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Founded Date February 20, 2023
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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 awareness 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 uses vastly less facilities than the big AI tools we have actually been taking a look at.
Also: Apple scientists reveal the secret sauce behind DeepSeek AI
Given the US government’s concerns over TikTok and possible Chinese federal government participation in that code, a new AI emerging from China is bound to create attention. ZDNET’s Radhika Rajkumar did a deep dive into those problems in her post 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’ve thrown at 10 other big language designs. According to DeepSeek itself:
Choose V3 for tasks needing depth and accuracy (e.g., fixing sophisticated math problems, producing complicated code).
Choose R1 for latency-sensitive, high-volume applications (e.g., customer support automation, fundamental text processing).
You can choose 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 response is this: remarkable, but clearly not perfect. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was in fact my first test of ChatGPT’s shows prowess, way back in the day. My wife required a plugin for WordPress that would assist her run an involvement device for her online group.
Also: The best AI for coding in 2025 (and what not to use)
Her needs were relatively simple. It required 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 actually have time to code it for her, so I chose to give the AI the difficulty on an impulse. To my substantial surprise, it worked.
Ever since, it’s been my first test for AIs when evaluating their programs skills. It needs the AI to know how to establish code for the WordPress structure and follow prompts clearly adequate to create both the user interface and program logic.
Only about half of the AIs I have actually tested can fully pass this test. Now, nevertheless, we can add one more to the winner’s circle.
DeepSeek V3 developed both the user interface and program reasoning precisely as specified. As for DeepSeek R1, well that’s an intriguing case. The “thinking” element of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.
The UI looked different, with much larger input areas. However, both the UI and logic worked, so R1 likewise passes this test.
Up until now, DeepSeek V3 and R1 both passed among 4 tests.
Test 2: Rewriting a string function
A user complained that he was not able to get in dollars and cents into a contribution entry field. As written, my code just permitted dollars. So, the test includes giving the AI the routine that I wrote and asking it to rewrite it to enable both dollars and cents
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Usually, this leads to the AI generating some regular expression recognition code. DeepSeek did generate code that works, although there is space for enhancement. The code that DeepSeek V2 composed was unnecessarily long and repetitive while the reasoning before generating the code in R1 was also long.
My greatest issue is that both models of the DeepSeek recognition ensures recognition 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 knowledge. The R1 model likewise utilized JavaScript’s Number conversion without checking for edge case inputs. If bad information 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 great list of tests to confirm against:
So here, we have a split choice. I’m giving the point to DeepSeek V3 because neither of these issues 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 fail to R1 because if something that’s not a string somehow enters into the Number function, a crash will occur.
Which provides DeepSeek V3 two wins out of 4, however DeepSeek R1 just one triumph of four up until now.
Test 3: Finding a frustrating bug
This is a test produced when I had a very frustrating bug that I had difficulty tracking down. Once again, I decided to see if ChatGPT might manage it, which it did.
The obstacle is that the answer isn’t apparent. Actually, the difficulty is that there is an obvious answer, based upon the mistake message. But the obvious answer is the wrong answer. This not just captured me, however it routinely catches some of the AIs.
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Solving this bug needs understanding how particular 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 4 wins for R1. That currently puts DeepSeek ahead of Gemini, Copilot, Claude, and Meta.
Will DeepSeek score a home run for V3? Let’s discover.
Test 4: Writing a script
And another one bites the dust. This is a challenging test since it requires the AI to understand the interaction in between three environments: AppleScript, the Chrome things design, and a tool called Keyboard Maestro.
I would have called this an unjust test because Keyboard Maestro is not a mainstream programming tool. But ChatGPT managed the test quickly, comprehending precisely what part of the problem is handled 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 design understood that it required to split the task between guidelines to Keyboard Maestro and Chrome. It also had fairly weak understanding of AppleScript, writing custom-made regimens for AppleScript that are belonging to the language.
Weirdly, the R1 model failed too because it made a bunch of incorrect assumptions. It assumed that a front window always exists, which is certainly not the case. It likewise made the assumption that the currently front running program would always be Chrome, rather than clearly examining to see if Chrome was running.
This leaves DeepSeek V3 with 3 correct tests and one fail and DeepSeek R1 with 2 correct tests and 2 stops working.
Final thoughts
I found that DeepSeek’s persistence on using a public cloud email address like gmail.com (rather than my normal e-mail address with my business domain) was annoying. 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 write code: What it succeeds and what it doesn’t
I wasn’t sure I ‘d have the ability to write this short article since, for the majority of the day, I got this mistake when trying to sign up:
DeepSeek’s online services have actually just recently faced large-scale harmful attacks. To make sure continued service, registration is briefly restricted to +86 contact number. Existing users can log in as typical. Thanks for your understanding and support.
Then, I got in and was able to run the tests.
DeepSeek seems to be excessively chatty in terms of the code it creates. The AppleScript code in Test 4 was both wrong and exceedingly long. The regular expression code in Test 2 was correct in V3, but it could have been written in a method that made it far more maintainable. It stopped working in R1.
Also: If ChatGPT produces AI-generated code for your app, who does it truly come from?
I’m certainly satisfied that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which suggests there’s certainly room for enhancement. I was dissatisfied with the outcomes for the R1 model. Given the option, I ‘d still choose ChatGPT as my programming code assistant.
That said, for a brand-new tool running on much lower infrastructure than the other tools, this could be an AI to enjoy.
What do you think? Have you tried DeepSeek? Are you utilizing any AIs for programs assistance? Let us understand in the comments listed below.
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