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

DeepSeek took off into the world’s consciousness this previous weekend. It sticks out for three effective reasons:

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

2. It’s open source.

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

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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 article Why China’s DeepSeek might burst 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 models. According to DeepSeek itself:

Choose V3 for tasks requiring depth and precision (e.g., resolving advanced math problems, creating intricate code).

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

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

The brief response is this: outstanding, however clearly not ideal. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was in fact my very first test of ChatGPT’s shows prowess, way back in the day. My spouse needed a plugin for WordPress that would help her run a participation gadget for her online group.

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Her needs were fairly 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 duplicate names, separate 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 difficulty on a whim. To my big surprise, it worked.

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

Only about half of the AIs I’ve tested can totally pass this test. Now, however, we can include another to the winner’s circle.

DeepSeek V3 developed both the user interface and program logic exactly as defined. When It Comes To DeepSeek R1, well that’s an interesting case. The “reasoning” 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 4 tests.

Test 2: Rewriting a string function

A user that he was unable to go into dollars and cents into a donation entry field. As written, my code just enabled dollars. So, the test includes offering 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 regular expression recognition code. DeepSeek did produce code that works, although there is room for enhancement. The code that DeepSeek V2 composed was needlessly long and repetitive while the thinking before creating the code in R1 was also long.

My most significant concern is that both models of the DeepSeek recognition ensures recognition as much as 2 decimal locations, but if a large number is gotten in (like 0.30000000000000004), using parseFloat does not have specific rounding understanding. The R1 design also used JavaScript’s Number conversion without looking 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, since R1 did present an extremely nice 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 generate the expected results. On the other hand, I need to provide a fail to R1 because if something that’s not a string somehow enters the Number function, a crash will occur.

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

Test 3: Finding an annoying bug

This is a test created when I had an extremely bothersome bug that I had difficulty tracking down. Once again, I chose to see if ChatGPT could handle it, which it did.

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

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Solving this bug needs comprehending how particular API calls within WordPress work, being able to see beyond the error message to the code itself, and then knowing where to discover the bug.

Both DeepSeek V3 and R1 passed this one with nearly identical answers, bringing us to three out of four wins for V3 and two out of four 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 difficult test because it needs the AI to understand the interaction in between 3 environments: AppleScript, the Chrome object model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unreasonable test because Keyboard Maestro is not a mainstream shows tool. But ChatGPT handled the test easily, understanding precisely what part of the problem is handled by each tool.

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Unfortunately, neither DeepSeek V3 or R1 had this level of knowledge. Neither model understood that it needed to divide the task in between directions to Keyboard Maestro and Chrome. It also had relatively weak understanding of AppleScript, composing custom regimens for AppleScript that are belonging to the language.

Weirdly, the R1 model failed also due to the fact that it made a bunch of inaccurate assumptions. It presumed that a front window always exists, which is absolutely not the case. It also made the assumption that the currently front running program would constantly be Chrome, instead of clearly checking to see if Chrome was running.

This leaves DeepSeek V3 with three appropriate tests and one fail and DeepSeek R1 with 2 proper tests and 2 stops working.

Final ideas

I found that DeepSeek’s persistence on utilizing a public cloud email address like gmail.com (instead of my normal e-mail address with my business domain) was frustrating. It also had a number of responsiveness stops working that made doing these tests take longer than I would have liked.

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I wasn’t sure I ‘d have the ability to compose this short article since, for the majority of the day, I got this error when attempting to register:

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

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

DeepSeek seems to be overly chatty in terms of the code it creates. The AppleScript code in Test 4 was both incorrect and excessively long. The regular expression code in Test 2 was proper in V3, but it might have been written in a manner in which made it far more maintainable. It stopped working in R1.

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

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

That said, for a new tool running on much lower facilities than the other tools, this might be an AI to watch.

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

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