I Requested ChatGPT, Claude and DeepSeek to Construct Tetris

Picture by Creator

# Introduction

 
It looks as if virtually each week, a brand new mannequin claims to be state-of-the-art, beating current 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 pals who need to construct software program with AI that may be shipped to manufacturing. Since these initiatives are self-funded, their problem lies find the most effective mannequin to do the job. They need to stability price with reliability.

Attributable to 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 recreation with a single immediate.

These have 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 totally practical Tetris recreation as a single HTML file that I can open immediately in my browser.

Necessities:

GAME MECHANICS:
– All 7 Tetris piece varieties
– Easy piece rotation with wall kick collision detection
– Items ought to fall mechanically, improve the pace regularly 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, All the way down to drop sooner, As much as rotate
– Contact controls for cell: Swipe left/proper to maneuver, swipe all the way down to drop, faucet to rotate
– Spacebar to pause/unpause
– Enter key to restart after recreation over

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

GAMEPLAY EXPERIENCE AND POLISH:
– Easy 60fps gameplay
– Particle results when traces are cleared (optionally available however spectacular)
– Improve 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 must 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 have been displayed clearly on the display. All of the controls have been responsive and the sport was enjoyable to play.

The gameplay was so clean that I truly ended up taking part in for fairly a while and received sidetracked from testing the opposite fashions.

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

Tetris recreation 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, Considering, 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 dearer than Opus 4.5.

There was lots 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 attempt, GPT-5.2 Professional produced a Tetris recreation with a format bug. The underside rows of the sport have been outdoors of the viewport, and I couldn’t see the place the items have been touchdown.

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

Tetris recreation constructed by GPT-5.2

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

I made a decision to attempt 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. I am unable to see the items once they land and the canvas extends past the seen viewport.

Please repair this by:
1. Ensuring the whole recreation board suits within the viewport
2. Including correct centering so the complete board is seen

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

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

Tetris second attempt by GPT-5.2

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

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

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

(Be aware: I attempted the GPT-5.2 Normal mannequin too, which produced comparable buggy code on the primary attempt.)

// 3. DeepSeek V3.2

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

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

Tetris recreation constructed by DeepSeek V3.2

I re-prompted the mannequin to repair this concern, 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 concern might be fastened with 2–3 extra prompts, and given DeepSeek’s low pricing, you would 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 you may also obtain the mannequin’s weights at no cost and run it by yourself infrastructure.

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

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

For every extra iteration, we’ll estimate an additional 1,500 tokens per extra spherical. Right here is the whole price incurred per mannequin:

# Takeaways

 
Primarily based on my expertise constructing this recreation, I might keep on 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, because the iterations required to yield the identical outcome would possible result in the identical sum of money spent.

DeepSeek V3.2, nevertheless, is way extra inexpensive than the opposite fashions on this checklist.

In case you’re a developer on a price range 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 stunned at GPT 5.2 Professional’s lack of ability to provide a working recreation on the primary attempt, because it took round 6 minutes to suppose earlier than arising with flawed code. In spite of everything, that is OpenAI’s flagship mannequin, and Tetris must 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 will 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 finest at day-to-day coding duties.
• DeepSeek V3.2 is a price range various that delivers cheap output, though it requires some debugging effort to achieve your required final result.
• GPT-5.2 (Normal) 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 copy this take a look at with the immediate I’ve shared above, and joyful coding!
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Natassha Selvaraj is a self-taught knowledge scientist with a ardour for writing. Natassha writes on every part knowledge science-related, a real grasp of all knowledge matters. You may join along with her on LinkedIn or take a look at her YouTube channel.