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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 factors:
1. It’s an AI chatbot from China, rather than the US
2. It’s open source.
3. It utilizes significantly less infrastructure than the big AI tools we have actually been looking at.
Also: Apple scientists reveal the secret sauce behind DeepSeek AI
Given the US federal government’s concerns over TikTok and possible Chinese federal government involvement in that code, a brand-new AI emerging from China is bound to produce attention. ZDNET’s Radhika Rajkumar did a deep dive into those concerns in her article Why China’s DeepSeek could break our AI bubble.
In this post, we’re preventing 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 jobs needing depth and accuracy (e.g., solving sophisticated math problems, creating intricate code).
Choose R1 for latency-sensitive, high-volume applications (e.g., customer support automation, basic 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 short answer is this: impressive, but clearly not ideal. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was in fact my very first test of ChatGPT’s shows prowess, way 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 finest AI for coding in 2025 (and what not to utilize)
Her requirements were fairly 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 duplicate names, different them so they weren’t listed side-by-side.
I didn’t actually have time to code it for her, so I chose to provide the AI the obstacle on a whim. To my huge 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 establish code for the WordPress structure and follow triggers plainly enough to produce both the interface and program logic.
Only about half of the AIs I have actually tested can completely pass this test. Now, nevertheless, we can add another to the winner’s circle.
DeepSeek V3 developed both the interface and program reasoning precisely as defined. When It Comes To DeepSeek R1, well that’s an intriguing 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 wider input areas. However, both the UI and reasoning worked, so R1 also passes this test.
So far, DeepSeek V3 and R1 both passed one of 4 tests.
Test 2: Rewriting a string function
A user complained that he was unable to enter dollars and cents into a donation entry field. As written, my code just enabled dollars. So, the test involves giving the AI the regular that I composed and asking it to reword it to permit for both dollars and cents
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Usually, this leads to the AI generating some regular expression recognition code. DeepSeek did produce code that works, although there is space for enhancement. The code that DeepSeek V2 composed was needlessly long and repetitious while the thinking before producing the code in R1 was likewise long.
My most significant issue is that both models of the DeepSeek recognition guarantees recognition up to 2 decimal locations, however if a large number is gotten in (like 0.30000000000000004), using parseFloat doesn’t have specific rounding knowledge. The R1 model also utilized 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 very nice list of tests to validate versus:
So here, we have a split choice. I’m offering the point to DeepSeek V3 due to the fact that neither of these concerns its code produced would trigger the program to break when run by a user and would produce the expected outcomes. On the other hand, I have to give a fail to R1 since if something that’s not a string somehow gets into the Number function, a crash will occur.
And that provides DeepSeek V3 2 triumphes of 4, but DeepSeek R1 only one triumph of 4 so far.
Test 3: Finding a bothersome bug
This is a test created when I had a very irritating bug that I had problem locating. Once again, I decided to see if ChatGPT could manage 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 mistake message. But the obvious response is the wrong answer. This not only captured me, but it regularly captures some 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 mistake message to the code itself, and then knowing where to discover the bug.
Both DeepSeek V3 and R1 passed this one with almost similar answers, bringing us to three out of four wins for V3 and two out of four 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 difficult test due to the fact that it requires the AI to understand the interplay between 3 environments: AppleScript, the Chrome item model, and a Mac scripting tool called Keyboard Maestro.
I would have called this an unreasonable test since Keyboard Maestro is not a mainstream shows tool. But ChatGPT dealt with the test quickly, understanding exactly what part of the issue is handled 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 knew that it required to split the job in between directions to Keyboard Maestro and Chrome. It likewise had relatively weak understanding of AppleScript, composing customized regimens for that are native to the language.
Weirdly, the R1 model stopped working too since it made a bunch of inaccurate presumptions. It assumed that a front window constantly exists, which is absolutely not the case. It also made the assumption that the currently front running program would constantly be Chrome, rather than clearly examining to see if Chrome was running.
This leaves DeepSeek V3 with three proper tests and one stop working and DeepSeek R1 with two appropriate tests and two stops working.
Final ideas
I discovered that DeepSeek’s insistence on utilizing a public cloud e-mail address like gmail.com (instead of my normal e-mail address with my business domain) was bothersome. It likewise had a number 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 succeeds and what it doesn’t
I wasn’t sure I ‘d have the ability to compose this post because, for the majority of the day, I got this mistake when attempting to sign up:
DeepSeek’s online services have actually just recently dealt with massive destructive attacks. To guarantee continued service, registration is briefly restricted to +86 telephone number. Existing users can log in as normal. Thanks for your understanding and support.
Then, I got in and was able to run the tests.
DeepSeek seems to be extremely chatty in regards to the code it creates. The AppleScript code in Test 4 was both wrong and excessively long. The routine expression code in Test 2 was right in V3, however it could have been composed in a manner in which made it far more maintainable. It stopped working in R1.
Also: If ChatGPT produces AI-generated code for your app, who does it actually come from?
I’m certainly pleased that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which suggests there’s definitely space for improvement. I was disappointed with the outcomes for the R1 design. Given the choice, I ‘d still choose ChatGPT as my programs code assistant.
That stated, for a new tool working on much lower infrastructure than the other tools, this could be an AI to enjoy.
What do you think? Have you attempted DeepSeek? Are you utilizing any AIs for programs assistance? Let us know in the comments listed below.
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