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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 previous weekend. It sticks out for 3 effective reasons:
1. It’s an AI chatbot from China, instead of the US
2. It’s open source.
3. It uses significantly less infrastructure than the huge AI tools we have actually been looking at.
Also: Apple scientists expose the secret sauce behind DeepSeek AI
Given the US government’s issues over TikTok and possible Chinese federal government participation in that code, a new AI emerging from China is bound to produce 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 short article, we’re preventing politics. Instead, I’m putting both DeepSeek V3 and DeekSeek R1 through the very same set of AI coding tests I have actually thrown at 10 other big language designs. According to DeepSeek itself:
Choose V3 for tasks needing depth and precision (e.g., resolving sophisticated mathematics issues, creating intricate code).
Choose R1 for latency-sensitive, high-volume applications (e.g., client support automation, standard text processing).
You can choose 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 best. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was in fact my very first test of ChatGPT’s programs 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 relatively easy. It needed to take in a list of names, one name per line. It then needed to sort the names, and if there were replicate 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 an impulse. To my big surprise, it worked.
Since then, it’s been my very first test for AIs when examining their programs skills. It requires the AI to understand how to set up code for the WordPress framework and follow prompts clearly enough to create both the user interface and program logic.
Only about half of the AIs I have actually tested can totally pass this test. Now, nevertheless, we can include another to the winner’s circle.
DeepSeek V3 created both the interface and program logic precisely as defined. 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 various, with much wider . However, both the UI and logic worked, so R1 likewise passes this test.
Up until now, DeepSeek V3 and R1 both passed one of four tests.
Test 2: Rewriting a string function
A user complained that he was unable to get in dollars and cents into a donation entry field. As composed, 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
Also: My favorite ChatGPT function simply got way more effective
Usually, this leads to the AI creating some regular expression recognition code. DeepSeek did produce code that works, although there is space for improvement. The code that DeepSeek V2 composed was needlessly long and repetitive while the reasoning before creating the code in R1 was likewise very long.
My most significant issue is that both models of the DeepSeek validation ensures recognition approximately 2 decimal locations, however if a really large number is gone into (like 0.30000000000000004), the usage of parseFloat does not have specific rounding knowledge. The R1 model likewise used JavaScript’s Number conversion without checking 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, because R1 did present a very great list of tests to confirm versus:
So here, we have a split decision. I’m providing the point to DeepSeek V3 since neither of these problems 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 have to offer a fail to R1 due to the fact that if something that’s not a string in some way enters into the Number function, a crash will occur.
And that provides DeepSeek V3 2 wins out of 4, however DeepSeek R1 just one win out of 4 up until now.
Test 3: Finding a bothersome bug
This is a test developed when I had a very frustrating bug that I had trouble locating. Once again, I chose to see if ChatGPT might handle it, which it did.
The challenge is that the response isn’t apparent. Actually, the difficulty is that there is an apparent answer, based upon the mistake message. But the apparent answer is the wrong answer. This not just captured me, however it routinely captures 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 understanding how specific 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 almost similar answers, bringing us to 3 out of 4 wins for V3 and 2 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 discover out.
Test 4: Writing a script
And another one bites the dust. This is a tough test because it requires the AI to comprehend the interplay between three environments: AppleScript, the Chrome object design, and a Mac scripting tool called Keyboard Maestro.
I would have called this an unreasonable test due to the fact that Keyboard Maestro is not a mainstream programs tool. But ChatGPT managed the test easily, comprehending exactly what part of the problem 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 design knew that it required to split the task in between instructions to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, writing custom regimens for AppleScript that are belonging to the language.
Weirdly, the R1 design failed as well because it made a bunch of incorrect presumptions. It presumed that a front window always exists, which is absolutely not the case. It likewise made the assumption 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 proper tests and one fail and DeepSeek R1 with 2 right tests and two fails.
Final thoughts
I discovered that DeepSeek’s persistence on using a public cloud email address like gmail.com (instead of my normal email address with my business domain) was frustrating. It likewise had a variety of responsiveness fails that made doing these tests take longer than I would have liked.
Also: How to utilize ChatGPT to compose code: What it does well and what it doesn’t
I wasn’t sure I ‘d have the ability to compose this short article because, for the majority of the day, I got this error when trying to register:
DeepSeek’s online services have actually recently dealt with large-scale harmful attacks. To guarantee ongoing service, registration is momentarily limited to +86 contact number. 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 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 proper in V3, however it might have been written in a way that made it much more maintainable. It failed in R1.
Also: If ChatGPT produces AI-generated code for your app, who does it truly belong to?
I’m definitely amazed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which means there’s definitely room for enhancement. I was dissatisfied with the outcomes for the R1 design. Given the choice, I ‘d still pick ChatGPT as my programs code helper.
That stated, for a new tool operating on much lower infrastructure than the other tools, this could be an AI to enjoy.
What do you believe? Have you attempted DeepSeek? Are you using any AIs for programming assistance? Let us understand in the comments below.
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