Updated Nov 13, 2025

Our Verdict

GPT-5.1-Codex-Mini

Llama 4 ScoutRunner-up

No community votes yet. On paper, GPT-5.1-Codex-Mini has the edge — bigger model tier, newer.

Llama 4 Scout is 12x cheaper per token — worth considering if cost matters.

Too close to call

Writing DNA

Style Comparison

Similarity

98%

Llama 4 Scout uses 3.9x more headings

GPT-5.1-Codex-Mini

Llama 4 Scout

65%Vocabulary43%

20wSentence Length42w

0.56Hedging0.81

3.3Bold4.1

3.4Lists7.3

0.00Emoji0.00

0.08Headings0.30

0.05Transitions0.04

Based on 13 + 10 text responses

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Ask them anything yourself

GPT-5.1-Codex-Mini

Llama 4 Scout

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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GPT-5.1-Codex-Mini vs Llama 4 MaverickCross-provider

GPT-5.1-Codex-Mini vs Grok 3New provider

GPT-5.1-Codex-Mini vs Gemini 2.5 Pro (I/O Edition)New provider

GPT-5.1-Codex-MinivsLlama 4 Scout

49 fights queued

Why GPT-5.1-Codex-Mini?

7 months newerNov 2025

Why Llama 4 Scout?

10.5x cheaper overall$0.25/M in · $0.50/M out

25x more context10.0M

GPT-5.1-Codex-MiniLlama 4 Scout

Input price

$1.50/M

$0.25/M

Output price

$6.00/M

$0.50/M

Context

400K

10.0M

Released

Nov 2025

Apr 2025

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Blade Runner 2049

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The Legend of Zelda: Ocarina of Time

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Adventure, Action4.4

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Action, Puzzle4.5

12 / 49

01

Estimate Complexityreasoning

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025text

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Llama 4 Scout

Llama 4 Scout

Meta · Apr 2025text

To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 without searching online, we'll need to make some educated guesses and use general knowledge about the model and deep learning computations. GPT-3 is a transformer-based language model developed by OpenAI, and its training details have been publicly discussed, but we will not rely on specific online numbers for this estimation.

1. Understanding GPT-3

Model Size and Architecture: GPT-3 has 175 billion parameters. It's based on the transformer architecture, which is primarily composed of self-attention and feed-forward neural network layers.

2. Estimating FLOPs for Training

The training of a neural network involves a series of forward and backward passes through the network. For a single pass (either forward or backward), the number of FLOPs can be roughly estimated based on the model's architecture and size.

Parameter Count: With 175 billion parameters, most operations will involve these parameters.

3. FLOPs for a Forward Pass

A rough estimate for the FLOPs in a forward pass for a transformer model can be derived from its main components:

Self-Attention: This involves matrix multiplications and divisions/additions, but matrix multiplications dominate the cost. For a layer with (n) inputs (or sequence length) and (d) as the embedding dimension, the self-attention mechanism's computational cost can be approximated. However, for simplicity, let's focus on the overall model.
Feed-Forward Networks (FFNNs): These are also matrix multiplications.

Given that matrix multiplication of two matrices of size (m \times n) and (n \times p) requires (2mn p) FLOPs (additions and multiplications), and assuming that most of the model's parameters are involved in these operations, we can approximate.

4. Backward Pass

The backward pass, which is used for training, involves computing gradients, which roughly doubles the FLOPs count compared to the forward pass because for each multiplication in the forward pass, there are several operations (including another multiplication and addition) in the backward pass.

5. Training Iterations

GPT-3 was trained on a massive dataset and took a considerable amount of time. The model reportedly used 45 terabytes of text data. Assuming a batch size (the number of data samples used for a single forward/backward pass) and knowing that training took approximately 100-150 days on 8 A100 GPUs (or their equivalent), we can make an educated guess.

6. Making an Estimate

Assumptions:
- Average operations per parameter per forward pass: Let's assume 10-20 FLOPs/param (very rough, includes both forward and attention mechanisms simplistically).
- Model Size: 175 billion parameters.
- Training Time: Approximately 100-150 days.
- Batch Size: Speculative, but let's assume around 1 million tokens/batch (a token can be a word or part of a word).
Rough Calculation:
- Operations per Forward/Backward Pass: With 175 billion parameters and 10-20 FLOPs/param, that's (175 \times 10^{9} \times 15) FLOPs/pass (averaging) = (2.625 \times 10^{12}) FLOPs/pass.
- Assuming 2 passes (forward and backward), that's roughly (5.25 \times 10^{12}) FLOPs/iteration.

Given the complexity and speculative nature, let's aim for a much simpler, order-of-magnitude estimate:

If GPT-3 took about 100 days to train, and assuming 1 million batches/day (which is speculative), and with 2 FLOPs/param for both forward and backward pass (very rough), we'd estimate:

(175 \times 10^9 \text{ params} \times 4 \text{ FLOPs/param} \times 1,000,000 \text{ batches/day} \times 100 \text{ days})

(= 7 \times 10^{22}) FLOPs.

Conclusion

Given these very rough and speculative assumptions, an order-of-magnitude estimate for training GPT-3 might be around (10^{22}) to (10^{23}) FLOPs. For comparison, some reports suggest numbers in the order of (10^{24}) FLOPs for training GPT-3, indicating that our simplified calculation underestimates, likely due to oversimplification of operations per pass and batch sizes.

Keep in mind, this is a very rough estimate and actual numbers can vary significantly based on specifics like hardware optimizations, software stack efficiency, data parallelism, and more.

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2 / 49

02

AI Board Game Logicreasoning

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025svg

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

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Meta · Apr 2025svg

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3 / 49

03

Logic Puzzlereasoning

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025text

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

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Llama 4 Scout

Llama 4 Scout

Meta · Apr 2025text

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4 / 49

04

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025text

Why don't scientists trust atoms? Because they make up everything!
I told my computer I needed a break, and it said: "You seem stressed. Have a byte!"
Parallel lines have so much in common — it’s a shame they’ll never meet.
Why did the scarecrow win an award? Because he was outstanding in his field!
I used to play piano by ear, but now I use my hands — the neighbors appreciate the quiet!

All 5 jokes are unique.

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Llama 4 Scout

Llama 4 Scout

Meta · Apr 2025text

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5 / 49

05

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025svg

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Meta · Apr 2025svg

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6 / 49

06

Xbox Controller SVG Artimage generation

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025svg

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7 / 49

07

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025text

[Lights up. Comedian walks center stage, smiles.]

“Hey! Great to be here. You ever notice how everyone’s life hack is now ‘just drink more water’? Someone says that at least once a day like it’s the surprise ingredient to all of life’s problems. ‘Oh, you’re tired?’ ‘Drink water!’ ‘Oh, you’re sad?’ ‘Drink water!’ I tried that on a date once. I’m like, ‘You’re not laughing. Want water?’ She said, ‘You’re not funny.’ I said, ‘Hydrate.’

Speaking of life hacks, have you ever downloaded a productivity app just to check your productivity apps? It’s like, I spend more time organizing the thing that’s supposed to keep me organized. I open the app, spend ten minutes setting the perfect color-coded schedule, then realize I’m supposed to be working, so I close it. Now my brain is stuck in the loading screen of ‘I’ll do it tomorrow... but first, let me sync it with my wellness app.’

Why is it that the moment you buy a new appliance, it becomes a museum piece? You get a blender with a million buttons — smoothies, soup, salsa, self-esteem. You use it once, put it in the cupboard, and suddenly you’re in denial. Friends come over, and you’re like, ‘That blender? It’s not for blending. It’s for letting the kitchen know you’re trying.’ It’s like a motivational poster in stainless steel.

And grocery stores — why are they all arranged like you’re on a scavenger hunt for salad? Milk is in the back like it’s playing hide-and-seek with lactose intolerant people. You have to walk past the bakery, the candy, the olives — because clearly, the only way to pick up almond milk is to triple-check you’re committed to your new lifestyle. The cashier scans your oats and almond milk, and you whisper, ‘It’s for a smoothie. No, I’m not depressed.’

Flight travel — I love it. Nothing like boarding a plane where everyone is on the same mission: avoid eye contact with the person in the aisle seat who clearly brought three carry-ons and zero self-awareness. The flight attendant is like, ‘Please place your carry-on in the overhead compartment.’ You’re like, ‘I can’t even place my life in there right now.’ They always say, ‘For everyone’s safety, make sure the aisle is clear.’ The aisle is about as clear as my schedule — which is also, ironically, empty.

Technology keeps telling us we have connectivity issues, but the minute something goes wrong, the customer service person is like, ‘Have you tried turning it off and on again?’ That’s their whole job. I’m paying you to reboot my wifi like I’m negotiating with a temperamental teenager. I’m like, ‘No, listen, I unplugged it, I sang it a lullaby, I begged it not to drop my Zoom call. What else do you want?’

But I don’t mind. Honestly, the best part of adulthood is realizing you’re still making it up as you go — just with smaller toys and more insurance policies. I mean, my version of wild now is ordering a fancy cheese plate and pretending it’s a celebration. I’m just like, ‘Congratulations, you made it to Thursday. Here’s brie.’

Thanks, you’ve been awesome! I’ll be around; I might just try that water thing again… later.”

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Llama 4 Scout

Llama 4 Scout

Meta · Apr 2025text

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

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8 / 49

08

Realistic AI Interviewconversation

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025text

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

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Meta · Apr 2025text

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9 / 49

09

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025text

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

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Llama 4 Scout

Llama 4 Scout

Meta · Apr 2025text

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

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10 / 49

10

Character Voice Testconversation

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025text

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

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Llama 4 Scout

Llama 4 Scout

Meta · Apr 2025text

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

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11 / 49

11

Minimalist Landing Pageweb design

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025website

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Meta · Apr 2025website

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12 / 49

12

Pokémon Battle UI Recreationweb design

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

GPT-5.1-Codex-Mini

GPT-5.1-Codex-Mini

Openai · Nov 2025website

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Meta · Apr 2025website

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