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 previous weekend. It sticks out for 3 powerful reasons:

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

2. It’s open source.

3. It utilizes greatly less facilities than the huge AI tools we have actually been taking a look at.

Also: Apple researchers reveal the secret sauce behind DeepSeek AI

Given the US federal government’s concerns over TikTok and possible Chinese government involvement in that code, a new AI emerging from China is bound to create attention. ZDNET’s Radhika Rajkumar did a deep dive into those problems in her article Why China’s DeepSeek could break our AI bubble.

In this post, 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 tossed at 10 other big language designs. According to DeepSeek itself:

Choose V3 for jobs requiring depth and precision (e.g., resolving innovative mathematics problems, producing complex code).

Choose R1 for latency-sensitive, high-volume applications (e.g., client assistance automation, standard 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, but clearly not best. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was really my first test of ChatGPT’s programming prowess, method back in the day. My spouse required a plugin for WordPress that would assist her run a participation gadget for her online group.

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Her were relatively basic. It required to take in a list of names, one name per line. It then needed to arrange the names, and if there were replicate names, different 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 substantial surprise, it worked.

Since then, it’s been my very first test for AIs when examining their programming abilities. It needs the AI to know how to set up code for the WordPress framework and follow triggers clearly enough to produce 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 another to the winner’s circle.

DeepSeek V3 created both the user interface and program reasoning precisely as defined. As for DeepSeek R1, well that’s an intriguing 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 wider input areas. However, both the UI and reasoning worked, so R1 likewise passes this test.

So far, DeepSeek V3 and R1 both passed among 4 tests.

Test 2: Rewriting a string function

A user complained that he was unable to enter dollars and cents into a donation entry field. As written, my code only permitted dollars. So, the test involves offering the AI the regular that I composed and asking it to reword it to permit both dollars and cents

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Usually, this leads to the AI generating some routine expression recognition code. DeepSeek did produce code that works, although there is space for improvement. The code that DeepSeek V2 wrote was unnecessarily long and repetitious while the reasoning before creating the code in R1 was also long.

My greatest issue is that both designs of the DeepSeek validation ensures recognition approximately 2 decimal places, however if a huge number is entered (like 0.30000000000000004), the use of parseFloat does not have specific rounding knowledge. The R1 model also used JavaScript’s Number conversion without examining 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 present an extremely good list of tests to confirm versus:

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

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

Test 3: Finding a bothersome bug

This is a test created when I had a really frustrating bug that I had difficulty tracking down. Once again, I decided to see if ChatGPT could manage it, which it did.

The challenge is that the response isn’t obvious. Actually, the challenge is that there is an apparent answer, based upon the error message. But the obvious answer is the incorrect response. This not just captured me, however it regularly catches 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 nearly similar responses, bringing us to 3 out of four wins for V3 and two 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 difficult test since it requires the AI to comprehend the interplay between three environments: AppleScript, the Chrome item model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test because Keyboard Maestro is not a traditional shows tool. But ChatGPT managed the test easily, understanding precisely what part of the issue 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 required to split the job between instructions to Keyboard Maestro and Chrome. It likewise had relatively weak understanding of AppleScript, composing customized routines for AppleScript that are belonging to the language.

Weirdly, the R1 design failed also since it made a lot of inaccurate presumptions. It assumed that a front window always exists, which is absolutely not the case. It also made the assumption that the presently front running program would always be Chrome, rather than clearly examining to see if Chrome was running.

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

Final ideas

I found that DeepSeek’s insistence on using a public cloud email address like gmail.com (rather than my normal email address with my corporate domain) was irritating. It likewise 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 succeeds and what it does not

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

DeepSeek’s online services have actually just recently dealt with large-scale harmful attacks. To ensure ongoing service, registration is temporarily restricted to +86 phone numbers. Existing users can visit as typical. Thanks for your understanding and support.

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

DeepSeek seems to be excessively chatty in regards to the code it creates. The AppleScript code in Test 4 was both wrong and exceedingly 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 failed in R1.

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I’m definitely pleased that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which indicates there’s absolutely room for improvement. I was disappointed with the results for the R1 design. Given the option, I ‘d still select ChatGPT as my programming code helper.

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

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

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