DeepSeek exploded into the world's consciousness this previous weekend. It stands out for three powerful factors:

1. It's an AI chatbot from China, instead of the US

2. It's open source.

3. It utilizes vastly less infrastructure than the huge AI tools we've been taking a look at.

Also: Apple scientists expose the secret sauce behind DeepSeek AI

Given the US government's issues 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 could burst our AI bubble.

In this article, we're avoiding politics. Instead, I'm putting both DeepSeek V3 and DeekSeek R1 through the exact same set of AI coding tests I have actually tossed at 10 other big language models. According to DeepSeek itself:

Choose V3 for tasks needing depth and precision (e.g., solving sophisticated mathematics problems, producing complex code).

Choose R1 for latency-sensitive, high-volume applications (e.g., customer support 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 brief answer is this: excellent, but plainly not perfect. Let's dig in.

Test 1: Writing a WordPress plugin

This test was in fact my very first test of ChatGPT's shows prowess, method back in the day. My other half required a plugin for WordPress that would assist her run a participation gadget for her online group.

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Her requirements were fairly simple. It required to take in a list of names, one name per line. It then needed to sort 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 decided to give the AI the obstacle on an impulse. To my huge surprise, it worked.

Since then, it's been my very first test for AIs when evaluating their programs skills. It needs the AI to know how to establish code for the WordPress structure and follow triggers plainly sufficient to create both the user interface and program reasoning.

Only about half of the AIs I have actually checked can totally pass this test. Now, however, we can include another to the winner's circle.

DeepSeek V3 produced both the user interface and program logic precisely as specified. When It Comes To DeepSeek R1, well that's an interesting case. The "reasoning" element of R1 caused 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 logic worked, so R1 also 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 unable to enter dollars and cents into a contribution entry field. As written, my code just enabled dollars. So, the test includes giving the AI the routine that I composed and asking it to rewrite it to enable both dollars and cents

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Usually, this leads to the AI producing some routine expression validation code. DeepSeek did produce code that works, although there is room for improvement. The code that DeepSeek V2 composed was unnecessarily long and repetitive while the thinking before generating the code in R1 was also extremely long.

My biggest issue is that both models of the DeepSeek recognition ensures validation up to 2 decimal locations, however if a large number is entered (like 0.30000000000000004), making use of parseFloat does not have explicit rounding knowledge. The R1 design also utilized 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, since R1 did provide a really great list of tests to confirm against:

So here, we have a split choice. I'm providing the point to DeepSeek V3 due to the fact that neither of these problems its code produced would trigger the program to break when run by a user and would produce the expected results. On the other hand, I need to offer a fail to R1 since if something that's not a string in some way enters into the Number function, a crash will occur.

And that gives DeepSeek V3 2 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 produced when I had a really irritating bug that I had trouble finding. Once once again, I decided to see if ChatGPT might handle it, which it did.

The obstacle is that the answer isn't apparent. Actually, the challenge is that there is an apparent answer, based upon the error message. But the apparent answer is the wrong response. This not just captured me, however it regularly 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 after that knowing where to find the bug.

Both DeepSeek V3 and R1 passed this one with nearly identical answers, bringing us to three out of 4 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 discover out.

Test 4: Writing a script

And another one bites the dust. This is a tough test since it needs the AI to understand the interplay between 3 environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test due to the fact that Keyboard Maestro is not a mainstream programming tool. But ChatGPT handled the test easily, understanding precisely what part of the issue is handled 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 knowledge. Neither model understood that it required to divide the task in between guidelines to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, writing custom-made routines for AppleScript that are belonging to the language.

Weirdly, the R1 design failed too because it made a lot of inaccurate assumptions. It assumed 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 explicitly examining to see if Chrome was running.

This leaves DeepSeek V3 with 3 appropriate tests and one fail and DeepSeek R1 with two correct tests and 2 fails.

Final ideas

I found that DeepSeek's insistence on utilizing a public cloud e-mail address like gmail.com (instead of my regular e-mail address with my corporate domain) was bothersome. It likewise had a variety of responsiveness fails that made doing these tests take longer than I would have liked.

Also: How to use ChatGPT to write code: What it does well and what it doesn't

I wasn't sure I 'd have the ability to compose this article since, for the majority of the day, I got this error when trying to sign up:

DeepSeek's online services have just recently faced large-scale malicious attacks. To make sure continued service, registration is momentarily restricted to +86 telephone number. Existing users can log in as normal. Thanks for your understanding and support.

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

DeepSeek seems to be excessively chatty in terms of the code it produces. The AppleScript code in Test 4 was both wrong and exceedingly long. The regular expression code in Test 2 was correct in V3, however it might have been composed in a manner in which made it a lot more maintainable. It failed in R1.

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

I'm certainly amazed that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which indicates there's definitely room for enhancement. I was disappointed with the outcomes for the R1 model. Given the choice, I 'd still pick ChatGPT as my shows code helper.

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

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

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