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

DeepSeek took off into the world’s awareness this previous weekend. It stands out for 3 powerful factors:

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

2. It’s open source.

3. It uses greatly less infrastructure than the huge AI tools we’ve been looking at.

Also: Apple scientists 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 produce attention. ZDNET’s Radhika Rajkumar did a deep dive into those concerns in her post 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 thrown at 10 other big language designs. According to DeepSeek itself:

Choose V3 for tasks needing depth and accuracy (e.g., fixing advanced math issues, producing intricate code).

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

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

The brief answer is this: impressive, but plainly not ideal. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was actually my first test of ChatGPT’s shows expertise, 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.

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

I didn’t really have time to code it for her, so I decided to provide 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 skills. It requires the AI to understand how to set up code for the WordPress framework and follow prompts clearly adequate to develop both the interface and program logic.

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

DeepSeek V3 produced both the interface and program logic precisely as specified. When It Comes To DeepSeek R1, well that’s an intriguing case. The “reasoning” element of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.

The UI looked various, with much larger 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 grumbled that he was not able to go into dollars and cents into a donation entry field. As written, my code just allowed dollars. So, the test includes offering the AI the routine that I wrote and asking it to rewrite it to enable both dollars and cents

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Usually, this results in the AI generating some regular expression recognition code. DeepSeek did generate code that works, although there is room for improvement. The code that DeepSeek V2 wrote was unnecessarily long and repetitious while the thinking before generating the code in R1 was likewise long.

My greatest issue is that both designs of the DeepSeek validation guarantees validation as much as 2 decimal locations, but if a very big number is gotten in (like 0.30000000000000004), the usage of parseFloat does not have specific rounding knowledge. The R1 design likewise utilized JavaScript’s Number conversion without looking for edge case inputs. If bad information 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 present an extremely great list of tests to verify against:

So here, we have a split decision. I’m offering the indicate DeepSeek V3 due to the fact that neither of these concerns its code produced would cause 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 because if something that’s not a string in some way enters the Number function, a crash will take place.

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

Test 3: Finding a bothersome bug

This is a test created when I had an extremely frustrating bug that I had difficulty locating. Once once again, I chose to see if ChatGPT might handle it, which it did.

The challenge is that the answer isn’t obvious. Actually, the challenge is that there is an obvious response, based upon the error message. But the obvious response is the wrong response. This not only captured me, but it some of the AIs.

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Solving this bug needs understanding 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 find the bug.

Both DeepSeek V3 and R1 passed this one with nearly identical responses, bringing us to 3 out of four wins for V3 and two out of 4 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 challenging test since it needs the AI to comprehend the interaction in between three environments: AppleScript, the Chrome object design, 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 mainstream programming tool. But ChatGPT dealt with the test easily, comprehending precisely what part of the issue 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 required to divide the job between directions to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, writing custom-made regimens for AppleScript that are belonging to the language.

Weirdly, the R1 design failed as well because it made a bunch of inaccurate assumptions. It presumed that a front window constantly exists, which is absolutely not the case. It likewise made the presumption that the currently front running program would always be Chrome, rather than explicitly inspecting to see if Chrome was running.

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

Final ideas

I discovered that DeepSeek’s insistence on using a public cloud email address like gmail.com (rather than my regular e-mail 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 write code: What it does well and what it does not

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

DeepSeek’s online services have actually recently faced massive destructive attacks. To make sure ongoing service, registration is temporarily limited to +86 telephone number. Existing users can log in as typical. Thanks for your understanding and support.

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

DeepSeek seems to be excessively loquacious in terms of the code it produces. The AppleScript code in Test 4 was both wrong and excessively long. The routine expression code in Test 2 was correct in V3, but it could have been composed in a manner in which made it far more maintainable. It failed in R1.

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I’m certainly impressed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which indicates there’s definitely space for improvement. I was dissatisfied with the outcomes for the R1 design. Given the choice, I ‘d still choose ChatGPT as my programming code assistant.

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

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

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