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

DeepSeek took off into the world’s awareness this previous weekend. It stands apart for 3 effective factors:

1. It’s an AI chatbot from China, rather than 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 expose the secret sauce behind DeepSeek AI

Given the US federal government’s concerns over TikTok and possible Chinese federal government involvement in that code, a brand-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 preventing 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 designs. According to DeepSeek itself:

Choose V3 for jobs needing depth and precision (e.g., resolving innovative mathematics issues, producing complicated code).

Choose R1 for latency-sensitive, high-volume applications (e.g., client assistance 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 short answer is this: outstanding, but plainly not best. Let’s dig in.

Test 1: Writing a WordPress plugin

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

Also: The very best AI for coding in 2025 (and what not to use)

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 duplicate names, different them so they weren’t noted side-by-side.

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

Since then, it’s been my first test for AIs when examining their shows skills. It needs the AI to know how to set up code for the WordPress structure and follow triggers clearly sufficient to create both the user interface and program logic.

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

DeepSeek V3 produced both the user interface and program logic exactly as defined. As for DeepSeek R1, well that’s a fascinating case. The “reasoning” element 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.

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

Test 2: Rewriting a string function

A user complained that he was unable to go into dollars and cents into a donation entry field. As composed, my code only enabled dollars. So, the test involves providing the AI the routine that I composed and asking it to reword it to permit for both dollars and cents

Also: My preferred ChatGPT function just got way more effective

Usually, this leads to the AI generating some regular expression recognition code. DeepSeek did create code that works, although there is room for . The code that DeepSeek V2 wrote was needlessly long and repetitive while the thinking before creating the code in R1 was also very long.

My most significant concern is that both designs of the DeepSeek validation ensures validation approximately 2 decimal places, however if an extremely large number is entered (like 0.30000000000000004), using parseFloat doesn’t have explicit rounding understanding. The R1 model likewise utilized JavaScript’s Number conversion without examining for edge case inputs. If bad information returns from an earlier part of the regular expression or a non-string makes it into that conversion, the code would crash.

It’s odd, due to the fact that R1 did present an extremely good list of tests to confirm against:

So here, we have a split choice. I’m offering 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 create the anticipated results. On the other hand, I need to offer a stop working to R1 because if something that’s not a string in some way gets into the Number function, a crash will occur.

Which offers DeepSeek V3 2 wins out of 4, but DeepSeek R1 only one triumph of 4 so far.

Test 3: Finding a bothersome bug

This is a test developed when I had a very bothersome bug that I had difficulty finding. Once again, I decided to see if ChatGPT could handle it, which it did.

The difficulty is that the response isn’t obvious. Actually, the difficulty is that there is an apparent answer, based upon the mistake message. But the apparent answer is the incorrect answer. This not just captured me, but it routinely captures some of the AIs.

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

Both DeepSeek V3 and R1 passed this one with almost identical answers, bringing us to 3 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 home run for V3? Let’s learn.

Test 4: Writing a script

And another one bites the dust. This is a challenging test since it needs the AI to understand the interplay in between 3 environments: AppleScript, the Chrome item 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 precisely what part of the issue is dealt with 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 knew that it required to split the task between directions to Keyboard Maestro and Chrome. It also had relatively weak knowledge of AppleScript, composing custom regimens for AppleScript that are belonging to the language.

Weirdly, the R1 model failed also because it made a bunch of inaccurate presumptions. It presumed that a front window always exists, which is definitely not the case. It likewise made the presumption that the presently front running program would constantly be Chrome, instead of clearly inspecting to see if Chrome was running.

This leaves DeepSeek V3 with three proper tests and one stop working and DeepSeek R1 with 2 proper tests and two fails.

Final ideas

I found that DeepSeek’s persistence on using a public cloud e-mail address like gmail.com (rather than my regular email address with my corporate domain) was annoying. It likewise had a variety of responsiveness stops working that made doing these tests take longer than I would have liked.

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

I wasn’t sure I ‘d be able to compose this post due to the fact that, 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 make sure continued service, registration is momentarily limited to +86 telephone number. Existing users can log in as usual. Thanks for your understanding and support.

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

DeepSeek seems to be extremely chatty in regards to the code it creates. The AppleScript code in Test 4 was both incorrect and exceedingly long. The regular expression code in Test 2 was appropriate in V3, but it could have been composed in a manner in which made it much more maintainable. It stopped working in R1.

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

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

That said, for a new tool working 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 using any AIs for programs assistance? Let us know in the remarks below.

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