• Home
  • About Us
  • Contact Us
  • Disclaimer
  • Privacy Policy
Sunday, January 11, 2026
newsaiworld
  • Home
  • Artificial Intelligence
  • ChatGPT
  • Data Science
  • Machine Learning
  • Crypto Coins
  • Contact Us
No Result
View All Result
  • Home
  • Artificial Intelligence
  • ChatGPT
  • Data Science
  • Machine Learning
  • Crypto Coins
  • Contact Us
No Result
View All Result
Morning News
No Result
View All Result
Home Data Science

I Requested ChatGPT, Claude and DeepSeek to Construct Tetris

Admin by Admin
January 6, 2026
in Data Science
0
Kdn selvaraj chatgpt claude deepseek.png
0
SHARES
0
VIEWS
Share on FacebookShare on Twitter


I Asked ChatGPT, Claude and DeepSeek to Build TetrisI Asked ChatGPT, Claude and DeepSeek to Build Tetris
Picture by Creator

 

# Introduction

 
It looks like nearly 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 newest 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.

Nevertheless, I do know builders and pals who wish to construct software program with AI that may be shipped to manufacturing. Since these initiatives are self-funded, their problem lies to find the perfect mannequin to do the job. They wish 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 sport with a single immediate.

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

 

Standards Description
First Try Success With only one immediate, did the mannequin ship working code? A number of debugging iterations results in greater price over time, which is why this metric was chosen.
Characteristic Completeness Have been all of the options talked about within the immediate constructed by the mannequin, or was something missed out?
Playability Past the technical implementation, was the sport really easy to play? Or had been there points that created friction within the person expertise?
Value-effectiveness How a lot did it price to get production-ready code?

 

# The Immediate

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

Construct a totally useful Tetris sport 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 robotically, enhance the pace steadily 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 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 each bit sort
– Easy animations when items transfer and contours clear
– Clear UI with rounded corners
– Replace scores in actual time
– Stage indicator
– Recreation over display screen with ultimate rating and restart button

GAMEPLAY EXPERIENCE AND POLISH:
– Easy 60fps gameplay
– Particle results when traces are cleared (elective however spectacular)
– Enhance the rating based mostly 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 received 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 Gameplay Screen by ClaudeTetris Gameplay Screen by Claude
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: On the spot, 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 is usually 4x dearer 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 format bug. The underside rows of the sport had been exterior 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 game built by GPT-5.2Tetris game built by GPT-5.2
Tetris sport constructed by GPT-5.2

 

I used to be particularly stunned 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 downside:

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 after they land and the canvas extends past the seen viewport.

Please repair this by:
1. Ensuring your entire sport board suits 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 useful sport, as seen within the under screenshot:

 

Tetris Second Try by GPT-5.2Tetris Second Try by GPT-5.2
Tetris second strive by GPT-5.2

 

Nevertheless, the sport play wasn’t as easy 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 generally plummet immediately at a excessive pace, not giving me sufficient time to consider methods to place it.

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

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

 

// 3. DeepSeek V3.2

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

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

 

Tetris game built by DeepSeek V3.2Tetris game built by DeepSeek V3.2
Tetris sport constructed by DeepSeek V3.2

 

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

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

I’m certain that this difficulty will be fastened with 2–3 extra prompts, and given DeepSeek’s low pricing, you might 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:
 

Mannequin Enter (per 1M tokens) Output (per 1M tokens)
DeepSeek V3.2 $0.27 $1.10
GPT-5.2 $1.75 $14.00
Claude Opus 4.5 $5.00 $25.00
GPT-5.2 Professional $21.00 $84.00

 

DeepSeek V3.2 is the most affordable various, and you can too obtain the mannequin’s weights at no cost and run it by yourself infrastructure.

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

For this particular job (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 full price incurred per mannequin:

 

Mannequin Complete Value Outcome
DeepSeek V3.2 ~$0.005 Recreation is not playable
GPT-5.2 ~$0.07 Playable, however poor person expertise
Claude Opus 4.5 ~$0.09 Playable and good person expertise
GPT-5.2 Professional ~$0.41 Playable, however poor person expertise

 

# Takeaways

 
Based mostly on my expertise constructing this sport, I might persist 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 end result would doubtless result in the identical amount of cash spent.

DeepSeek V3.2, nonetheless, is way extra inexpensive than the opposite fashions on this listing.

In the event you’re a developer on a funds 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 incapacity to supply a working sport on the primary strive, because it took round 6 minutes to assume earlier than developing with flawed code. In spite of everything, that is OpenAI’s flagship mannequin, and Tetris needs to be a comparatively easy job.

Nevertheless, 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 an alternative 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 funds various that delivers cheap 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 at liberty to copy this take a look at with the immediate I’ve shared above, and comfortable coding!
&nbsp
&nbsp

Natassha Selvaraj is a self-taught knowledge scientist with a ardour for writing. Natassha writes on the whole lot knowledge science-related, a real grasp of all knowledge subjects. You possibly can join along with her on LinkedIn or try her YouTube channel.

READ ALSO

Highly effective Native AI Automations with n8n, MCP and Ollama

Function of QR Codes in Knowledge-Pushed Advertising


I Asked ChatGPT, Claude and DeepSeek to Build TetrisI Asked ChatGPT, Claude and DeepSeek to Build Tetris
Picture by Creator

 

# Introduction

 
It looks like nearly 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 newest 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.

Nevertheless, I do know builders and pals who wish to construct software program with AI that may be shipped to manufacturing. Since these initiatives are self-funded, their problem lies to find the perfect mannequin to do the job. They wish 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 sport with a single immediate.

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

 

Standards Description
First Try Success With only one immediate, did the mannequin ship working code? A number of debugging iterations results in greater price over time, which is why this metric was chosen.
Characteristic Completeness Have been all of the options talked about within the immediate constructed by the mannequin, or was something missed out?
Playability Past the technical implementation, was the sport really easy to play? Or had been there points that created friction within the person expertise?
Value-effectiveness How a lot did it price to get production-ready code?

 

# The Immediate

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

Construct a totally useful Tetris sport 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 robotically, enhance the pace steadily 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 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 each bit sort
– Easy animations when items transfer and contours clear
– Clear UI with rounded corners
– Replace scores in actual time
– Stage indicator
– Recreation over display screen with ultimate rating and restart button

GAMEPLAY EXPERIENCE AND POLISH:
– Easy 60fps gameplay
– Particle results when traces are cleared (elective however spectacular)
– Enhance the rating based mostly 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 received 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 Gameplay Screen by ClaudeTetris Gameplay Screen by Claude
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: On the spot, 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 is usually 4x dearer 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 format bug. The underside rows of the sport had been exterior 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 game built by GPT-5.2Tetris game built by GPT-5.2
Tetris sport constructed by GPT-5.2

 

I used to be particularly stunned 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 downside:

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 after they land and the canvas extends past the seen viewport.

Please repair this by:
1. Ensuring your entire sport board suits 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 useful sport, as seen within the under screenshot:

 

Tetris Second Try by GPT-5.2Tetris Second Try by GPT-5.2
Tetris second strive by GPT-5.2

 

Nevertheless, the sport play wasn’t as easy 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 generally plummet immediately at a excessive pace, not giving me sufficient time to consider methods to place it.

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

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

 

// 3. DeepSeek V3.2

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

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

 

Tetris game built by DeepSeek V3.2Tetris game built by DeepSeek V3.2
Tetris sport constructed by DeepSeek V3.2

 

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

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

I’m certain that this difficulty will be fastened with 2–3 extra prompts, and given DeepSeek’s low pricing, you might 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:
 

Mannequin Enter (per 1M tokens) Output (per 1M tokens)
DeepSeek V3.2 $0.27 $1.10
GPT-5.2 $1.75 $14.00
Claude Opus 4.5 $5.00 $25.00
GPT-5.2 Professional $21.00 $84.00

 

DeepSeek V3.2 is the most affordable various, and you can too obtain the mannequin’s weights at no cost and run it by yourself infrastructure.

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

For this particular job (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 full price incurred per mannequin:

 

Mannequin Complete Value Outcome
DeepSeek V3.2 ~$0.005 Recreation is not playable
GPT-5.2 ~$0.07 Playable, however poor person expertise
Claude Opus 4.5 ~$0.09 Playable and good person expertise
GPT-5.2 Professional ~$0.41 Playable, however poor person expertise

 

# Takeaways

 
Based mostly on my expertise constructing this sport, I might persist 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 end result would doubtless result in the identical amount of cash spent.

DeepSeek V3.2, nonetheless, is way extra inexpensive than the opposite fashions on this listing.

In the event you’re a developer on a funds 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 incapacity to supply a working sport on the primary strive, because it took round 6 minutes to assume earlier than developing with flawed code. In spite of everything, that is OpenAI’s flagship mannequin, and Tetris needs to be a comparatively easy job.

Nevertheless, 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 an alternative 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 funds various that delivers cheap 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 at liberty to copy this take a look at with the immediate I’ve shared above, and comfortable coding!
&nbsp
&nbsp

Natassha Selvaraj is a self-taught knowledge scientist with a ardour for writing. Natassha writes on the whole lot knowledge science-related, a real grasp of all knowledge subjects. You possibly can join along with her on LinkedIn or try her YouTube channel.

Tags: askedBuildChatGPTClaudeDeepSeekTetris

Related Posts

Kdn powerful local ai automations n8n mcp ollama.png
Data Science

Highly effective Native AI Automations with n8n, MCP and Ollama

January 10, 2026
Image fx 20.jpg
Data Science

Function of QR Codes in Knowledge-Pushed Advertising

January 10, 2026
Kdn 5 useful python scripts automate data cleaning.png
Data Science

5 Helpful Python Scripts to Automate Knowledge Cleansing

January 9, 2026
Image fx 21.jpg
Data Science

How Information Analytics Helps Smarter Inventory Buying and selling Methods

January 9, 2026
Generic ai shutterstock 2 1 2198551419.jpg
Data Science

AI Will Not Ship Enterprise Worth Till We Let It Act

January 8, 2026
Kdn vibe coding what you can actually build.png
Data Science

Vibe Code Actuality Verify: What You Can Really Construct with Solely AI

January 8, 2026
Next Post
A b9f89e.jpg

Bitcoin Bulls Loading Up As Whales And Sharks Purchase The Dip

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

POPULAR NEWS

Chainlink Link And Cardano Ada Dominate The Crypto Coin Development Chart.jpg

Chainlink’s Run to $20 Beneficial properties Steam Amid LINK Taking the Helm because the High Creating DeFi Challenge ⋆ ZyCrypto

May 17, 2025
Image 100 1024x683.png

Easy methods to Use LLMs for Highly effective Computerized Evaluations

August 13, 2025
Gemini 2.0 Fash Vs Gpt 4o.webp.webp

Gemini 2.0 Flash vs GPT 4o: Which is Higher?

January 19, 2025
Blog.png

XMN is accessible for buying and selling!

October 10, 2025
0 3.png

College endowments be a part of crypto rush, boosting meme cash like Meme Index

February 10, 2025

EDITOR'S PICK

1ajfxmu569arbpu4x8ixwew.png

Lacking Worth Imputation, Defined: A Visible Information with Code Examples for Learners | by Samy Baladram | Aug, 2024

August 27, 2024
Conf matrix.png

Pairwise Cross-Variance Classification | In direction of Information Science

June 4, 2025
Growtika Ngocbxiaro0 Unsplash.jpg

Will Qu?antum Computer systems Outpace Our Means to Safe Knowledge

September 28, 2024
1ceigzqpsesgfv2mwe9cmqa.jpeg

Bridging the Knowledge Literacy Hole. The Introduction, Evolution, and Present… | by Nithhyaa Ramamoorthy | Dec, 2024

December 6, 2024

About Us

Welcome to News AI World, your go-to source for the latest in artificial intelligence news and developments. Our mission is to deliver comprehensive and insightful coverage of the rapidly evolving AI landscape, keeping you informed about breakthroughs, trends, and the transformative impact of AI technologies across industries.

Categories

  • Artificial Intelligence
  • ChatGPT
  • Crypto Coins
  • Data Science
  • Machine Learning

Recent Posts

  • Bitcoin Community Mining Problem Falls in Jan 2026
  • Past the Flat Desk: Constructing an Enterprise-Grade Monetary Mannequin in Energy BI
  • Federated Studying, Half 1: The Fundamentals of Coaching Fashions The place the Information Lives
  • Home
  • About Us
  • Contact Us
  • Disclaimer
  • Privacy Policy

© 2024 Newsaiworld.com. All rights reserved.

No Result
View All Result
  • Home
  • Artificial Intelligence
  • ChatGPT
  • Data Science
  • Machine Learning
  • Crypto Coins
  • Contact Us

© 2024 Newsaiworld.com. All rights reserved.

Are you sure want to unlock this post?
Unlock left : 0
Are you sure want to cancel subscription?