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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 consciousness this past weekend. It stands out for three effective factors:
1. It’s an AI chatbot from China, rather than the US
2. It’s open source.
3. It uses vastly less infrastructure 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 federal government’s issues over TikTok and possible Chinese 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 article Why China’s DeepSeek could rupture our AI bubble.
In this article, we’re avoiding politics. Instead, I’m putting both DeepSeek V3 and DeekSeek R1 through the very same set of AI coding tests I’ve thrown at 10 other big language designs. According to DeepSeek itself:
Choose V3 for tasks requiring depth and precision (e.g., fixing advanced mathematics problems, creating complex code).
Choose R1 for latency-sensitive, high-volume applications (e.g., client assistance automation, fundamental 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 using R1.
The short response is this: excellent, however clearly not best. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was in fact my very first test of ChatGPT’s programming prowess, method back in the day. My partner required a plugin for WordPress that would assist 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 required to take in a list of names, one name per line. It then had to sort the names, and if there were replicate names, different 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 challenge on a whim. To my big surprise, it worked.
Since then, it’s been my first test for AIs when examining their programming abilities. It needs the AI to understand how to set up code for the WordPress structure and follow triggers clearly enough to create both the user interface and program logic.
Only about half of the AIs I’ve checked can completely pass this test. Now, nevertheless, we can add another to the winner’s circle.
DeepSeek V3 produced both the interface and program logic precisely as specified. As for DeepSeek R1, well that’s an interesting case. The „reasoning“ element 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 among four tests.
Test 2: Rewriting a string function
A user grumbled that he was unable to go into dollars and cents into a contribution entry field. As written, my code just allowed dollars. So, the test includes giving the AI the regular that I composed and asking it to reword it to permit both dollars and cents
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Usually, this results in the AI generating some regular expression recognition code. DeepSeek did create code that works, although there is space for enhancement. The code that DeepSeek V2 composed was unnecessarily long and repetitious while the reasoning before generating the code in R1 was likewise long.
My biggest issue is that both models of the DeepSeek recognition makes sure validation up to 2 decimal places, but if a huge number is gone into (like 0.30000000000000004), making use of parseFloat doesn’t have explicit rounding understanding. The R1 model also used JavaScript’s Number conversion without looking 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, since R1 did provide a really good list of tests to validate versus:
So here, we have a split choice. I’m providing the point to DeepSeek V3 since neither of these concerns its code produced would trigger the program to break when run by a user and would generate the expected outcomes. On the other hand, I need to offer a stop working to R1 due to the fact that if something that’s not a string in some way enters the Number function, a crash will occur.
Which gives DeepSeek V3 2 wins out of 4, however DeepSeek R1 just one triumph of four so far.
Test 3: Finding a frustrating bug
This is a test developed when I had a very irritating bug that I had difficulty finding. Once once again, I chose to see if ChatGPT could handle it, which it did.
The obstacle is that the response isn’t obvious. Actually, the obstacle is that there is an obvious response, based upon the error message. But the apparent answer is the wrong response. This not just captured me, however it routinely captures a few of the AIs.
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Solving this bug needs comprehending how specific API calls within WordPress work, being able to see beyond the error message to the code itself, and after that knowing where to find the bug.
Both DeepSeek V3 and R1 passed this one with almost identical responses, bringing us to 3 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 crowning achievement for V3? Let’s learn.
Test 4: Writing a script
And another one bites the dust. This is a tough test because it requires the AI to comprehend the interaction in between three environments: AppleScript, the Chrome object model, and a Mac scripting tool called Keyboard Maestro.
I would have called this an unjust test because Keyboard Maestro is not a mainstream 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, saving me hours of work
Unfortunately, neither DeepSeek V3 or R1 had this level of knowledge. Neither design understood that it needed to divide the task in between instructions to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, writing customized routines for AppleScript that are native to the language.
Weirdly, the R1 design failed as well because 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 presently front running program would always be Chrome, rather than explicitly examining to see if Chrome was running.
This leaves DeepSeek V3 with three correct tests and one stop working and DeepSeek R1 with 2 right tests and two stops working.
Final thoughts
I found that DeepSeek’s insistence on utilizing a public cloud email address like gmail.com (rather than my normal email address with my corporate domain) was bothersome. It likewise 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 write code: What it does well and what it doesn’t
I wasn’t sure I ‘d have the ability to write this post due to the fact that, for the majority of the day, I got this mistake when trying to register:
DeepSeek’s online services have actually just recently faced large-scale malicious attacks. To make sure ongoing service, registration is temporarily restricted to +86 contact number. Existing users can visit as typical. Thanks for your understanding and support.
Then, I got in and was able to run the tests.
DeepSeek appears to be excessively loquacious in terms of the code it generates. The AppleScript code in Test 4 was both incorrect and exceedingly long. The routine expression code in Test 2 was correct in V3, but it might 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 definitely impressed that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which implies there’s absolutely space for improvement. I was disappointed with the outcomes for the R1 model. Given the choice, I ‘d still select ChatGPT as my programming code helper.
That stated, for a new tool working on much lower infrastructure than the other tools, this might be an AI to see.
What do you think? Have you attempted DeepSeek? Are you utilizing any AIs for programs support? Let us understand in the comments below.
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