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 past weekend. It sticks out for 3 effective reasons:

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

2. It’s open source.

3. It uses significantly less facilities than the big AI tools we have actually been taking a look at.

Also: Apple scientists reveal the secret sauce behind DeepSeek AI

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

In this short 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 have actually thrown at 10 other big language models. According to DeepSeek itself:

Choose V3 for tasks needing depth and accuracy (e.g., fixing innovative math problems, creating intricate code).

Choose R1 for latency-sensitive, high-volume applications (e.g., consumer assistance automation, fundamental 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 short 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 prowess, way back in the day. My partner required a plugin for WordPress that would help her run an involvement device for her online group.

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Her requirements 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, separate them so they weren’t noted side-by-side.

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

Ever since, it’s been my very first test for AIs when examining their shows skills. It requires the AI to understand how to set up code for the WordPress structure and follow prompts clearly enough to develop both the user 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 created both the interface and program reasoning exactly as specified. As for DeepSeek R1, well that’s an intriguing case. The “thinking” 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 areas. However, both the UI and logic worked, so R1 likewise passes this test.

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

Test 2: Rewriting a string function

A user grumbled that he was not able to go into dollars and cents into a contribution entry field. As written, my code only allowed dollars. So, the test includes providing the AI the regular that I wrote and asking it to rewrite it to permit both dollars and cents

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Usually, this results in the AI creating some regular expression validation code. DeepSeek did create code that works, although there is room for improvement. The code that DeepSeek V2 composed was needlessly long and repetitious while the thinking before generating the code in R1 was likewise long.

My most significant concern is that both models of the DeepSeek validation makes sure validation up to 2 decimal places, however if a very large number is entered (like 0.30000000000000004), the usage of parseFloat doesn’t have explicit rounding knowledge. The R1 design also 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, since R1 did present an extremely good list of tests to validate against:

So here, we have a split choice. I’m giving the point to 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 produce the anticipated outcomes. On the other hand, I need to offer a stop working to R1 because if something that’s not a string somehow enters into the Number function, a crash will take place.

And that offers DeepSeek V3 2 triumphes of 4, however 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 very bothersome bug that I had trouble locating. Once once again, I chose to see if ChatGPT might handle it, which it did.

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

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Solving this bug needs understanding how specific API calls within WordPress work, having the ability to see beyond the error message to the code itself, and after that knowing where to discover the bug.

Both DeepSeek V3 and R1 passed this one with almost identical answers, bringing us to three out of 4 wins for V3 and 2 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 discover out.

Test 4: Writing a script

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

I would have called this an unfair test because Keyboard Maestro is not a traditional programs tool. But ChatGPT dealt with the test quickly, comprehending precisely what part of the issue is dealt with 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 knew that it required to split the task in between guidelines to Keyboard Maestro and Chrome. It likewise had relatively weak understanding of AppleScript, composing customized routines for AppleScript that are native to the language.

Weirdly, the R1 model stopped working as well because it made a lot of inaccurate assumptions. It assumed that a front window always exists, which is absolutely not the case. It likewise made the assumption that the currently front running program would always be Chrome, rather than explicitly examining to see if Chrome was running.

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

Final thoughts

I discovered that DeepSeek’s insistence on utilizing a public cloud email address like gmail.com (rather than my regular email address with my corporate domain) was bothersome. It likewise had a number 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 post because, for the majority of the day, I got this mistake when attempting to sign up:

DeepSeek’s online services have actually recently dealt with massive harmful attacks. To ensure ongoing service, registration is momentarily restricted to +86 phone numbers. Existing users can log in as typical. Thanks for your understanding and assistance.

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 right in V3, but it might have been written in a manner in which made it much more maintainable. It failed 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 appears to be at the old GPT-3.5 level, which indicates there’s definitely room for improvement. I was disappointed with the results for the R1 design. Given the choice, I ‘d still pick ChatGPT as my programs code helper.

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

What do you think? Have you tried DeepSeek? Are you utilizing any AIs for programs assistance? Let us know in the comments below.

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