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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 consciousness this previous weekend. It stands out for 3 effective reasons:
1. It’s an AI chatbot from China, rather than the US
2. It’s open source.
3. It uses greatly less facilities than the huge AI tools we have actually been looking at.
Also: Apple researchers reveal the secret sauce behind DeepSeek AI
Given the US federal government’s concerns 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 problems in her post 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 have actually thrown at 10 other large language designs. According to DeepSeek itself:
Choose V3 for tasks needing depth and accuracy (e.g., resolving innovative mathematics issues, generating intricate code).
Choose R1 for latency-sensitive, high-volume applications (e.g., customer assistance 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: excellent, however plainly not best. Let’s dig in.
Test 1: Writing a WordPress plugin
This test was really my first test of ChatGPT’s shows expertise, method back in the day. My better half required a plugin for WordPress that would help her run a participation device for her online group.
Also: The finest AI for coding in 2025 (and what not to use)
Her requirements were relatively simple. 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 really have time to code it for her, so I chose to offer the AI the obstacle on a whim. To my big surprise, it worked.
Since then, it’s been my very first test for AIs when examining their programs abilities. It requires the AI to understand how to establish code for the WordPress structure and follow triggers clearly sufficient to produce both the user interface and program reasoning.
Only about half of the AIs I’ve checked can completely pass this test. Now, however, we can add one more to the winner’s circle.
DeepSeek V3 created both the user interface and program reasoning precisely as defined. When It Comes To 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 wider input locations. However, both the UI and logic worked, so R1 also 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 go into dollars and cents into a donation entry field. As written, my code just enabled dollars. So, the test involves providing the AI the routine that I wrote and asking it to reword it to permit both dollars and cents
Also: My favorite ChatGPT feature just got way more effective
Usually, this leads to the AI generating some routine expression recognition code. DeepSeek did generate code that works, although there is space for improvement. The code that DeepSeek V2 wrote was unnecessarily long and repetitive while the thinking before creating the code in R1 was likewise long.
My biggest issue is that both models of the DeepSeek recognition guarantees recognition up to 2 decimal locations, however if a huge number is entered (like 0.30000000000000004), making use of parseFloat doesn’t have specific rounding knowledge. The R1 design also used JavaScript’s Number conversion without looking 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, due to the fact that R1 did provide a really nice list of tests to validate against:
So here, we have a split choice. I’m providing the indicate DeepSeek V3 due to the fact that neither of these issues its code produced would cause the program to break when run by a user and would generate the anticipated outcomes. On the other hand, I need to provide a stop working to R1 due to the fact that if something that’s not a string somehow gets into the Number function, a crash will occur.
Which offers DeepSeek V3 two wins out of 4, however DeepSeek R1 only one triumph of 4 so far.
Test 3: Finding a bothersome bug
This is a test developed when I had a very bothersome bug that I had problem locating. Once again, I decided 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 obvious response, based on the error message. But the apparent response is the incorrect answer. This not just caught me, however it frequently captures some of the AIs.
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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 after that knowing where to discover 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 2 out of 4 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 out.
Test 4: Writing a script
And another one bites the dust. This is a challenging test since it requires the AI to understand the interplay between 3 environments: AppleScript, the Chrome things design, and a Mac scripting tool called Keyboard Maestro.
I would have called this an unreasonable test because Keyboard Maestro is not a mainstream programs tool. But ChatGPT handled the test quickly, 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 understanding. Neither design understood that it required to divide the job between instructions to Keyboard Maestro and Chrome. It also had fairly weak understanding of AppleScript, composing customized regimens for AppleScript that are native to the language.
Weirdly, the R1 model stopped working also because it made a lot of inaccurate presumptions. It assumed that a front window constantly exists, which is absolutely not the case. It also made the presumption that the currently front running program would constantly be Chrome, instead of explicitly checking to see if Chrome was running.
This leaves DeepSeek V3 with 3 proper tests and one fail and DeepSeek R1 with two correct tests and 2 stops working.
Final thoughts
I discovered that DeepSeek’s persistence on utilizing a public cloud e-mail address like gmail.com (instead of my normal email address with my corporate domain) was annoying. It likewise had a variety of responsiveness stops working that made doing these tests take longer than I would have liked.
Also: How to use ChatGPT to write code: What it does well and what it does not
I wasn’t sure I ’d be able to compose this post because, for most of the day, I got this error when trying to sign up:
DeepSeek’s online services have actually recently faced massive malicious attacks. To make sure ongoing service, registration is momentarily restricted to +86 telephone number. users can visit as usual. Thanks for your understanding and assistance.
Then, I got in and was able to run the tests.
DeepSeek seems to be extremely loquacious in terms of the code it produces. 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 could have been written in a method that made it much 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 absolutely pleased that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which means there’s absolutely space for enhancement. I was dissatisfied with the results for the R1 model. Given the choice, I ’d still choose ChatGPT as my shows code assistant.
That said, for a new tool operating on much lower infrastructure than the other tools, this could be an AI to see.
What do you think? Have you tried DeepSeek? Are you utilizing any AIs for programming assistance? Let us understand in the remarks below.
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