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

DeepSeek blew up into the world’s awareness this previous weekend. It stands apart for three effective reasons:

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

2. It’s open source.

3. It uses greatly less facilities 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 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 article Why China’s DeepSeek could break our AI bubble.

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

Choose V3 for tasks needing depth and accuracy (e.g., fixing innovative mathematics problems, producing complex 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 user interface. If the button is blue, you’re utilizing R1.

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

Test 1: Writing a WordPress plugin

This test was in fact my first test of ChatGPT’s programming prowess, method back in the day. My spouse needed a plugin for WordPress that would help her run an involvement device for her online group.

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

Her needs 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 chose to give the AI the obstacle on an . To my big surprise, it worked.

Since then, it’s been my first test for AIs when evaluating their programming skills. It needs the AI to understand how to set up code for the WordPress framework and follow prompts plainly adequate to develop both the user interface and program logic.

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

DeepSeek V3 produced both the user interface and program reasoning exactly as defined. As for DeepSeek R1, well that’s an interesting case. The “thinking” aspect of R1 triggered 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 reasoning 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 unable to go into dollars and cents into a donation entry field. As composed, my code only allowed dollars. So, the test involves providing the AI the regular that I wrote and asking it to rewrite it to permit both dollars and cents

Also: My preferred ChatGPT function just got way more effective

Usually, this results in the AI producing some routine expression validation code. DeepSeek did create code that works, although there is room for improvement. The code that DeepSeek V2 composed was unnecessarily long and repetitive while the reasoning before producing the code in R1 was also long.

My most significant concern is that both models of the DeepSeek validation guarantees validation as much as 2 decimal locations, however if an extremely large number is gone into (like 0.30000000000000004), the use of parseFloat does not have specific rounding knowledge. The R1 model likewise utilized JavaScript’s Number conversion without looking for edge case inputs. If bad data 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 provide a really good list of tests to confirm versus:

So here, we have a split choice. I’m giving the point to DeepSeek V3 because neither of these concerns its code produced would cause the program to break when run by a user and would generate the anticipated outcomes. On the other hand, I need to give a stop working to R1 because if something that’s not a string somehow gets into the Number function, a crash will ensue.

Which offers DeepSeek V3 2 triumphes of 4, however DeepSeek R1 only one triumph of four up until now.

Test 3: Finding an irritating bug

This is a test developed when I had a really frustrating bug that I had trouble tracking down. Once once again, I decided to see if ChatGPT could manage it, which it did.

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

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

Both DeepSeek V3 and R1 passed this one with nearly identical responses, 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 home run for V3? Let’s learn.

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 three environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unreasonable test due to the fact that Keyboard Maestro is not a traditional programs tool. But ChatGPT managed the test quickly, comprehending exactly 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 knowledge. Neither model understood that it required to split the task in between instructions to Keyboard Maestro and Chrome. It likewise had fairly weak understanding of AppleScript, composing custom regimens for AppleScript that are native to the language.

Weirdly, the R1 model failed also due to the fact that 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 always be Chrome, rather than explicitly inspecting to see if Chrome was running.

This leaves DeepSeek V3 with 3 proper tests and one stop working and DeepSeek R1 with two appropriate tests and 2 fails.

Final thoughts

I found that DeepSeek’s persistence on using a public cloud email address like gmail.com (instead of my normal email address with my business domain) was bothersome. 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 compose code: What it does well and what it doesn’t

I wasn’t sure I ‘d be able to compose this article since, for many of the day, I got this mistake when trying to sign up:

DeepSeek’s online services have just recently faced large-scale destructive attacks. To make sure ongoing service, registration is momentarily restricted to +86 contact number. Existing users can visit as typical. Thanks for your understanding and assistance.

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

DeepSeek seems to be overly chatty in regards to the code it generates. The AppleScript code in Test 4 was both incorrect and excessively long. The routine expression code in Test 2 was proper in V3, but it could have been composed in a method 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 amazed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which means there’s absolutely room for enhancement. I was disappointed with the results for the R1 model. Given the option, I ‘d still pick ChatGPT as my programs code assistant.

That said, for a brand-new tool working on much lower infrastructure 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 understand in the remarks listed below.

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