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I Tested DeepSeek’s R1 and V3 Coding Skills – and we’re not All Doomed (Yet).
DeepSeek blew up into the world’s consciousness this past weekend. It stands 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 vastly less infrastructure than the huge AI tools we have actually been looking at.
Also: Apple researchers expose the secret sauce behind DeepSeek AI
Given the US federal government’s issues over TikTok and possible Chinese federal government participation because code, a new AI emerging from China is bound to generate attention. ZDNET’s Radhika Rajkumar did a deep dive into those issues in her article Why China’s DeepSeek might rupture 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 designs. According to DeepSeek itself:
Choose V3 for tasks requiring depth and accuracy (e.g., fixing innovative math problems, creating complicated code).
Choose R1 for latency-sensitive, high-volume applications (e.g., client assistance automation, standard text processing).
You can select 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 answer is this: remarkable, however clearly not ideal. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was actually my first test of ChatGPT’s programming prowess, method back in the day. My better half required 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 easy. It required to take in a list of names, one name per line. It then had to arrange the names, and if there were duplicate names, different them so they weren’t listed side-by-side.
I didn’t truly have time to code it for her, so I chose to give the AI the challenge on a whim. To my huge surprise, it worked.
Ever since, it’s been my very first test for AIs when examining their programming abilities. It requires the AI to understand how to for the WordPress structure and follow triggers plainly adequate to develop both the user interface and program reasoning.
Only about half of the AIs I’ve tested can totally pass this test. Now, nevertheless, we can add another to the winner’s circle.
DeepSeek V3 developed both the interface and program logic precisely as defined. When It Comes To DeepSeek R1, well that’s an interesting case. The “thinking” aspect of R1 caused the AI to spit out 4502 words of analysis before sharing the code.
The UI looked various, with much larger input locations. However, both the UI and logic worked, so R1 likewise passes this test.
Up until now, DeepSeek V3 and R1 both passed one of 4 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 only allowed dollars. So, the test involves offering the AI the regular that I composed and asking it to rewrite it to enable both dollars and cents
Also: My favorite ChatGPT feature just got way more powerful
Usually, this leads to the AI creating some routine expression validation 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 reasoning before creating the code in R1 was likewise long.
My greatest concern is that both designs of the DeepSeek recognition guarantees validation up to 2 decimal places, however if a huge number is gone into (like 0.30000000000000004), making use of parseFloat doesn’t have specific rounding knowledge. The R1 model also used JavaScript’s Number conversion without inspecting 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 a really nice list of tests to validate versus:
So here, we have a split choice. I’m providing the point to DeepSeek V3 because neither of these issues its code produced would cause the program to break when run by a user and would produce the expected outcomes. On the other hand, I need to give a stop working to R1 because if something that’s not a string somehow enters the Number function, a crash will take place.
Which offers DeepSeek V3 2 triumphes of 4, but DeepSeek R1 only one triumph of four so far.
Test 3: Finding an annoying bug
This is a test produced when I had a really annoying bug that I had trouble tracking down. Once again, I chose to see if ChatGPT might handle it, which it did.
The difficulty is that the answer isn’t obvious. Actually, the difficulty is that there is an apparent response, based upon the error message. But the apparent response is the incorrect answer. This not just caught me, however it routinely catches some 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 specific API calls within WordPress work, having the ability to see beyond the error message to the code itself, and then understanding where to discover the bug.
Both DeepSeek V3 and R1 passed this one with nearly identical answers, 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 crowning achievement for V3? Let’s learn.
Test 4: Writing a script
And another one bites the dust. This is a tough test since it requires the AI to comprehend the interplay between 3 environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.
I would have called this an unreasonable test since Keyboard Maestro is not a mainstream programming tool. But ChatGPT handled the test quickly, understanding 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 model understood that it required to split the task between instructions to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, writing custom-made regimens for AppleScript that are native to the language.
Weirdly, the R1 design failed too since it made a bunch of incorrect presumptions. It assumed that a front window constantly exists, which is definitely not the case. It also made the presumption that the presently front running program would constantly be Chrome, rather than explicitly inspecting 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 fails.
Final thoughts
I discovered that DeepSeek’s persistence on using a public cloud e-mail address like gmail.com (instead of my regular e-mail address with my business domain) was frustrating. It also 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 write code: What it does well and what it doesn’t
I wasn’t sure I ‘d be able to write this post due to the fact that, for many of the day, I got this mistake when trying to register:
DeepSeek’s online services have just recently faced massive malicious attacks. To make sure continued service, registration is briefly restricted to +86 phone numbers. 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 extremely loquacious in regards to the code it produces. 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 manner in which made it much more maintainable. It failed in R1.
Also: If ChatGPT produces AI-generated code for your app, who does it actually belong to?
I’m certainly pleased that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which indicates there’s certainly room for improvement. I was dissatisfied with the outcomes for the R1 model. Given the choice, I ‘d still select ChatGPT as my programming code assistant.
That said, for a brand-new tool operating on much lower facilities than the other tools, this could be an AI to see.
What do you believe? Have you tried DeepSeek? Are you using any AIs for programs support? Let us understand in the comments listed below.
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