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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 apart for three effective factors:
1. It’s an AI chatbot from China, instead of the US
2. It’s open source.
3. It utilizes significantly less infrastructure than the big AI tools we’ve been taking a look at.
Also: Apple scientists expose the secret sauce behind DeepSeek AI
Given the US federal government’s issues over TikTok and possible Chinese federal government participation 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 post Why China’s DeepSeek might rupture our AI bubble.
In this post, 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 big language designs. According to DeepSeek itself:
Choose V3 for tasks needing depth and precision (e.g., resolving sophisticated mathematics issues, generating complex code).
Choose R1 for latency-sensitive, high-volume applications (e.g., consumer support automation, standard text processing).
You can select between R1 and V3 by clicking the little button in the chat user interface. If the button is blue, you’re utilizing R1.
The short answer is this: outstanding, but plainly not best. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was actually my very first test of ChatGPT’s programs expertise, method back in the day. My partner required a plugin for WordPress that would help her run an involvement device for her online group.
Also: The best AI for coding in 2025 (and what not to use)
Her requirements were fairly simple. It needed to take in a list of names, one name per line. It then had to arrange the names, and if there were duplicate names, separate them so they weren’t noted .
I didn’t actually have time to code it for her, so I decided to provide the AI the obstacle on a whim. To my huge surprise, it worked.
Ever since, it’s been my very first test for AIs when evaluating their shows skills. It needs the AI to know how to establish code for the WordPress framework and follow triggers clearly enough to produce both the interface and program reasoning.
Only about half of the AIs I’ve evaluated can fully pass this test. Now, however, we can add another to the winner’s circle.
DeepSeek V3 created 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 triggered the AI to spit out 4502 words of analysis before sharing the code.
The UI looked various, with much broader input areas. However, both the UI and reasoning worked, so R1 likewise 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 not able to get in dollars and cents into a contribution entry field. As written, my code only permitted dollars. So, the test includes offering the AI the routine that I wrote and asking it to rewrite it to enable both dollars and cents
Also: My preferred ChatGPT feature simply got method more effective
Usually, this results in the AI producing some routine expression recognition code. DeepSeek did generate code that works, although there is space for enhancement. The code that DeepSeek V2 composed was needlessly long and repetitive while the reasoning before generating the code in R1 was likewise long.
My most significant issue is that both models of the DeepSeek validation ensures validation up to 2 decimal places, however if a huge number is entered (like 0.30000000000000004), using parseFloat doesn’t have explicit rounding understanding. The R1 design likewise utilized JavaScript’s Number conversion without looking for edge case inputs. If bad information returns 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 present a very good list of tests to verify versus:
So here, we have a split decision. I’m giving the point to DeepSeek V3 due to the fact that neither of these issues its code produced would trigger the program to break when run by a user and would produce the expected results. On the other hand, I need to give a stop working to R1 because if something that’s not a string somehow gets into the Number function, a crash will occur.
Which offers DeepSeek V3 2 wins out of 4, however DeepSeek R1 just one win out of 4 so far.
Test 3: Finding an annoying bug
This is a test developed when I had a really irritating bug that I had difficulty finding. Once again, I chose to see if ChatGPT might manage it, which it did.
The difficulty is that the answer isn’t obvious. Actually, the obstacle is that there is an apparent response, based on the error message. But the obvious response is the wrong answer. This not only captured me, however it routinely captures a few of the AIs.
Also: Are ChatGPT Plus or Pro worth it? Here’s how they compare to the complimentary version
Solving this bug needs comprehending how particular API calls within WordPress work, having the ability to see beyond the mistake message to the code itself, and then understanding 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 4 wins for R1. That already puts DeepSeek ahead of Gemini, Copilot, Claude, and Meta.
Will DeepSeek score a home run for V3? Let’s find out.
Test 4: Writing a script
And another one bites the dust. This is a difficult test because it needs the AI to understand the interaction between three environments: AppleScript, the Chrome item model, and a Mac scripting tool called Keyboard Maestro.
I would have called this an unjust test due to the fact that Keyboard Maestro is not a mainstream shows tool. But ChatGPT handled the test easily, comprehending precisely 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 knowledge. Neither design knew that it needed to divide the job in between guidelines to Keyboard Maestro and Chrome. It also had fairly weak knowledge of AppleScript, writing custom-made routines for AppleScript that are belonging to the language.
Weirdly, the R1 model failed also since it made a lot of inaccurate presumptions. It presumed that a front window always exists, which is definitely not the case. It also made the presumption 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 three right tests and one fail and DeepSeek R1 with two proper tests and two stops working.
Final thoughts
I found that DeepSeek’s insistence on utilizing a public cloud email address like gmail.com (instead of my typical e-mail address with my business domain) was annoying. 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 write this article since, for the majority of the day, I got this mistake when attempting to register:
DeepSeek’s online services have actually recently faced massive destructive attacks. To ensure continued service, registration is momentarily limited to +86 telephone 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 appears to be overly chatty in terms of the code it produces. The AppleScript code in Test 4 was both wrong and exceedingly long. The regular expression code in Test 2 was right in V3, but it might have been composed in a manner in which made it a lot more maintainable. It stopped working in R1.
Also: If ChatGPT produces AI-generated code for your app, who does it truly belong to?
I’m absolutely pleased that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which implies there’s absolutely room for enhancement. I was dissatisfied with the results for the R1 design. Given the option, I ‘d still choose ChatGPT as my programming code assistant.
That stated, for a brand-new tool operating on much lower facilities than the other tools, this might be an AI to watch.
What do you believe? Have you tried DeepSeek? Are you using any AIs for programming assistance? Let us know in the comments listed below.
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