I Requested ChatGPT, Claude and DeepSeek to Construct Tetris

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# Introduction

 
It looks like nearly each week, a brand new mannequin claims to be state-of-the-art, beating present AI fashions on all benchmarks.

I get free entry to the most recent AI fashions at my full-time job inside weeks of launch. I sometimes don’t pay a lot consideration to the hype and simply use whichever mannequin is auto-selected by the system.

Nonetheless, I do know builders and mates who wish to construct software program with AI that may be shipped to manufacturing. Since these initiatives are self-funded, their problem lies find one of the best mannequin to do the job. They wish to stability price with reliability.

Because of this, after the discharge of GPT-5.2, I made a decision to run a sensible take a look at to grasp whether or not this mannequin was definitely worth the hype, and if it actually was higher than the competitors.

Particularly, I selected to check flagship fashions from every supplier: Claude Opus 4.5 (Anthropic’s most succesful mannequin), GPT-5.2 Professional (OpenAI’s newest prolonged reasoning mannequin), and DeepSeek V3.2 (one of many newest open-source options).

To place these fashions to the take a look at, I selected to get them to construct a playable Tetris sport with a single immediate.

These had been the metrics I used to judge the success of every mannequin:

# The Immediate

 
Right here is the immediate I entered into every AI mannequin:

Construct a completely practical Tetris sport as a single HTML file that I can open instantly in my browser.

Necessities:

GAME MECHANICS:
– All 7 Tetris piece varieties
– Easy piece rotation with wall kick collision detection
– Items ought to fall routinely, improve the pace progressively because the person’s rating will increase
– Line clearing with visible animation
– “Subsequent piece” preview field
– Recreation over detection when items attain the highest

CONTROLS:
– Arrow keys: Left/Proper to maneuver, Right down to drop quicker, As much as rotate
– Contact controls for cellular: Swipe left/proper to maneuver, swipe right down to drop, faucet to rotate
– Spacebar to pause/unpause
– Enter key to restart after sport over

VISUAL DESIGN:
– Gradient colours for every bit sort
– Easy animations when items transfer and contours clear
– Clear UI with rounded corners
– Replace scores in actual time
– Degree indicator
– Recreation over display screen with remaining rating and restart button

GAMEPLAY EXPERIENCE AND POLISH:
– Easy 60fps gameplay
– Particle results when traces are cleared (optionally available however spectacular)
– Enhance the rating primarily based on variety of traces cleared concurrently
– Grid background
– Responsive design

Make it visually polished and really feel satisfying to play. The code needs to be clear and well-organized.

# The Outcomes

// 1. Claude Opus 4.5

The Opus 4.5 mannequin constructed precisely what I requested for.

The UI was clear and directions had been displayed clearly on the display screen. All of the controls had been responsive and the sport was enjoyable to play.

The gameplay was so easy that I really ended up enjoying for fairly a while and acquired sidetracked from testing the opposite fashions.

Additionally, Opus 4.5 took lower than 2 minutes to supply me with this working sport, leaving me impressed on the primary strive.

Tetris sport constructed by Opus 4.5

// 2. GPT-5.2 Professional

GPT-5.2 Professional is OpenAI’s newest mannequin with prolonged reasoning. For context, GPT-5.2 has three tiers: Prompt, Pondering, and Professional. On the level of writing this text, GPT-5.2 Professional is their most clever mannequin, offering prolonged pondering and reasoning capabilities.

It’s also 4x costlier than Opus 4.5.

There was a whole lot of hype round this mannequin, main me to go in with excessive expectations.

Sadly, I used to be underwhelmed by the sport this mannequin produced.

On the first strive, GPT-5.2 Professional produced a Tetris sport with a structure bug. The underside rows of the sport had been outdoors of the viewport, and I couldn’t see the place the items had been touchdown.

This made the sport unplayable, as proven within the screenshot under:

Tetris sport constructed by GPT-5.2

I used to be particularly shocked by this bug because it took round 6 minutes for the mannequin to supply this code.

I made a decision to strive once more with this follow-up immediate to repair the viewport drawback:

The sport works, however there is a bug. The underside rows of the Tetris board are minimize off on the backside of the display screen. I can not see the items once they land and the canvas extends past the seen viewport.

Please repair this by:
1. Ensuring all the sport board matches within the viewport
2. Including correct centering so the complete board is seen

The sport ought to match on the display screen with all rows seen.

After the follow-up immediate, the GPT-5.2 Professional mannequin produced a practical sport, as seen within the under screenshot:

Tetris second strive by GPT-5.2

Nonetheless, the sport play wasn’t as easy because the one produced by the Opus 4.5 mannequin.

Once I pressed the “down” arrow for the piece to drop, the subsequent piece would typically plummet immediately at a excessive pace, not giving me sufficient time to consider find out how to place it.

The sport ended up being playable provided that I let every bit fall by itself, which wasn’t one of the best expertise.

(Observe: I attempted the GPT-5.2 Customary mannequin too, which produced related buggy code on the primary strive.)

// 3. DeepSeek V3.2

DeepSeek’s first try at constructing this sport had two points:

• Items began disappearing once they hit the underside of the display screen.
• The “down” arrow that’s used to drop the items quicker ended up scrolling all the webpage moderately than simply transferring the sport items.

Tetris sport constructed by DeepSeek V3.2

I re-prompted the mannequin to repair this subject, and the gameplay controls ended up working accurately.

Nonetheless, some items nonetheless disappeared earlier than they landed. This made the sport fully unplayable even after the second iteration.

I’m certain that this subject could be fastened with 2–3 extra prompts, and given DeepSeek’s low pricing, you can afford 10+ debugging rounds and nonetheless spend lower than one profitable Opus 4.5 try.

# Abstract: GPT-5.2 vs Opus 4.5 vs DeepSeek 3.2

// Value Breakdown

Here’s a price comparability between the three fashions:
 

DeepSeek V3.2 is the most affordable various, and it’s also possible to obtain the mannequin’s weights free of charge and run it by yourself infrastructure.

GPT-5.2 is nearly 7x costlier than DeepSeek V3.2, adopted by Opus 4.5 and GPT-5.2 Professional.

For this particular process (constructing a Tetris sport), we consumed roughly 1,000 enter tokens and three,500 output tokens.

For every extra iteration, we are going to estimate an additional 1,500 tokens per extra spherical. Right here is the entire price incurred per mannequin:

# Takeaways

 
Based mostly on my expertise constructing this sport, I might stick with the Opus 4.5 mannequin for day after day coding duties.

Though GPT-5.2 is cheaper than Opus 4.5, I personally wouldn’t use it to code, for the reason that iterations required to yield the identical consequence would probably result in the identical amount of cash spent.

DeepSeek V3.2, nonetheless, is much extra reasonably priced than the opposite fashions on this checklist.

In case you’re a developer on a finances and have time to spare on debugging, you’ll nonetheless find yourself saving cash even when it takes you over 10 tries to get working code.

I used to be shocked at GPT 5.2 Professional’s lack of ability to supply a working sport on the primary strive, because it took round 6 minutes to assume earlier than arising with flawed code. In any case, that is OpenAI’s flagship mannequin, and Tetris needs to be a comparatively easy process.

Nonetheless, GPT-5.2 Professional’s strengths lie in math and scientific analysis, and it’s particularly designed for issues that don’t depend on sample recognition from coaching knowledge. Maybe this mannequin is over-engineered for easy day-to-day coding duties, and may as a substitute be used when constructing one thing that’s complicated and requires novel structure.

The sensible takeaway from this experiment:

• Opus 4.5 performs greatest at day-to-day coding duties.
• DeepSeek V3.2 is a finances various that delivers affordable output, though it requires some debugging effort to succeed in your required final result.
• GPT-5.2 (Customary) didn’t carry out in addition to Opus 4.5, whereas GPT-5.2 (Professional) might be higher fitted to complicated reasoning than fast coding duties like this one.

Be happy to duplicate this take a look at with the immediate I’ve shared above, and completely happy coding!
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Natassha Selvaraj is a self-taught knowledge scientist with a ardour for writing. Natassha writes on all the things knowledge science-related, a real grasp of all knowledge matters. You may join along with her on LinkedIn or try her YouTube channel.