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  • Founded Date September 26, 1988
  • Sectors Marketing and Communications
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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 out for 3 effective factors:

1. It’s an AI chatbot from China, instead of the US

2. It’s open source.

3. It uses vastly less infrastructure than the huge AI tools we have actually 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 create attention. ZDNET’s Radhika Rajkumar did a deep dive into those problems in her post Why China’s DeepSeek might burst our AI bubble.

In this article, we’re avoiding 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 big language designs. According to DeepSeek itself:

Choose V3 for tasks requiring depth and accuracy (e.g., resolving sophisticated mathematics issues, producing complicated code).

Choose R1 for latency-sensitive, high-volume applications (e.g., consumer assistance automation, basic text processing).

You can choose in 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: impressive, but plainly not perfect. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was actually my first test of ChatGPT’s programs expertise, method back in the day. My spouse required a plugin for WordPress that would help her run an involvement gadget for her online group.

Also: The best AI for coding in 2025 (and what not to use)

Her needs were fairly simple. It required to take in a list of names, one name per line. It then needed to sort the names, and if there were duplicate names, separate them so they weren’t noted side-by-side.

I didn’t actually have time to code it for her, so I decided to give the AI the obstacle on a whim. To my big surprise, it worked.

Ever since, it’s been my first test for AIs when assessing their programs abilities. It requires the AI to understand how to set up code for the WordPress framework and follow prompts clearly adequate to develop both the user interface and program logic.

Only about half of the AIs I have actually evaluated can totally pass this test. Now, nevertheless, we can include one more to the winner’s circle.

DeepSeek V3 produced both the interface and program logic precisely as defined. As for DeepSeek R1, well that’s an intriguing case. The “reasoning” element of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.

The UI looked various, with much larger input locations. However, both the UI and logic worked, so R1 likewise passes this test.

Up until now, DeepSeek V3 and R1 both passed among four tests.

Test 2: Rewriting a string function

A user grumbled that he was not able to go into dollars and cents into a contribution entry field. As written, my code just enabled dollars. So, the test involves offering the AI the routine that I composed and asking it to reword it to permit both dollars and cents

Also: My favorite ChatGPT feature simply got way more powerful

Usually, this results in the AI producing some regular expression validation code. DeepSeek did create code that works, although there is room for enhancement. The code that DeepSeek V2 composed was unnecessarily long and repetitious while the thinking before generating the code in R1 was also really long.

My greatest issue is that both designs of the DeepSeek recognition guarantees validation up to 2 decimal locations, but if a huge number is gone into (like 0.30000000000000004), the usage of parseFloat does not have specific rounding knowledge. The R1 model also utilized JavaScript’s Number conversion without looking for edge case inputs. If bad information comes back from an earlier part of the routine expression or a non-string makes it into that conversion, the code would crash.

It’s odd, because R1 did present a very good list of tests to verify against:

So here, we have a split decision. I’m giving the point to DeepSeek V3 due to the fact that neither of these concerns its code produced would trigger the program to break when run by a user and would create the anticipated outcomes. On the other hand, I have to provide 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 take place.

And that offers DeepSeek V3 2 wins out of 4, however DeepSeek R1 only one triumph of 4 so far.

Test 3: Finding an irritating bug

This is a test developed when I had an extremely frustrating bug that I had trouble finding. Once again, I decided to see if ChatGPT might manage it, which it did.

The challenge is that the response isn’t obvious. Actually, the challenge is that there is an obvious response, based upon the mistake message. But the apparent response is the wrong answer. This not just caught 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 version

Solving this bug needs comprehending how specific API calls within WordPress work, being able to see beyond the error message to the code itself, and then understanding where to discover the bug.

Both DeepSeek V3 and R1 passed this one with nearly identical responses, bringing us to three out of four wins for V3 and 2 out of four 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 because it needs the AI to comprehend the interplay between three environments: AppleScript, the Chrome item 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 traditional programming tool. But ChatGPT handled 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 job in between instructions to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, composing custom regimens for AppleScript that are belonging to the language.

Weirdly, the R1 model stopped working too because it made a bunch of incorrect assumptions. It assumed that a front window always exists, which is definitely not the case. It also made the assumption that the currently front running program would always be Chrome, instead of explicitly checking to see if Chrome was running.

This leaves DeepSeek V3 with three right tests and one stop working and DeepSeek R1 with 2 correct tests and 2 stops working.

Final ideas

I found that DeepSeek’s persistence on utilizing a public cloud email address like gmail.com (rather than my typical e-mail address with my business domain) was frustrating. It also had a number 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 succeeds and what it does not

I wasn’t sure I ‘d have the ability to write this post due to the fact that, for most of the day, I got this mistake when trying to register:

DeepSeek’s online services have actually just recently dealt with large-scale harmful attacks. To guarantee continued service, registration is momentarily restricted to +86 contact 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 excessively loquacious in regards to 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, but it could have been written in a manner in which made it far more maintainable. It failed in R1.

Also: If ChatGPT produces AI-generated code for your app, who does it truly come from?

I’m certainly impressed that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which means there’s certainly space for enhancement. I was dissatisfied with the outcomes for the R1 model. Given the choice, I ‘d still select ChatGPT as my shows code helper.

That stated, for a new tool operating on much lower infrastructure than the other tools, this could be an AI to see.

What do you believe? Have you tried DeepSeek? Are you utilizing any AIs for programming assistance? Let us know in the comments below.

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