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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 sticks out for three powerful factors:

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

2. It’s open source.

3. It utilizes greatly less facilities than the huge AI tools we have actually been taking a look 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 new AI emerging from China is bound to produce attention. ZDNET’s Radhika Rajkumar did a deep dive into those problems in her short article Why China’s DeepSeek might burst our AI bubble.

In this post, 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 thrown at 10 other big language models. According to DeepSeek itself:

Choose V3 for tasks needing depth and accuracy (e.g., resolving advanced math issues, creating intricate code).

Choose R1 for latency-sensitive, high-volume applications (e.g., customer support automation, standard 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 brief response is this: impressive, however clearly not perfect. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was really my very first test of ChatGPT’s shows expertise, way back in the day. My other 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 utilize)

Her needs were relatively 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 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 provide the AI the difficulty on an impulse. To my substantial surprise, it worked.

Since then, it’s been my first test for AIs when assessing their shows skills. It requires the AI to know how to establish code for the WordPress framework and follow triggers clearly adequate to create both the interface and program logic.

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

DeepSeek V3 developed both the interface and program logic exactly as defined. As for DeepSeek R1, well that’s a fascinating case. The “thinking” aspect of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.

The UI looked various, with much broader input locations. However, both the UI and reasoning worked, so R1 also passes this test.

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

Test 2: Rewriting a string function

A user complained that he was unable to go into dollars and cents into a contribution entry field. As composed, my code only permitted dollars. So, the test includes giving the AI the routine that I composed and asking it to reword it to enable both dollars and cents

Also: My favorite ChatGPT feature just got way more effective

Usually, this results in the AI creating 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 repetitious while the reasoning before generating the code in R1 was likewise long.

My most significant issue is that both designs of the DeepSeek validation ensures validation up to 2 decimal places, but if a really big number is gotten in (like 0.30000000000000004), using parseFloat does not have specific rounding knowledge. The R1 model also utilized JavaScript’s Number conversion without inspecting 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 present an extremely nice list of tests to confirm against:

So here, we have a split decision. I’m giving the indicate DeepSeek V3 because neither of these concerns its code produced would trigger the program to break when run by a user and would create the expected outcomes. On the other hand, I need to give a stop working to R1 since if something that’s not a string in some way enters the Number function, a crash will occur.

Which provides DeepSeek V3 2 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 a really annoying bug that I had problem locating. Once once again, I chose to see if ChatGPT might manage it, which it did.

The obstacle is that the answer isn’t apparent. Actually, the challenge is that there is an apparent response, based upon the mistake message. But the apparent response is the incorrect response. This not only captured me, however it routinely captures a few of the AIs.

Also: Are ChatGPT Plus or Pro worth it? Here’s how they compare to the free variation

Solving this bug requires comprehending how particular API calls within WordPress work, being able to see beyond the mistake message to the code itself, and after that understanding where to discover the bug.

Both DeepSeek V3 and R1 passed this one with nearly similar responses, bringing us to 3 out of 4 wins for V3 and 2 out of 4 wins for R1. That already puts DeepSeek ahead of Gemini, Copilot, Claude, and Meta.

Will DeepSeek score a crowning achievement for V3? Let’s discover.

Test 4: Writing a script

And another one bites the dust. This is a tough test since it needs the AI to understand the interplay between three environments: AppleScript, the Chrome things 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 programming tool. But ChatGPT handled the test easily, comprehending precisely what part of the problem 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 understood that it required to divide the task in between guidelines to Keyboard Maestro and Chrome. It also had fairly weak knowledge of AppleScript, composing custom routines for AppleScript that are native to the language.

Weirdly, the R1 design stopped working too since it made a bunch of inaccurate presumptions. It assumed that a front window constantly exists, which is certainly not the case. It likewise made the presumption that the currently front running program would constantly be Chrome, instead of explicitly examining to see if Chrome was running.

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

Final ideas

I discovered that DeepSeek’s persistence on using a public cloud e-mail address like gmail.com (instead of my normal e-mail address with my business domain) was irritating. It also had a variety 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 succeeds and what it does not

I wasn’t sure I ‘d be able to compose this short article due to the fact that, for the of the day, I got this error when trying to sign up:

DeepSeek’s online services have recently dealt with large-scale malicious attacks. To make sure ongoing service, registration is momentarily limited to +86 contact number. Existing users can log in as normal. Thanks for your understanding and assistance.

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

DeepSeek seems to be overly loquacious in regards to the code it creates. The AppleScript code in Test 4 was both incorrect and excessively long. The routine expression code in Test 2 was correct in V3, but it could have been written 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 truly 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 implies there’s definitely space for improvement. I was disappointed with the results for the R1 design. Given the choice, I ‘d still select ChatGPT as my programs code assistant.

That said, for a brand-new tool running on much lower facilities than the other tools, this could be an AI to watch.

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

You can follow my daily project updates on social media. Be sure to register for my weekly upgrade newsletter, and follow me on Twitter/X at @DavidGewirtz, on Facebook at Facebook.com/ DavidGewirtz, on Instagram at Instagram.com/ DavidGewirtz, on Bluesky at @DavidGewirtz. com, and on YouTube at YouTube.com/ DavidGewirtzTV.

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