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I Tested DeepSeek’s R1 and V3 Coding Skills – and we’re not All Doomed (Yet).
DeepSeek took off into the world’s awareness this past 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 facilities than the big AI tools we’ve been taking a look at.
Also: Apple researchers reveal the secret sauce behind DeepSeek AI
Given the US government’s concerns over TikTok and possible Chinese government involvement in that code, a new AI emerging from China is bound to generate attention. ZDNET’s Radhika Rajkumar did a deep dive into those concerns in her post Why China’s DeepSeek might break our AI bubble.
In this article, 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 requiring depth and accuracy (e.g., resolving innovative math problems, creating complicated code).
Choose R1 for latency-sensitive, high-volume applications (e.g., customer assistance automation, fundamental text processing).
You can choose between R1 and V3 by clicking the little button in the chat interface. If the button is blue, you’re using R1.
The short response is this: outstanding, but 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, way back in the day. My other half needed a plugin for WordPress that would assist 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 relatively easy. It needed to take in a list of names, one name per line. It then had to sort the names, and if there were duplicate names, different them so they weren’t noted side-by-side.
I didn’t truly have time to code it for her, so I decided to offer the AI the challenge on a whim. To my huge surprise, it worked.
Ever since, it’s been my first test for AIs when evaluating their programming abilities. It requires the AI to understand how to establish code for the WordPress framework and follow triggers plainly enough to produce both the interface and program logic.
Only about half of the AIs I’ve tested can completely pass this test. Now, nevertheless, we can include one more to the winner’s circle.
DeepSeek V3 produced both the user interface and program reasoning exactly as defined. As for DeepSeek R1, well that’s a fascinating case. The “reasoning” element of R1 caused the AI to spit out 4502 words of analysis before sharing the code.
The UI looked various, with much larger input areas. However, both the UI and reasoning worked, so R1 likewise passes this test.
So far, DeepSeek V3 and R1 both passed one of four 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 composed, my code only permitted dollars. So, the test includes providing the AI the routine that I composed and asking it to reword it to permit for both dollars and cents
Also: My favorite ChatGPT feature simply got method more effective
Usually, this results in the AI creating some routine expression validation code. DeepSeek did create code that works, although there is room for enhancement. The code that DeepSeek V2 composed was needlessly long and repetitious while the thinking before generating the code in R1 was likewise long.
My greatest issue is that both designs of the DeepSeek validation ensures recognition as much as 2 decimal places, but if a huge number is gone into (like 0.30000000000000004), making use of parseFloat doesn’t have explicit rounding understanding. The R1 model likewise used JavaScript’s Number conversion without examining for edge case inputs. If bad data 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, due to the fact that R1 did provide a really great list of tests to validate versus:
So here, we have a split decision. I’m giving the point to DeepSeek V3 because neither of these issues its code produced would cause 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 gets into the Number function, a crash will occur.
Which gives DeepSeek V3 2 triumphes of 4, but DeepSeek R1 only one win out of 4 so far.
Test 3: Finding a frustrating bug
This is a test created when I had an extremely frustrating bug that I had difficulty finding. Once again, I decided to see if ChatGPT might handle it, which it did.
The challenge is that the response isn’t obvious. Actually, the challenge is that there is an obvious response, based on the error message. But the apparent response is the wrong answer. This not just captured me, but it frequently catches some of the AIs.
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Solving this bug requires comprehending how particular API calls within WordPress work, having the ability to see beyond the error message to the code itself, and after that understanding where to find the bug.
Both DeepSeek V3 and R1 passed this one with almost similar responses, bringing us to three out of 4 wins for V3 and two 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.
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 in between 3 environments: AppleScript, the Chrome item model, 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 traditional shows tool. But ChatGPT managed the test easily, understanding precisely what part of the problem is dealt with 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 understood that it needed to divide the job between guidelines to Keyboard Maestro and Chrome. It also had fairly weak understanding of AppleScript, composing custom regimens for AppleScript that are belonging to the language.
Weirdly, the R1 model stopped working as well because it made a lot of inaccurate presumptions. It assumed that a front window always exists, which is definitely not the case. It also made the assumption that the presently front running program would always be Chrome, instead of explicitly checking to see if Chrome was running.
This leaves DeepSeek V3 with 3 right tests and one stop working and DeepSeek R1 with 2 right tests and two fails.
Final thoughts
I discovered that DeepSeek’s insistence on using a public cloud email address like gmail.com (rather than my normal email address with my corporate domain) was bothersome. It also had a number of responsiveness fails that made doing these tests take longer than I would have liked.
Also: How to use ChatGPT to write code: What it and what it doesn’t
I wasn’t sure I ‘d be able to compose this article due to the fact that, for most of the day, I got this mistake when attempting to register:
DeepSeek’s online services have actually recently dealt with massive malicious attacks. To ensure continued service, registration is temporarily limited to +86 contact number. Existing users can visit as usual. Thanks for your understanding and assistance.
Then, I got in and was able to run the tests.
DeepSeek appears to be excessively chatty in terms of the code it creates. The AppleScript code in Test 4 was both incorrect and exceedingly long. The routine expression code in Test 2 was right in V3, but it might have been written in a method that made it much more maintainable. It failed in R1.
Also: If ChatGPT produces AI-generated code for your app, who does it actually come from?
I’m certainly pleased 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 definitely room for enhancement. I was disappointed with the outcomes for the R1 design. Given the option, I ‘d still pick ChatGPT as my programs code helper.
That said, for a new tool running on much lower infrastructure than the other tools, this might be an AI to watch.
What do you think? Have you tried DeepSeek? Are you utilizing any AIs for programs support? Let us know in the comments below.
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