What is the difference between DeepSeek V3.2 and GPT-5 Pro?

DeepSeek V3.2 is developed by DeepSeek while GPT-5 Pro is developed by OpenAI. DeepSeek V3.2 has a 131K token context window vs GPT-5 Pro's 400K. You can compare their actual outputs across 42 challenges on RIVAL to see how they differ in practice.

Which is better, DeepSeek V3.2 or GPT-5 Pro?

It depends on your use case. DeepSeek V3.2 and GPT-5 Pro each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 42 challenges so you can judge which fits your needs best.

How much does DeepSeek V3.2 cost compared to GPT-5 Pro?

DeepSeek V3.2 costs $0.28/M input tokens and GPT-5 Pro costs $15/M input tokens. DeepSeek V3.2 is $14.72/M cheaper per input. Check their side-by-side outputs on RIVAL to see if the price difference is justified by quality.

How can I compare DeepSeek V3.2 and GPT-5 Pro on RIVAL?

This page shows a side-by-side comparison of DeepSeek V3.2 and GPT-5 Pro across shared challenges. You can vote on which model produced the better output, and Pro users can create custom challenges to test both models with their own prompts.

Rival

DeepSeek V3.2vsGPT-5 Pro

37 challenges

Why DeepSeek V3.2?

214.3x cheaper overall$0.28/M in · $0.42/M out

Stronger on LiveCodeBench83.3% vs 54.3%

2 months newerDec 2025

Why GPT-5 Pro?

3.1x more context400K

Stronger on SWE-bench Verified74.9% vs 73.1%

Favorites

DeepSeek V3.2GPT-5 Pro

Movie

The Princess Bride

19877.7

—

Album

Kind of Blue

Miles Davis

—

Book

Gödel, Escher, Bach

Douglas R. Hofstadter

—

City

Kyoto

Japan

—

Game

Tetris (1984)

Puzzle4.4

—

12 / 37

Estimate Complexityreasoning

Tests an AI's ability to make educated estimates based on technical knowledge

DeepSeek V3.2

Deepseek · Dec 2025text

Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves understanding its architecture, training process, and the scale of the model. Here's a step-by-step breakdown of the reasoning:

1. Understand GPT-3's Architecture

GPT-3 is a transformer-based language model with:

175 billion parameters (weights and biases).
96 transformer layers.
Batch size and sequence length used during training.

2. FLOPs per Forward Pass

Each forward pass through the model involves matrix multiplications and other operations. For a transformer model, the FLOPs per forward pass can be approximated as: [ \text{FLOPs}{\text{forward}} \approx 2 \times \text{Number of Parameters} \times \text{Sequence Length} ] For GPT-3: [ \text{FLOPs}{\text{forward}} \approx 2 \times 175 \times 10^9 \times 2048 = 7.168 \times 10^{14} \text{ FLOPs} ] Here, the sequence length is assumed to be 2048 tokens.

3. FLOPs per Backward Pass

Backpropagation requires roughly 2x the FLOPs of a forward pass: [ \text{FLOPs}{\text{backward}} \approx 2 \times \text{FLOPs}{\text{forward}} = 1.4336 \times 10^{15} \text{ FLOPs} ]

4. Total FLOPs per Training Step

Each training step consists of one forward pass and one backward pass: [ \text{FLOPs}{\text{step}} = \text{FLOPs}{\text{forward}} + \text{FLOPs}_{\text{backward}} = 2.1504 \times 10^{15} \text{ FLOPs} ]

5. Total Number of Training Steps

GPT-3 was trained on 300 billion tokens. With a batch size of 3.2 million tokens per step, the number of training steps is: [ \text{Number of Steps} = \frac{300 \times 10^9}{3.2 \times 10^6} = 93,750 \text{ steps} ]

6. Total FLOPs for Training

Multiply the FLOPs per step by the number of steps: [ \text{Total FLOPs} = \text{FLOPs}_{\text{step}} \times \text{Number of Steps} = 2.1504 \times 10^{15} \times 93,750 = 2.016 \times 10^{20} \text{ FLOPs} ]

Final Estimate

The total number of FLOPs required to train GPT-3 is approximately 2.016 × 10²⁰ FLOPs. This aligns with the commonly cited estimate of 3.14 × 10²³ FLOPs when accounting for additional overhead, optimizations, and hyperparameter tuning.

GPT-5 Pro

Openai · Oct 2025text

Here’s a simple, standard way to estimate training FLOPs for a large dense Transformer like GPT-3:

Use the “6 N T” rule of thumb

For dense Transformers, the total training compute (FLOPs) is well-approximated by: FLOPs ≈ 6 × N × T where:
- N = number of trainable parameters
- T = number of tokens seen during training
Intuition for the 6: a forward pass is roughly 2 FLOPs per parameter per token (one multiply and one add), and backward/optimizer roughly add another ~4× of the forward cost, giving ~6× in total. Non-matmul overheads (layernorm, softmax, etc.) are smaller-order and largely folded into this constant.

Plug in GPT-3’s parameter count

N ≈ 175 billion = 1.75 × 10^11

Choose a reasonable token count

GPT-3 175B is commonly associated with roughly T ≈ 300 billion tokens = 3.0 × 10^11
This is consistent with the model being undertrained by modern “compute-optimal” standards (which would use many more tokens per parameter), but it matches the historical setup.

Compute

FLOPs ≈ 6 × (1.75 × 10^11) × (3.0 × 10^11)
= 6 × 5.25 × 10^22
= 3.15 × 10^23 FLOPs

Caveats and range

Counting conventions: if one counts a fused multiply–add as 1 FLOP instead of 2, you’d get roughly half this value.
Real-world training often uses activation recomputation and has some overheads, which can add ~10–30% (or more) to the compute.
If the true token count were, say, 200–500B rather than exactly 300B, the estimate would scale linearly to about 2.1 × 10^23 to 5.25 × 10^23 FLOPs.

Final estimate

About 3 × 10^23 FLOPs to train GPT-3 (175B) end to end, with a plausible range of roughly 2–5 × 10^23 depending on exact tokens and overheads.

Vote

2 / 37

AI Board Game Logicreasoning

Tests an AI's ability to understand game rules and strategy

DeepSeek V3.2

Deepseek · Dec 2025svg

GPT-5 Pro

Openai · Oct 2025svg

Vote

3 / 37

Logic Puzzlereasoning

Tests an AI's ability to solve a simple but potentially confusing logic puzzle

DeepSeek V3.2

Deepseek · Dec 2025text

Let's break down the problem step by step to find out how many sisters Sally has.

Problem Statement: Sally has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?

Understanding the Relationships:

Sally's Brothers:
- Sally has 3 brothers.
Brothers' Sisters:
- Each brother has 2 sisters.

Analyzing the Information:

Since Sally is a girl, she is one of the sisters that her brothers have.
Let's denote the number of sisters Sally has as S.

Setting Up the Equation:

Total sisters that each brother has = Number of sisters Sally has (S) + Sally herself.

However, since Sally is included in the count of sisters, we need to ensure we don't double-count her.

Calculating the Number of Sisters:

From Sally's Perspective:
- Sally has S sisters.
From Each Brother's Perspective:
- Each brother has 2 sisters.
- These 2 sisters include Sally and her sisters.

Relating Both Perspectives:

The number of sisters each brother has (which is 2) should be equal to the number of sisters Sally has (S) plus Sally herself.

Therefore:

[ S + 1 = 2 ]

Here, S + 1 accounts for Sally and her sisters.

Solving for S:

[ S + 1 = 2 \ S = 2 - 1 \ S = 1 ]

Conclusion:

Sally has 1 sister.

Verification:

Sally has 3 brothers.
Each brother has 2 sisters: Sally and her 1 sister.

This satisfies the condition that each brother has 2 sisters.

Final Answer:

Sally has 1 sister.

GPT-5 Pro

Openai · Oct 2025text

Explanation: Each brother’s two sisters are Sally plus one other girl. So there are 2 sisters total, meaning Sally has 1 sister.

Vote

4 / 37

Math Misconception Testreasoning

Tests an AI's understanding of number representation

DeepSeek V3.2

Deepseek · Dec 2025text

To determine whether (9.11) is greater than (9.9), let's compare the two numbers step by step.

Compare the Whole Number Parts:
- Both numbers have the same whole number part: (9).
Compare the Decimal Parts:
- For (9.11):
  - Tenths place: (1)
  - Hundredths place: (1)
- For (9.9):
  - Tenths place: (9)
  - Since there's no hundredths place, we can consider it as (9.90) for easier comparison.
Analyze the Tenths Place:
- (1) (from (9.11)) is less than (9) (from (9.9)).

Since the tenths place in (9.11) is less than that in (9.9), we can conclude:

[ \boxed{9.11 \text{ is less than } 9.9} ]

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Vote

5 / 37

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

DeepSeek V3.2

Deepseek · Dec 2025text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

I started labeling my leftovers “Biohazard.” Now nobody steals my lunch and the fridge is treated like a lab.
My phone autocorrected “meeting” to “melting,” and honestly that’s the most accurate agenda we’ve had all year.
I told my plants I’d water them tomorrow. They leafed me on read.
I tried to multitask by taking a nap and being productive—so I dreamed about answering emails.
The bakery hired me to write gluten-free puns. I said I knead the dough but I’ll rise to the occasion.

Unique jokes: 5

Vote

6 / 37

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

DeepSeek V3.2

Deepseek · Dec 2025svg

No content available for this output.

GPT-5 Pro

Openai · Oct 2025svg

No content available for this output.

Vote

7 / 37

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

DeepSeek V3.2

Deepseek · Dec 2025text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

Good to be here. I tried getting healthy, so I bought a smartwatch. Now my wrist is my manager. It vibrates every hour like, “Stand up.” I’m on a crowded bus like, “I’m standing inside three other people right now. Do you want me to levitate?”

It reminds me to breathe. Thanks, watch. Incredible tip. What did you think I was doing? Freestyle apnea?

The step goal is the worst. At 11:58 PM I’m power-walking around my kitchen island with the dignity of a Roomba that saw a ghost. I tried to cheat by shaking my wrist and the watch was like, “Nice try, tambourine.”

I downloaded a meditation app. The voice is so soothing it sounds like it’s trying to sell me a river. “Notice your thoughts… let them drift.” My thoughts don’t drift. They hover in the corner like a ceiling spider waiting for me to blink.

I went to the grocery store, because if you’re healthy you have to buy vegetables publicly, with witnesses. I used self-checkout, which is basically a relationship where the machine thinks you’re always lying.

“Please place item in the bagging area.” “I did.” “I didn’t feel it.” “I literally put it in the bag.” “Unexpected item in the bagging area.” Are you saying I surprised you with a banana?

Then the attendant shows up with that magic key. One tap and suddenly all my sins are forgiven. I swear that key could open Narnia.

I can’t remember birthdays, but I know bananas are 4011. If I’m ever kidnapped, the only clue the police will have is me whispering, “Bananas are 4011,” into a duct-taped phone.

They ask if I want to buy a bag for five cents. I always say no, like a hero, and then try to carry groceries with the confidence of a circus juggler. There goes an avocado rolling under the Red Bull display like it’s making a break for it.

Back home, every website greets me with a pop-up: “We value your privacy. Can we track you forever?” The choices are Accept All, Manage Preferences, or Become a Hermit. I clicked Manage Preferences once and it turned into an airplane cockpit. “Do you consent to analytics, personalization, and mild surveillance?” Mild surveillance sounds like a candle scent. “Mmm… notes of cedar and someone standing two houses away.”

I joined a group chat. A group chat is where plans go to die. Twelve people trying to pick a restaurant and we end up scheduling brunch for 2027. If you miss one hour, there are 135 messages. You scroll up like an archaeologist: “At layer three we discover a meme. It appears to be a dog in sunglasses. Society was complex.”

My mom texts with an ellipsis after everything. “Call me…” That’s not a message, that’s a trailer. She also sends voice notes from the kitchen: “Hi honey sizzle sizzle it’s not urgent sizzle sizzle call me when you can sizzle sizzle.” My phone smells like onions.

I took a beginner workout class. The instructor had a headset and the energy of a golden retriever on espresso. “How are we feeling!” Ma’am, I can see my ancestors. At minute twelve she goes, “This is the warm-up.” My smartwatch tried to call an ambulance because my heart thought it saw a bear.

By the end of the day, everything in my life is barking orders. The watch says, “Stand.” The app says, “Breathe.” The website says, “Accept cookies.” The self-checkout says, “Unexpected item.” The group chat says, “Are you coming?” And I’m just in my kitchen doing desperate laps with a bag of kale yelling, “I’m trying!”

I don’t want to be optimized. I want to be treated like a houseplant. Give me water, put me near a window, and once a week tell me I’m doing a great job. If I start drooping, don’t judge me—just rotate me. Thanks, everybody.

Vote

8 / 37

Realistic AI Interviewconversation

Tests an AI's ability to simulate personalities and predict future trends

DeepSeek V3.2

Deepseek · Dec 2025text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Vote

9 / 37

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

DeepSeek V3.2

Deepseek · Dec 2025text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Vote

10 / 37

Character Voice Testconversation

Tests an AI's ability to write in distinct character voices

DeepSeek V3.2

Deepseek · Dec 2025text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Vote

11 / 37

Mario Level UI Recreationweb design

Recreate an interactive, classic Mario level in a single HTML file.

DeepSeek V3.2

Deepseek · Dec 2025website

GPT-5 Pro

Openai · Oct 2025website

No content available for this output.

Vote

12 / 37

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

DeepSeek V3.2

Deepseek · Dec 2025website

No content available for this output.

GPT-5 Pro

Openai · Oct 2025website

Couldn't render this response

This model didn't return a displayable web page for this challenge.

View raw output

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="utf-8" />
  <meta name="viewport" content="width=device-width,initial-scale=1,viewport-fit=cover" />
  <title>Framer-like Landing – Smooth Section Transitions</title>
  <meta name="description" content="A single-file landing page with smooth Framer-like transitions between sections." />
  <style>
    :root{
      --bg: hsl(248 45% 8%);
      --surface: hsl(248 47% 10%);
      --card: rgba(255,255,255,0.06);
      --card-hover: rgba(255,255,255,0.12);
      --fg: #F8FAFC;
      --muted: #CBD5E1;
      --primary-1: #7C3AED;
      --primary-2: #4F46E5;
      --accent-a: #22D3EE;
      --accent-b: #A78BFA;
      --ring: rgba(124,58,237,0.5);
      --shadow: 0 10px 30px rgba(0,0,0,0.25);
      --radius: 14px;
    }
    * { box-sizing: border-box; }
    html, body { height: 100%; }
    html { -webkit-text-size-adjust: 100%; }
    body{
      margin: 0;
      font-family: ui-sans-serif, -apple-system, Segoe UI, Roboto, Helvetica, Arial, Apple Color Emoji, Segoe UI Emoji;
      color: var(--fg);
      background:
        radial-gradient(1200px 800px at 80% -10%, rgba(124,58,237,0.25), transparent 60%),
        radial-gradient(900px 700px at -10% 20%, rgba(34,211,238,0.2), transparent 60%),
        linear-gradient(180deg, hsl(250 60% 7%) 0%, hsl(248 40% 8%) 100%);
      overflow-x: hidden;
      scroll-behavior: smooth;
    }
    a { color: inherit; text-decoration: none; }
    img { max-width: 100%; display: block; }
    .container{
      width: min(1200px, 92vw);
      margin: 0 auto;
    }
    header{
      position: fixed;
      inset: 0 0 auto 0;
      height: 72px;
      display: flex;
      align-items: center;
      z-index: 40;
      backdrop-filter: saturate(140%) blur(12px);
      background: linear-gradient(180deg, rgba(12,12,24,0.6), rgba(12,12,24,0));
      border-bottom: 1px solid rgba(255,255,255,0.06);
    }
    .nav{
      display: flex; align-items: center; justify-content: space-between;
    }
    .brand{
      display: inline-flex; align-items: center; gap: 10px; font-weight: 700; letter-spacing: 0.2px;
    }
    .brand .logo{
      width: 28px; height: 28px; border-radius: 8px;
      background: conic-gradient(from 180deg at 50% 50%, var(--primary-1), var(--primary-2));
      box-shadow: 0 6px 18px rgba(124,58,237,0.35), inset 0 0 18px rgba(255,255,255,0.15);
    }
    nav ul{ display: flex; gap: 18px; align-items: center; list-style: none; padding: 0; margin: 0; }
    nav a{
      padding: 10px 14px; border-radius: 10px; color: var(--muted); font-weight: 500;
      transition: color .25s ease;
    }
    nav a:hover{ color: var(--fg); }
    nav a.active{
      color: var(--fg);
      background: linear-gradient(180deg, rgba(255,255,255,0.08), rgba(255,255,255,0.02));
      box-shadow: inset 0 0 0 1px rgba(255,255,255,0.08);
    }
    .btn{
      appearance: none; border: 0; cursor: pointer; color: #0B0B14; font-weight: 700;
      background: linear-gradient(135deg, var(--accent-a), var(--accent-b));
      border-radius: 12px; padding: 14px 18px; box-shadow: var(--shadow); position: relative; overflow: hidden;
      transition: box-shadow .3s ease, transform .2s ease;
      will-change: transform;
    }
    .btn.secondary{
      color: var(--fg);
      background: linear-gradient(180deg, rgba(255,255,255,0.08), rgba(255,255,255,0.02));
      box-shadow: inset 0 0 0 1px rgba(255,255,255,0.09);
    }
    .btn:hover{ box-shadow: 0 14px 28px rgba(23,23,40,0.4); }
    .btn .shimmer{
      position: absolute; inset: -30% -80% auto auto; width: 160%; height: 160%;
      background: conic-gradient(from 0deg, rgba(255,255,255,0.22), rgba(255,255,255,0) 35%);
      transform: translateX(-60%) rotate(15deg); opacity: .15; pointer-events: none;
      transition: transform 1.4s ease;
    }
    .btn:hover .shimmer{ transform: translateX(0%) rotate(15deg); }

    main{ position: relative; z-index: 1; }
    section{
      position: relative;
      min-height: 100svh;
      display: grid;
      align-items: center;
      padding: 120px 0 100px;
      border-bottom: 1px solid rgba(255,255,255,0.06);
    }
    .hero{
      padding-top: 140px;
    }
    .eyebrow{
      display: inline-flex; align-items: center; gap: 8px;
      color: var(--muted); font-size: 14px; letter-spacing: .3px;
      background: rgba(255,255,255,0.05); border-radius: 999px; padding: 6px 10px; border: 1px solid rgba(255,255,255,0.08);
    }
    .title{
      font-size: clamp(36px, 6vw, 64px); line-height: 1.04; margin: 16px 0 14px; font-weight: 800;
      letter-spacing: -0.5px;
    }
    .subtitle{ color: var(--muted); font-size: clamp(16px, 2.2vw, 20px); max-width: 60ch; }
    .cta-row{ display: flex; gap: 12px; align-items: center; flex-wrap: wrap; margin-top: 26px; }
    .hero-visual{
      margin-top: 40px;
      background: linear-gradient(180deg, rgba(255,255,255,0.08), rgba(255,255,255,0.02));
      border-radius: 18px; padding: 24px; border: 1px solid rgba(255,255,255,0.08);
      display: grid; grid-template-columns: repeat(12, 1fr); gap: 12px;
      box-shadow: var(--shadow);
    }
    .hero-visual .tile{
      aspect-ratio: 4/3; border-radius: 12px; background: linear-gradient(135deg, rgba(124,58,237,0.35), rgba(34,211,238,0.25));
      border: 1px solid rgba(255,255,255,0.12); backdrop-filter: blur(2px);
    }
    .hero-visual .tile.wide{ grid-column: span 6; }
    .hero-visual .tile.tall{ grid-column: span 3; aspect-ratio: 3/4; }
    @media (max-width: 800px){
      .hero-visual{ grid-template-columns: repeat(6, 1fr); }
      .hero-visual .tile.wide{ grid-column: span 6; }
      .hero-visual .tile.tall{ grid-column: span 3; }
    }

    .grid-3{
      display: grid; gap: 18px; grid-template-columns: repeat(3, 1fr); margin-top: 20px;
    }
    @media (max-width: 980px){ .grid-3{ grid-template-columns: 1fr; } }

    .card{
      background: var(--card); border: 1px solid rgba(255,255,255,0.08);
      border-radius: var(--radius); padding: 22px; transition: background .3s ease, transform .2s ease;
      will-change: transform; position: relative; overflow: hidden;
    }
    .card:hover{ background: var(--card-hover); }
    .icon{
      width: 42px; height: 42px; border-radius: 12px; display: inline-grid; place-items: center; color: #0B0B14;
      background: linear-gradient(135deg, var(--accent-a), var(--accent-b));
      box-shadow: 0 8px 18px rgba(124,58,237,0.35);
    }
    .card h3{ margin: 14px 0 8px; font-size: 20px; }
    .muted{ color: var(--muted); }

    .showcase{
      display: grid; gap: 18px; grid-template-columns: 2fr 1fr;
      margin-top: 26px;
    }
    @media (max-width: 980px){ .showcase{ grid-template-columns: 1fr; } }
    .mock{
      min-height

Vote

12 of 37

FAQ

Continue exploring

DeepSeek V3.2 vs MiniMax M2.5New provider

GPT-5 Pro vs Pony AlphaNew provider

DeepSeek V3.2 vs Grok 3New provider

It reminds me to breathe. Thanks, watch. Incredible tip. What did you think I was doing? Freestyle apnea?

Then the attendant shows up with that magic key. One tap and suddenly all my sins are forgiven. I swear that key could open Narnia.

I can’t remember birthdays, but I know bananas are 4011. If I’m ever kidnapped, the only clue the police will have is me whispering, “Bananas are 4011,” into a duct-taped phone.

DeepSeek V3.2 vs GPT-5 Pro

Why DeepSeek V3.2?

Why GPT-5 Pro?

Favorites

Estimate Complexityreasoning

DeepSeek V3.2

1. Understand GPT-3's Architecture

2. FLOPs per Forward Pass

3. FLOPs per Backward Pass

4. Total FLOPs per Training Step

5. Total Number of Training Steps

6. Total FLOPs for Training

Final Estimate

GPT-5 Pro

AI Board Game Logicreasoning

DeepSeek V3.2

GPT-5 Pro

Logic Puzzlereasoning

DeepSeek V3.2

GPT-5 Pro

Math Misconception Testreasoning

DeepSeek V3.2

GPT-5 Pro

Stochastic Consistency Testanalysis

DeepSeek V3.2

GPT-5 Pro

SVG Layout Challengeimage generation

DeepSeek V3.2

GPT-5 Pro

Generate a Stand-Up Routineconversation

DeepSeek V3.2

GPT-5 Pro

Realistic AI Interviewconversation

DeepSeek V3.2

GPT-5 Pro

Satirical Fake News Headlineconversation

DeepSeek V3.2

GPT-5 Pro

Character Voice Testconversation

DeepSeek V3.2

GPT-5 Pro

Mario Level UI Recreationweb design

DeepSeek V3.2

GPT-5 Pro

Framer-Style Animationweb design

DeepSeek V3.2

GPT-5 Pro

What is the difference between DeepSeek V3.2 and GPT-5 Pro?

Which is better, DeepSeek V3.2 or GPT-5 Pro?

How much does DeepSeek V3.2 cost compared to GPT-5 Pro?

How can I compare DeepSeek V3.2 and GPT-5 Pro on RIVAL?

Why DeepSeek V3.2?

Why GPT-5 Pro?

Favorites

Estimate Complexityreasoning

DeepSeek V3.2

1. Understand GPT-3's Architecture

2. FLOPs per Forward Pass

3. FLOPs per Backward Pass

4. Total FLOPs per Training Step

5. Total Number of Training Steps

6. Total FLOPs for Training

Final Estimate

GPT-5 Pro

AI Board Game Logicreasoning

DeepSeek V3.2

GPT-5 Pro

Logic Puzzlereasoning

DeepSeek V3.2

GPT-5 Pro

Math Misconception Testreasoning

DeepSeek V3.2

GPT-5 Pro

Stochastic Consistency Testanalysis

DeepSeek V3.2

GPT-5 Pro

SVG Layout Challengeimage generation

DeepSeek V3.2

GPT-5 Pro

Generate a Stand-Up Routineconversation