for her online group.

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

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

I didn’t really have time to code it for her, so I chose to offer the AI the challenge on an impulse. To my big surprise, it worked.

Since then, it’s been my first test for AIs when examining their programs skills. It needs the AI to understand how to set up code for the WordPress structure and follow prompts clearly enough to create both the interface and program reasoning.

Only about half of the AIs I have actually evaluated can totally pass this test. Now, however, 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 a fascinating case. The “thinking” element of R1 caused the AI to spit out 4502 words of analysis before sharing the code.

The UI looked different, with much larger input areas. 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 grumbled that he was unable to go into dollars and cents into a contribution entry field. As composed, my code just allowed dollars. So, the test involves offering the AI the regular that I wrote and asking it to reword it to allow for both dollars and cents

Also: My favorite ChatGPT function just got method more powerful

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

My most significant issue is that both models of the DeepSeek recognition makes sure recognition approximately 2 decimal locations, but if a large number is gone into (like 0.30000000000000004), the 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 provide an extremely great list of tests to validate against:

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

And that offers DeepSeek V3 2 wins out of 4, but DeepSeek R1 only one win out of four so far.

Test 3: Finding an annoying bug

This is a test produced when I had a very annoying bug that I had trouble finding. Once once again, I decided to see if ChatGPT might handle it, which it did.

The difficulty is that the response isn’t obvious. Actually, the challenge is that there is an obvious answer, based on the mistake message. But the apparent answer is the incorrect response. This not just caught me, however it routinely captures a few of the AIs.

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Solving this bug requires comprehending how specific API calls within WordPress work, being able to see beyond the mistake message to the code itself, and then knowing where to find the bug.

Both DeepSeek V3 and R1 passed this one with almost identical responses, bringing us to 3 out of 4 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 crowning achievement 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 understand the interaction between three environments: AppleScript, the Chrome object 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 programs tool. But ChatGPT managed the test easily, understanding precisely what part of the problem is dealt with 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 design understood that it needed to divide the job in between directions to Keyboard Maestro and Chrome. It also had fairly weak knowledge of AppleScript, writing customized routines for AppleScript that are belonging to the language.

Weirdly, the R1 model stopped working too since it made a lot of incorrect assumptions. It presumed that a front window always exists, which is definitely not the case. It also made the assumption that the presently front running program would constantly be Chrome, instead of explicitly inspecting to see if Chrome was running.

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

Final ideas

I discovered that DeepSeek’s persistence on utilizing a public cloud e-mail address like gmail.com (rather than my normal e-mail address with my corporate domain) was frustrating. It also had a number 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 have the ability to write this post due to the fact that, for the majority of the day, I got this error when attempting to sign up:

DeepSeek’s online services have actually just recently faced large-scale malicious attacks. To guarantee continued service, registration is temporarily limited to +86 contact number. Existing users can visit as typical. Thanks for your understanding and assistance.

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

DeepSeek seems to be overly loquacious in regards to the code it generates. The AppleScript code in Test 4 was both incorrect and exceedingly long. The regular expression code in Test 2 was right in V3, however it could have been written in a manner in which made it much more maintainable. It stopped working 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 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which indicates there’s certainly room for improvement. I was dissatisfied with the outcomes for the R1 design. Given the choice, I ‘d still pick ChatGPT as my shows code helper.

That said, for a brand-new tool working on much lower infrastructure than the other tools, this might be an AI to see.

What do you believe? Have you attempted DeepSeek? Are you utilizing any AIs for programs assistance? Let us know in the remarks listed below.

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