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 factors:

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

2. It’s open source.

3. It utilizes vastly less infrastructure than the big AI tools we have actually been looking at.

Also: Apple scientists expose the secret sauce behind DeepSeek AI

Given the US government’s issues over TikTok and possible Chinese government involvement in that code, a new AI emerging from China is bound to generate attention. ZDNET’s Radhika Rajkumar did a deep dive into those issues in her short article Why China’s DeepSeek could rupture our AI bubble.

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

Choose V3 for jobs requiring depth and accuracy (e.g., solving sophisticated mathematics problems, generating complicated code).

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

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

The short answer is this: outstanding, however clearly not best. Let’s dig in.

Test 1: Writing a WordPress plugin

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

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Her requirements were fairly basic. 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 noted side-by-side.

I didn’t really have time to code it for her, so I decided to provide the AI the challenge on a whim. To my huge surprise, it worked.

Since then, it’s been my first test for AIs when evaluating their programs skills. It needs the AI to know how to set up code for the WordPress framework and follow prompts clearly sufficient to develop both the user interface and program reasoning.

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

DeepSeek V3 created both the user interface and program logic precisely as specified. As for DeepSeek R1, well that’s an intriguing case. The “reasoning” aspect of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.

The UI looked different, with much larger input locations. However, both the UI and logic worked, so R1 also passes this test.

Up until now, DeepSeek V3 and R1 both passed among four tests.

Test 2: Rewriting a string function

A user grumbled that he was unable to get in dollars and cents into a donation entry field. As composed, my code only permitted dollars. So, the test includes providing the AI the routine that I composed and asking it to reword it to enable for both dollars and cents

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Usually, this leads to the AI creating some routine expression recognition code. DeepSeek did produce code that works, although there is room for improvement. The code that DeepSeek V2 wrote was unnecessarily long and repetitive while the thinking before producing 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 places, but if a huge number is entered (like 0.30000000000000004), making use of parseFloat doesn’t have explicit rounding understanding. The R1 design also utilized JavaScript’s Number conversion without examining 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, due to the fact that R1 did provide an extremely nice list of tests to verify versus:

So here, we have a split choice. I’m providing the point to 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 anticipated results. On the other hand, I need to give a stop working to R1 because if something that’s not a string somehow enters the Number function, a crash will ensue.

Which gives DeepSeek V3 two triumphes of 4, however DeepSeek R1 only one triumph of 4 so far.

Test 3: Finding a frustrating bug

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

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

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Solving this bug requires comprehending how specific API calls within WordPress work, having the ability to see beyond the mistake message to the code itself, and after that knowing where to discover 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 4 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 tough test since it requires the AI to understand the interplay in between 3 environments: AppleScript, the Chrome object design, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test because Keyboard Maestro is not a traditional shows tool. But ChatGPT handled the test quickly, comprehending exactly what part of the issue is handled 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 design knew that it needed to divide the job between to Keyboard Maestro and Chrome. It also had fairly weak understanding of AppleScript, writing customized regimens for AppleScript that are native to the language.

Weirdly, the R1 model failed too because it made a bunch of incorrect assumptions. It assumed that a front window always exists, which is absolutely not the case. It likewise made the assumption that the presently front running program would always be Chrome, rather than clearly examining to see if Chrome was running.

This leaves DeepSeek V3 with 3 appropriate tests and one stop working and DeepSeek R1 with two proper 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 typical e-mail address with my corporate domain) was bothersome. It also had a number of responsiveness fails that made doing these tests take longer than I would have liked.

Also: How to utilize ChatGPT to compose code: What it succeeds and what it doesn’t

I wasn’t sure I ‘d be able to write this post due to the fact that, for most of the day, I got this mistake when attempting to register:

DeepSeek’s online services have recently faced large-scale malicious attacks. To ensure ongoing service, registration is momentarily limited to +86 phone numbers. Existing users can log in as typical. Thanks for your understanding and support.

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

DeepSeek appears to be overly chatty in regards to the code it produces. The AppleScript code in Test 4 was both incorrect and exceedingly long. The routine expression code in Test 2 was proper in V3, however it could 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 belong to?

I’m certainly impressed that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which means there’s absolutely space for enhancement. I was disappointed with the results for the R1 model. Given the choice, I ‘d still select ChatGPT as my programming code assistant.

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

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

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