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

DeepSeek exploded into the world’s awareness this previous weekend. It stands 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 greatly less facilities than the huge AI tools we have actually been looking at.

Also: Apple researchers expose the secret sauce behind DeepSeek AI

Given the US 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 problems in her post Why China’s DeepSeek could break our AI bubble.

In this 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 itself:

Choose V3 for jobs requiring depth and precision (e.g., resolving sophisticated mathematics issues, generating intricate code).

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

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

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

Test 1: Writing a WordPress plugin

This test was really my first test of ChatGPT’s programming expertise, way back in the day. My other half needed a plugin for WordPress that would assist her run a participation device for her online group.

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

Her requirements were relatively simple. It needed to take in a list of names, one name per line. It then needed to sort the names, and if there were replicate names, separate them so they weren’t listed side-by-side.

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

Since then, it’s been my first test for AIs when assessing their programs abilities. It needs the AI to understand how to set up code for the WordPress structure and follow prompts clearly sufficient to produce both the user interface and program logic.

Only about half of the AIs I have actually checked can completely pass this test. Now, nevertheless, we can add another to the winner’s circle.

DeepSeek V3 developed both the user interface and program reasoning precisely as specified. When It Comes To DeepSeek R1, well that’s an intriguing case. The “thinking” element of R1 caused the AI to spit out 4502 words of analysis before sharing the code.

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

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

Test 2: Rewriting a string function

A user grumbled that he was unable to enter dollars and cents into a donation entry field. As composed, my code only permitted dollars. So, the test includes giving 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 leads to the AI creating some regular expression recognition code. DeepSeek did produce code that works, although there is space for enhancement. The code that DeepSeek V2 composed was unnecessarily long and repetitious while the thinking before creating the code in R1 was also long.

My most significant concern is that both designs of the DeepSeek validation guarantees recognition up to 2 decimal locations, but if a very large number is entered (like 0.30000000000000004), using parseFloat does not have explicit rounding knowledge. The R1 model also used JavaScript’s Number conversion without inspecting for edge case inputs. If bad information comes back 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 provide a really good list of tests to validate versus:

So here, we have a split decision. I’m providing the point to DeepSeek V3 since neither of these problems 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 provide a stop working to R1 since if something that’s not a string somehow enters the Number function, a crash will ensue.

Which gives DeepSeek V3 2 wins out of 4, however DeepSeek R1 just one win out of four up until now.

Test 3: Finding a bothersome bug

This is a test produced when I had a really bothersome bug that I had difficulty finding. Once again, I decided to see if ChatGPT might manage it, which it did.

The obstacle is that the answer isn’t obvious. Actually, the obstacle is that there is an obvious response, based on the mistake message. But the apparent response is the wrong response. This not just captured me, but it regularly captures a few 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 three 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 find out.

Test 4: Writing a script

And another one bites the dust. This is a difficult test because it requires the AI to comprehend the interaction 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 due to the fact that Keyboard Maestro is not a traditional programs tool. But ChatGPT managed the test easily, comprehending precisely what part of the issue 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 understanding. Neither design understood that it required to divide the job in between guidelines to Keyboard Maestro and Chrome. It also had fairly weak understanding of AppleScript, composing custom routines for AppleScript that are belonging to the language.

Weirdly, the R1 design stopped working as well because it made a lot of incorrect presumptions. It presumed that a front window always exists, which is definitely not the case. It also made the assumption that the currently front running program would constantly be Chrome, instead of clearly checking to see if Chrome was running.

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

Final thoughts

I discovered that DeepSeek’s persistence on using a public cloud email address like gmail.com (rather than my normal email address with my business domain) was frustrating. It also had a variety of responsiveness fails that made doing these tests take longer than I would have liked.

Also: How to utilize ChatGPT to write code: What it does well and what it does not

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

DeepSeek’s online services have actually just recently dealt with massive harmful attacks. To make sure ongoing service, registration is momentarily restricted to +86 phone numbers. Existing users can visit as usual. Thanks for your understanding and support.

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

DeepSeek seems to be excessively chatty in regards to the code it produces. The AppleScript code in Test 4 was both incorrect and excessively long. The regular expression code in Test 2 was appropriate in V3, however it could have been written in a way that made it much more maintainable. It failed in R1.

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

I’m absolutely satisfied 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 space for enhancement. I was disappointed with the outcomes for the R1 model. Given the choice, I ‘d still choose ChatGPT as my programming code helper.

That said, for a new tool operating on much lower infrastructure than the other tools, this could be an AI to view.

What do you believe? Have you tried DeepSeek? Are you utilizing any AIs for programming assistance? Let us understand in the remarks listed below.

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