Updated May 6, 2025

Our Verdict

Gemini 2.5 Pro (I/O Edition)Winner

OpenAI o4 Mini HighRunner-up

Pick Gemini 2.5 Pro (I/O Edition). In 5 blind votes, Gemini 2.5 Pro (I/O Edition) wins 100% of the time. That's not luck.

Gemini 2.5 Pro (I/O Edition) particularly excels in Image Generation.

Clear winner

Writing DNA

Style Comparison

Similarity

70%

Gemini 2.5 Pro (I/O Edition) uses 578.2x more bold

OpenAI o4 Mini High

Gemini 2.5 Pro (I/O Edition)

70%Vocabulary58%

18wSentence Length16w

0.33Hedging0.38

0.0Bold5.8

1.3Lists5.0

0.02Emoji0.00

0.00Headings0.63

0.04Transitions0.01

Based on 14 + 16 text responses

vs

Ask them anything yourself

OpenAI o4 Mini High

Gemini 2.5 Pro (I/O Edition)

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

Download the free preview or get all 58 slides for $49

FAQ

Common questions

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OpenAI o4 Mini High vs Grok 3New provider

OpenAI o4 Mini High vs Llama 4 MaverickNew provider

OpenAI o4 Mini High vs Claude Opus 4New provider

OpenAI o4 Mini HighvsGemini 2.5 Pro (I/O Edition)

29 fights queued

Why OpenAI o4 Mini High?

2.1x cheaper overall$1.10/M in · $4.40/M out

Why Gemini 2.5 Pro (I/O Edition)?

Dead even. This one's a coin flip.

OpenAI o4 Mini HighGemini 2.5 Pro (I/O Edition)

Input price

$1.10/M

$1.25/M

Output price

$4.40/M

$10.00/M

Context

200K

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Released

Apr 2025

May 2025

Favorites

OpenAI o4 Mini HighGemini 2.5 Pro (I/O Edition)

Movie

The Shawshank Redemption

The Shawshank Redemption

2001

2001

Album

—

The Dark

Book

—

The Hitch Hiker's Guide to the Galaxy

The Hitch Hiker's Guide to the Galaxy

City

—

Kyoto

Game

—

CHRONO

CHRONO

12 / 29

01

Estimate Complexityreasoning

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

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025text

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

Try this prompt

Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025text

Okay, let's estimate the FLOPs for training GPT-3. I'll break this down.

Key Formula: The number of FLOPs for training a transformer-based model can be roughly estimated as: FLOPs ≈ 6 * N * D Where:

N is the number of parameters in the model.
D is the total number of tokens processed during training (dataset size in tokens * number of epochs).
The factor of 6 comes from:
- 2 * N FLOPs per token for the forward pass (each parameter is involved in roughly one multiply-accumulate, and a MAC is 2 FLOPs).
- 4 * N FLOPs per token for the backward pass (gradient computation is roughly twice as expensive as the forward pass).

Assumptions for GPT-3 (Davinci, the largest version):

Number of Parameters (N):
- GPT-3 (the largest variant, Davinci) is famously stated to have 175 billion parameters.
- So, N = 175 * 10^9.
Number of Tokens Processed (D):
- This is the trickier part to recall without searching. I remember it was trained on a massive amount of data, a significant portion of Common Crawl, books, Wikipedia, etc.
- Large Language Models are typically trained for roughly one epoch over their massive datasets, or even less if datasets are mixed and weighted. The key figure is the total number of tokens seen by the model.
- I seem to recall a figure in the range of 300 billion to 500 billion tokens for GPT-3's training. The number often cited in discussions around the GPT-3 paper and subsequent scaling law papers (like Chinchilla) is around 300 billion tokens. Let's use that as a reasonable estimate for D.
- So, D = 300 * 10^9.

Calculation:

Now, let's plug these into the formula: FLOPs ≈ 6 * N * D FLOPs ≈ 6 * (175 * 10^9) * (300 * 10^9)

Let's do the numerical part: 6 * 175 = 1050 1050 * 300 = 315,000

Now, the powers of 10: 10^9 * 10^9 = 10^18

So, combining these: FLOPs ≈ 315,000 * 10^18 FLOPs ≈ 3.15 * 10^5 * 10^18 FLOPs ≈ 3.15 * 10^(5+18) FLOPs ≈ 3.15 * 10^23

Expressing the Result:

TeraFLOPs (TFLOPs) = 10^12
PetaFLOPs (PFLOPs) = 10^15
ExaFLOPs (EFLOPs) = 10^18
ZettaFLOPs (ZFLOPs) = 10^21
YottaFLOPs (YFLOPs) = 10^24

So, 3.15 * 10^23 FLOPs can be written as:

315 * 10^21 FLOPs = 315 ZettaFLOPs
Or, as 0.315 * 10^24 FLOPs = 0.315 YottaFLOPs

The number is often quoted in PetaFLOP/s-days or similar. Let's convert 3.15 * 10^23 FLOPs to PetaFLOP-seconds: 3.15 * 10^23 / 10^15 = 3.15 * 10^8 PetaFLOP-seconds.

Number of seconds in a day = 24 hours/day * 60 minutes/hour * 60 seconds/minute = 86,400 seconds/day. So, 3.15 * 10^8 PetaFLOP-seconds / 86,400 seconds/day ≈ 3.15 * 10^8 / (8.64 * 10^4) PetaFLOP-days ≈ (3.15 / 8.64) * 10^(8-4) PetaFLOP-days ≈ 0.3646 * 10^4 PetaFLOP-days ≈ 3646 PetaFLOP/s-days (if executed on a machine capable of 1 PFLOP/s constantly for that many days, or scaled accordingly).

Reasoning Summary:

Identified the core formula: FLOPs ≈ 6 * N * D for transformer training.
Recalled model parameters (N): GPT-3 Davinci has 175 billion parameters.
Estimated training tokens (D): Based on common knowledge about LLM training and GPT-3 specifics, estimated ~300 billion tokens processed. This assumes roughly one pass over the (weighted) training data.
Performed the multiplication: 6 * (175 * 10^9) * (300 * 10^9) = 3.15 * 10^23 FLOPs.
Interpreted the result: This is approximately 315 ZettaFLOPs. This also aligns with the often-cited figure for GPT-3 training compute, which is in the range of 3000-4000 PetaFLOP/s-days (my calculation of ~3646 PetaFLOP/s-days is right in this ballpark).

Caveats:

The 6ND is an approximation. The exact number can vary slightly based on the specific transformer architecture details (e.g., use of sparse attention, specific normalization layers), optimizer overhead, and whether techniques like activation checkpointing are used (which can increase FLOPs for recomputation but save memory).
Mixed-precision training (e.g., bfloat16 or fp16) doesn't change the number of FLOPs, but it changes the type of FLOPs and allows for faster computation on hardware that supports it.
The number of training tokens D is an estimate. The GPT-3 paper describes a complex data mixture and weighting schedule, so "300 billion tokens" is an effective number of tokens processed.

Therefore, my estimate is approximately 3.15 x 10^23 FLOPs or 315 ZettaFLOPs.

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2 / 29

02

Logic Puzzlereasoning

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

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025text

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

Try this prompt

Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025text

Let's break this down:

Sally is a girl.
Sally has 3 brothers.
These 3 brothers are all part of the same family.
Each brother shares the same sisters.
If one brother has 2 sisters, those 2 sisters are Sally and one other girl.

So, Sally has 1 sister.

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Vote

3 / 29

03

Math Misconception Testreasoning

Tests an AI's understanding of number representation

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025text

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

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025text

No, 9.11 is not greater than 9.9.

Here's why:

Compare the whole number part: Both numbers have 9 as the whole number p art, so they are equal so fa r. 2. Compare the tenths place (the first digit afte r the decimal):
- 9.11 has 1 in the tenths place.
- 9.9 has 9 in the tenths place.
Since 1 is less than 9, 9.11 is less than 9.9.

You can also think of it as 9.9 being the same as 9.90. Comparing 9.11 and 9.90, it's easier to see that 11 (hundredths) is less than 90 (hundredths).

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4 / 29

04

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025svg

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

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025svg

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5 / 29

05

Xbox Controller SVG Artimage generation

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

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025svg

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

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025svg

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

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Vote

6 / 29

06

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025text

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

Try this prompt

Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025text

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

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Sponsored

7 / 29

07

Realistic AI Interviewconversation

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

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025text

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

Try this prompt

Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025text

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

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Vote

8 / 29

08

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025text

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

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025text

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

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9 / 29

09

Minimalist Landing Pageweb design

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

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025website

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025website

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10 / 29

10

Pokémon Battle UI Recreationweb design

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

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025website

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

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025website

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

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11 / 29

11

Mario Level UI Recreationweb design

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

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025website

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025website

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12 / 29

12

Linear App Cloneweb design

Tests an AI's ability to replicate an existing UI with Tailwind CSS

OpenAI o4 Mini High

OpenAI o4 Mini High

Openai · Apr 2025website

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

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Gemini 2.5 Pro (I/O Edition)

Gemini 2.5 Pro (I/O Edition)

Google · May 2025website

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

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Vote

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