Updated Aug 7, 2025

Our Verdict

GPT-5 Nano

Qwen3 235B A22B

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

Too close to call

Writing DNA

Style Comparison

Similarity

78%

GPT-5 Nano uses 23.3x more hedging

GPT-5 Nano

Qwen3 235B A22B

59%Vocabulary68%

25wSentence Length9w

0.23Hedging0.00

0.0Bold5.8

4.2Lists3.7

0.00Emoji0.74

0.00Headings0.00

0.01Transitions0.12

Based on 14 + 2 text responses

vs

Ask them anything yourself

GPT-5 Nano

Qwen3 235B A22B

279 AI models invented the same fake scientist.

We read every word. 250 models. 2.14 million words. This is what we found.

AI Hallucination Index 2026

Free preview13 of 58 slides

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Qwen3 235B A22B vs Gemini 2.5 Pro (I/O Edition)New provider

GPT-5 NanovsQwen3 235B A22B

45 fights queued

Why GPT-5 Nano?

3 months newerAug 2025

Why Qwen3 235B A22B?

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified61.7% vs 54.7%

GPT-5 NanoQwen3 235B A22B

Context

—

33K

Released

Aug 2025

Apr 2025

Benchmarks1 common

SWE-bench Verified

54.7%

61.7%+11.3%

Favorites

GPT-5 NanoQwen3 235B A22B

Movie

—

The Godfather

The Godfather

Album

—

Sgt Peppers Lonely Hearts Club Band

Sgt Peppers Lonely Hearts Club Band

Book

—

Nineteen Eighty-Four

Nineteen Eighty-Four

City

—

Istanbul

Game

—

The Legend of Zelda: Ocarina of Time

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

12 / 45

01

Estimate Complexityreasoning

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025text

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025text

Step-by-Step Estimation of FLOPs Required to Train GPT-3

Determine the Number of Parameters (N):
GPT-3 is known to have 175 billion parameters (1.75 × 10¹¹). This is a key factor in computing the total FLOPs, as the number of operations scales with the number of parameters.
Estimate the Number of Training Tokens (T):
Large language models like GPT-3 are trained on massive datasets. Based on common knowledge and prior reports, GPT-3 was trained on approximately 300 billion tokens (3 × 10¹¹). This number is derived from the scale of training data used in similar models and the observed performance improvements.
Understand the FLOPs per Token and Parameter:
Training involves forward and backward passes through the network. Each parameter is used multiple times during these passes. A widely accepted approximation in machine learning literature is that each token requires 6 FLOPs per parameter (combining forward and backward operations). This factor of 6 accounts for:
- Forward Pass: 2 FLOPs per parameter (multiply and add for forward computation).
- Backward Pass: 4 FLOPs per parameter (gradient computation for weights and inputs).
This results in 6 FLOPs per parameter per token.
Calculate Total FLOPs:
Multiply the number of parameters (N), the number of tokens (T), and the FLOPs per parameter per token (6):
$$ \text{Total FLOPs} = 6 \times N \times T = 6 \times 1.75 \times 10^{11} \times 3 \times 10^{11} $$
Performing the calculation:
$$ 6 \times 1.75 \times 3 = 31.5 \quad \Rightarrow \quad 31.5 \times 10^{22} = 3.15 \times 10^{23} , \text{FLOPs}. $$
Final Estimate:
Rounding to one significant figure for simplicity, the total number of FLOPs required to train GPT-3 is approximately 3 × 10²³ FLOPs.

Summary of Key Assumptions and Reasoning:

Parameters (N): 175 billion (1.75 × 10¹¹) based on public knowledge.
Tokens (T): 300 billion (3 × 10¹¹) as a reasonable estimate for the scale of training data.
FLOPs per Parameter per Token: 6, derived from the combination of forward and backward pass operations.
Formula: Total FLOPs ≈ 6 × N × T.

This approach aligns with standard practices in estimating compute requirements for training large language models, such as those outlined in the Chinchilla scaling laws and similar research.

Final Answer:

The total number of FLOPs required to train GPT-3 is approximately 3 × 10²³ FLOPs.

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2 / 45

02

AI Board Game Logicreasoning

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025svg

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025svg

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

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3 / 45

03

Logic Puzzlereasoning

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025text

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025text

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

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4 / 45

04

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025text

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025text

Here are 5 jokes I generated:

Why don't skeletons fight each other?
They don't have the guts!
What do you call a fake noodle?
An impasta!
Why did the math book look sad?
Because it had too many problems!
What do you call a fish wearing a bowtie?
Sofishticated!
Why did the programmer quit?
Because he didn't get arrays (a raise)!

How many are unique?
All 5 are unique! While some joke structures (e.g., puns, wordplay) overlap, the topics (skeletons, math, fish, programming) and punchlines are distinct. Even if some ideas are common, they don't repeat within this set. 😄

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5 / 45

05

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025svg

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025svg

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6 / 45

06

Xbox Controller SVG Artimage generation

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025svg

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025svg

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Sponsored

7 / 45

07

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025text

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025text

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

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8 / 45

08

Realistic AI Interviewconversation

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025text

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025text

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

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9 / 45

09

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025text

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025text

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

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10 / 45

10

Character Voice Testconversation

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025text

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025text

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

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11 / 45

11

Minimalist Landing Pageweb design

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025website

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025website

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12 / 45

12

Pokémon Battle UI Recreationweb design

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

GPT-5 Nano

GPT-5 Nano

Openai · Aug 2025website

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

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Qwen3 235B A22B

Qwen3 235B A22B

Qwen · Apr 2025website

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

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