Updated Mar 10, 2026

Our Verdict

Qwen3.5 9B

GPT-4o (Omni)

No community votes yet. On paper, these are closely matched - try both with your actual task to see which fits your workflow.

Qwen3.5 9B is 67x cheaper per token — worth considering if cost matters.

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Qwen3.5 9BvsGPT-4o (Omni)

44 fights queued

Why Qwen3.5 9B?

53.8x cheaper overall$0.10/M in · $0.15/M out

2x more context262K

22 months newerMar 2026

Why GPT-4o (Omni)?

Dead even. This one's a coin flip.

Favorites

Qwen3.5 9BGPT-4o (Omni)

Movie

The Godfather

The Godfather

Inception

Inception

Album

The Dark Side of the Moon

The Dark Side of the Moon

Dark Side Of The Moon

Dark Side Of The Moon

Book

Dune

Dune

To Kill a Mockingbird

To Kill a Mockingbird

City

Paris

Kyoto

Game

—

The Legend of Zelda: Breath of the Wild

The Legend of Zelda: Breath of the Wild

Adventure, Action4.5

12 / 44

01

Estimate Complexityreasoning

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

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026text

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024text

To estimate the total number of FLOPs required to train GPT-3, let's break it down into key components.

1. Model Size and Parameters

GPT-3 has 175 billion parameters.

2. FLOPs per Forward and Backward Pass

In deep learning, the FLOPs required for a forward pass and backward pass are estimated as:

Forward pass: ( 2 imes ) the number of parameters
Backward pass: ( 2 imes ) the FLOPs of the forward pass

Thus, total FLOPs per training step:

[ ext{Total FLOPs per step} = 6 imes ext{number of parameters} imes ext{tokens per batch} ]

For GPT-3, let's assume 175B parameters and a batch size of 2048 tokens (common for large-scale training).

[ ext{FLOPs per training step} = 6 imes 175 imes 10^9 imes 2048 ]

[ = 2.15 imes 10^{15} ext{ FLOPs per step} ]

3. Total Training Steps

GPT-3 was trained with 300 billion tokens (as per OpenAI's paper).

If the batch size is 2048 tokens, the total number of steps is:

[ ext{Total steps} = rac{300 imes 10^9}{2048} approx 1.46 imes 10^8 ]

4. Total FLOPs Required

Multiplying the FLOPs per step by the total number of steps:

[ ext{Total FLOPs} = (2.15 imes 10^{15}) imes (1.46 imes 10^8) ]

[ approx 3.14 imes 10^{23} ext{ FLOPs} ]

Final Answer:

The estimated total number of FLOPs required to train GPT-3 is about ( 3.14 imes 10^{23} ) floating point operations.

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2 / 44

02

AI Board Game Logicreasoning

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

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026svg

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024svg

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3 / 44

03

Logic Puzzlereasoning

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

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026text

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024text

Nothing here. The model returned empty. We stared at it for a while.

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4 / 44

04

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026text

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024text

Here are five jokes for you:

Why did the scarecrow win an award?
Because he was outstanding in his field!
Why don’t skeletons fight each other?
They don’t have the guts!
Why did the math book look sad?
It had too many problems.
Why don’t eggs tell jokes?
Because they might crack up!
What did one ocean say to the other ocean?
Nothing, they just waved.

Now, let's check how many of these jokes are unique. I'll analyze them for similarity.

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity

List of jokes

jokes = [ "Why did the scarecrow win an award? Because he was outstanding in his field!", "Why don’t skeletons fight each other? They don’t have the guts!", "Why did the math book look sad? It had too many problems.", "Why don’t eggs tell jokes? Because they might crack up!", "What did one ocean say to the other ocean? Nothing, they just waved." ]

Vectorize the jokes

vectorizer = TfidfVectorizer().fit_transform(jokes) similarity_matrix = cosine_similarity(vectorizer)

Count unique jokes (jokes with low similarity to others)

unique_jokes = set() threshold = 0.5 # Define similarity threshold

for i in range(len(jokes)): is_unique = True for j in range(len(jokes)): if i != j and similarity_matrix[i, j] > threshold: is_unique = False break if is_unique: unique_jokes.add(jokes[i])

Count unique jokes

num_unique_jokes = len(unique_jokes) num_unique_jokes

All 5 jokes are unique! None of them are too similar to each other. 🎉

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5 / 44

05

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026svg

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024svg

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6 / 44

06

Xbox Controller SVG Artimage generation

Tests an AI's ability to create detailed SVG illustrations of gaming hardware

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026svg

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024svg

Nothing here. The model returned empty. We stared at it for a while.

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7 / 44

07

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026text

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024text

Nothing here. The model returned empty. We stared at it for a while.

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8 / 44

08

Realistic AI Interviewconversation

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

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026text

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024text

Nothing here. The model returned empty. We stared at it for a while.

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Which one handles your task better?

Paste your actual prompt. Same models, your work. 30 seconds.

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9 / 44

09

Character Voice Testconversation

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

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026text

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024text

Nothing here. The model returned empty. We stared at it for a while.

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10 / 44

10

Minimalist Landing Pageweb design

Tests an AI's ability to generate a complete, working landing page

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026website

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024website

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11 / 44

11

Pokémon Battle UI Recreationweb design

Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026website

Nothing here. The model returned empty. We stared at it for a while.

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024website

Nothing here. The model returned empty. We stared at it for a while.

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12 / 44

12

Mario Level UI Recreationweb design

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

Qwen3.5 9B

Qwen3.5 9B

Qwen · Mar 2026website

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GPT-4o (Omni)

GPT-4o (Omni)

Openai · May 2024website

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