
Mariannalibardoni
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Founded Date August 28, 2023
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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 consciousness this previous weekend. It stands out for 3 powerful reasons:
1. It’s an AI chatbot from China, instead of the US
2. It’s open source.
3. It uses vastly less facilities than the huge AI tools we’ve been looking at.
Also: Apple researchers reveal the secret sauce behind DeepSeek AI
Given the US federal government’s issues over TikTok and possible Chinese federal government involvement 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 short article Why China’s DeepSeek could 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 jobs requiring depth and accuracy (e.g., fixing innovative math problems, creating complex code).
Choose R1 for latency-sensitive, high-volume applications (e.g., consumer assistance automation, basic 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 brief response is this: outstanding, but clearly not best. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was actually my very first test of ChatGPT’s programming prowess, method back in the day. My better half needed a plugin for WordPress that would help her run a participation gadget for her online group.
Also: The very best AI for coding in 2025 (and what not to utilize)
Her requirements were fairly easy. It needed to take in a list of names, one name per line. It then needed to arrange the names, and if there were duplicate names, separate them so they weren’t listed side-by-side.
I didn’t truly have time to code it for her, so I decided to provide the AI the difficulty on a whim. To my huge surprise, it worked.
Ever since, it’s been my very first test for AIs when assessing their programming skills. It needs the AI to understand how to establish code for the WordPress framework and follow triggers clearly adequate to develop both the user interface and program reasoning.
Only about half of the AIs I have actually tested can fully pass this test. Now, however, we can add another to the winner’s circle.
DeepSeek V3 created both the user interface and program reasoning precisely as specified. When It Comes To DeepSeek R1, well that’s an intriguing case. The “reasoning” aspect of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.
The UI looked different, with much wider input locations. However, both the UI and reasoning 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 grumbled that he was unable to enter dollars and cents into a contribution entry field. As written, my code just enabled dollars. So, the test involves giving the AI the regular that I wrote and asking it to rewrite it to permit both dollars and cents
Also: My preferred ChatGPT feature simply got method more powerful
Usually, this leads to the AI creating some regular expression validation code. DeepSeek did generate code that works, although there is room for improvement. The code that DeepSeek V2 wrote was unnecessarily long and repetitive while the thinking before creating the code in R1 was likewise really long.
My biggest concern is that both designs of the DeepSeek validation guarantees recognition approximately 2 decimal locations, but if a really large number is entered (like 0.30000000000000004), the usage of parseFloat doesn’t have explicit rounding understanding. The R1 design likewise used JavaScript’s Number conversion without looking for edge case inputs. If bad data comes back 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 really good list of tests to validate versus:
So here, we have a split choice. I’m giving the point to DeepSeek V3 due to the fact that neither of these concerns its code produced would cause the program to break when run by a user and would create the expected outcomes. On the other hand, I need to provide a stop working to R1 since if something that’s not a string somehow enters into the Number function, a crash will ensue.
And that gives DeepSeek V3 2 wins out of 4, however DeepSeek R1 just one triumph of four so far.
Test 3: Finding a bothersome bug
This is a test created when I had a really annoying bug that I had difficulty finding. Once again, I decided 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 answer, based on the error message. But the obvious response is the wrong answer. This not only caught me, however it regularly catches a few of the AIs.
Also: Are ChatGPT Plus or Pro worth it? Here’s how they compare to the complimentary variation
Solving this bug requires understanding how calls within WordPress work, being able to see beyond the error 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 three out of four wins for V3 and 2 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 learn.
Test 4: Writing a script
And another one bites the dust. This is a difficult test because it needs the AI to understand the interplay between three environments: AppleScript, the Chrome things design, and a Mac scripting tool called Keyboard Maestro.
I would have called this an unfair test because Keyboard Maestro is not a mainstream shows tool. But ChatGPT managed the test easily, understanding precisely 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 knowledge. Neither model understood that it required to divide the job between guidelines to Keyboard Maestro and Chrome. It likewise had fairly weak knowledge of AppleScript, composing custom routines for AppleScript that are native to the language.
Weirdly, the R1 design failed also since it made a lot of incorrect assumptions. It presumed that a front window constantly exists, which is absolutely not the case. It also made the assumption that the presently front running program would constantly be Chrome, rather than explicitly checking to see if Chrome was running.
This leaves DeepSeek V3 with 3 correct tests and one fail and DeepSeek R1 with two correct tests and two fails.
Final thoughts
I discovered that DeepSeek’s insistence on utilizing a public cloud e-mail address like gmail.com (instead of my regular e-mail 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 write code: What it succeeds and what it doesn’t
I wasn’t sure I ‘d have the ability to write this article because, for most of the day, I got this mistake when attempting to register:
DeepSeek’s online services have just recently dealt with massive malicious attacks. To guarantee continued service, registration is momentarily limited to +86 telephone number. Existing users can log in as usual. Thanks for your understanding and support.
Then, I got in and had the ability to run the tests.
DeepSeek appears to be extremely chatty in terms of the code it creates. The AppleScript code in Test 4 was both wrong and excessively long. The routine expression code in Test 2 was appropriate in V3, but it could have been composed in a method that made it a lot more maintainable. It stopped working in R1.
Also: If ChatGPT produces AI-generated code for your app, who does it really come from?
I’m definitely pleased that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which implies there’s certainly space for enhancement. I was disappointed with the results for the R1 model. Given the choice, I ‘d still select ChatGPT as my programs code assistant.
That stated, for a new tool running on much lower infrastructure than the other tools, this might be an AI to view.
What do you think? Have you tried DeepSeek? Are you using any AIs for shows assistance? Let us know in the remarks listed below.
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