Updated Oct 31, 2025

Our Verdict

Claude Sonnet 3.6 (2022-10-22)

Nova Premier 1.0

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Claude Sonnet 3.6 (2022-10-22)

Nova Premier 1.0

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Claude Sonnet 3.6 (2022-10-22) vs Llama 4 MaverickNew provider

Claude Sonnet 3.6 (2022-10-22) vs GPT-5New provider

Claude Sonnet 3.6 (2022-10-22) vs MiniMax M2.5New provider

Claude Sonnet 3.6 (2022-10-22)vsNova Premier 1.0

36 fights queued

Why Claude Sonnet 3.6 (2022-10-22)?

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified49.0% vs 42.4%

Why Nova Premier 1.0?

1.2x cheaper overall$2.50/M in · $12.50/M out

5x more context1.0M

17 months newerOct 2025

Claude Sonnet 3.6 (2022-10-22)Nova Premier 1.0

Input price

$3.00/M

$2.50/M

Output price

$15.00/M

$12.50/M

Context

200K

1.0M

Released

Jun 2024

Oct 2025

Benchmarks1 common

SWE-bench Verified

49.0%+13.5%

42.4%

Favorites

Claude Sonnet 3.6 (2022-10-22)Nova Premier 1.0

Movie

The Princess Bride

The Princess Bride

The Shawshank Redemption

The Shawshank Redemption

Album

Kind of Blue

The Dark Side of the Moon

The Dark Side of the Moon

Book

Братья Карамазовы

Братья Карамазовы

Фёдор Михайлович Достоевский

To Kill a Mockingbird

To Kill a Mockingbird

City

Tokyo

Tokyo

Game

Portal

Portal

Action, Puzzle4.5

The Legend of Zelda: Breath of the Wild

The Legend of Zelda: Breath of the Wild

Adventure, Action4.5

12 / 36

01

Estimate Complexityreasoning

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

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

Try this prompt

Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

Estimating the total number of FLOPs (floating point operations) required to train a model like GPT-3 involves considering several key factors: model architecture, number of parameters, amount of training data, and number of training iterations. Here's a step-by-step approach to derive an estimate:

Model Architecture Understanding:
- GPT-3 is a transformer-based model. The transformer architecture primarily involves self-attention mechanisms and feed-forward neural networks.
- Each transformer layer consists of multi-head self-attention and position-wise feed-forward networks.
Number of Parameters:
- GPT-3 has 175 billion parameters.
Training Data:
- The model was trained on roughly 300 billion tokens (words or subwords).
Number of Training Iterations:
- To estimate the number of training iterations, we need to make some assumptions. Let's assume the model was trained for around 3 epochs over the entire dataset.
FLOPs per Token per Layer:
- Calculating FLOPs for transformers is complex, but we can approximate.
- For self-attention: The complexity is approximately ( O(n^2 \cdot d) ) per layer for sequence length ( n ) and model dimension ( d ).
- For feed-forward layers: The complexity is approximately ( O(n \cdot d^2) ).
Given that GPT-3 uses a context window (sequence length) of around 1024 (n = 1024) and a model dimension (d) of approximately 12,288 (derived from 175B parameters and architecture details), we can estimate the FLOPs.

Let's simplify by combining these complexities. An estimate often used in literature is that each token processed through one transformer layer requires about ( 2 \cdot 10^4 ) FLOPs.
Total FLOPs Calculation:
- Number of layers in GPT-3: Let's assume 96 layers (common for large transformers).
- Number of tokens processed: ( 300 , \text{billion tokens} \times 3 , \text{epochs} = 900 , \text{billion tokens} ).
So, approximately: [ \text{FLOPs per token per layer} = 2 \times 10^4 ]

[ \text{FLOPs per token for the whole model} = 2 \times 10^4 \times 96 = 1.92 \times 10^6 , \text{FLOPs} ]

[ \text{Total FLOPs} = 1.92 \times 10^6 , \text{FLOPs/token} \times 900 \times 10^9 , \text{tokens} = 1.728 \times 10^{18} , \text{FLOPs} ]

Thus, the estimated total number of FLOPs required to train GPT-3 is on the order of ( 1.7 \times 10^{18} ) FLOPs, or 1.7 exaFLOPs.

This is a rough estimation and the actual number can vary based on implementation details and optimizations. However, it provides a reasonable approximation based on known architectural details and typical scaling laws for transformer models.

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2 / 36

02

AI Board Game Logicreasoning

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024svg

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025svg

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

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3 / 36

03

Logic Puzzlereasoning

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

Let me help solve this word problem step by step.

Let's break it down:

Sally is a girl
Sally has 3 brothers
Each brother has 2 sisters

The key insight here is that if each brother has 2 sisters, and we know Sally is one of those sisters, then all brothers must share the same sisters.

Since each brother has 2 sisters total, and we know Sally is one of them, there must be one other sister.

Therefore, Sally has 1 sister.

To verify:

Sally + her 1 sister = 2 sisters total
This matches the fact that each of the 3 brothers has 2 sisters

The answer is 1 sister.

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

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

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4 / 36

04

Math Misconception Testreasoning

Tests an AI's understanding of number representation

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

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

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

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

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5 / 36

05

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

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

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

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

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6 / 36

06

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024svg

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

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025svg

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

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7 / 36

07

Xbox Controller SVG Artimage generation

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024svg

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

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025svg

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

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8 / 36

08

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

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

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

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

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9 / 36

09

Realistic AI Interviewconversation

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

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

Try this prompt

Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

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

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10 / 36

10

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

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

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

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

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11 / 36

11

Character Voice Testconversation

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024text

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

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025text

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

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12 / 36

12

Minimalist Landing Pageweb design

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024website

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Nova Premier 1.0

Nova Premier 1.0

Amazon · Oct 2025website

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