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 powerful factors:

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

2. It’s open source.

3. It uses vastly 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 federal government’s concerns over TikTok and possible Chinese federal government participation 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 problems in her post Why China’s DeepSeek could burst our AI bubble.

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

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

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

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

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

Test 1: Writing a WordPress plugin

This test was actually my very first test of ChatGPT’s shows prowess, method back in the day. My partner required a plugin for WordPress that would assist her run an involvement gadget for her online group.

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Her needs were relatively simple. 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, different them so they weren’t listed side-by-side.

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

Since then, it’s been my very first test for AIs when assessing their programs skills. It requires the AI to know how to establish code for the WordPress framework and follow triggers plainly adequate to develop both the interface and program logic.

Only about half of the AIs I’ve tested can totally pass this test. Now, nevertheless, we can add one more to the winner’s circle.

DeepSeek V3 produced both the interface and program logic exactly as specified. 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 different, with much wider input areas. However, both the UI and logic worked, so R1 also passes this test.

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

Test 2: Rewriting a string function

A user complained that he was unable to get in dollars and cents into a contribution entry field. As written, my code just enabled dollars. So, the test involves offering the AI the regular that I wrote and asking it to reword it to permit both dollars and cents

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

My most significant concern is that both models of the DeepSeek recognition makes sure validation approximately 2 decimal places, but if a huge number is entered (like 0.30000000000000004), making use of parseFloat does not have explicit rounding understanding. The R1 model also used JavaScript’s Number conversion without checking 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 provide an extremely nice list of tests to verify against:

So here, we have a split choice. I’m giving the indicate DeepSeek V3 due to the fact that neither of these issues 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 need to offer a stop working to R1 since if something that’s not a string somehow enters the Number function, a crash will take place.

And that offers DeepSeek V3 two wins out of 4, however DeepSeek R1 only one triumph of 4 so far.

Test 3: Finding an irritating bug

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

The difficulty is that the answer isn’t apparent. Actually, the difficulty is that there is an apparent response, based on the error message. But the obvious answer is the incorrect answer. This not only caught me, but it frequently captures some of the AIs.

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Solving this bug requires understanding how specific API calls within WordPress work, having the ability to see beyond the mistake message to the code itself, and then knowing where to discover the bug.

Both DeepSeek V3 and R1 passed this one with almost similar responses, bringing us to three out of four wins for V3 and 2 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 find out.

Test 4: Writing a script

And another one bites the dust. This is a challenging test because it requires the AI to comprehend the interaction between three environments: AppleScript, the Chrome item model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test since Keyboard Maestro is not a mainstream programming tool. But ChatGPT managed the test quickly, understanding exactly what part of the problem is managed 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 model understood that it needed to divide the task between guidelines to Keyboard Maestro and Chrome. It likewise had fairly weak knowledge of AppleScript, composing custom routines for AppleScript that are native to the language.

Weirdly, the R1 design failed also due to the fact that it made a lot of inaccurate presumptions. It presumed that a front window constantly exists, which is absolutely not the case. It likewise made the assumption that the currently front running program would always be Chrome, instead of explicitly examining to see if Chrome was running.

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

Final thoughts

I discovered that DeepSeek’s insistence on using a public cloud e-mail address like gmail.com (instead of my regular e-mail address with my business domain) was bothersome. It likewise had a variety 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 have the ability to write this short article since, for many of the day, I got this error when attempting to register:

DeepSeek’s online services have actually just recently dealt with massive harmful attacks. To make sure continued service, registration is briefly limited to +86 telephone number. Existing users can visit as normal. Thanks for your understanding and assistance.

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

DeepSeek appears to be excessively chatty 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, however it might have been composed in a manner in which made it far more maintainable. It failed in R1.

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I’m absolutely amazed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which suggests there’s definitely room for enhancement. I was disappointed with the outcomes for the R1 design. Given the choice, I ‘d still pick ChatGPT as my programs code assistant.

That stated, for a new tool operating on much lower facilities than the other tools, this might be an AI to see.

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

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