Updated Mar 11, 2026

Our Verdict

Healer Alpha

Claude Sonnet 3.6 (2022-10-22)Runner-up

No community votes yet. On paper, Healer Alpha has the edge — bigger model tier, newer.

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

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

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

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Claude Sonnet 3.6 (2022-10-22)vsHealer Alpha

33 fights queued

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

Dead even. This one's a coin flip.

Why Healer Alpha?

1.3x more context262K

22 months newerMar 2026

Favorites

Claude Sonnet 3.6 (2022-10-22)Healer Alpha

Movie

The Princess Bride

The Princess Bride

Her

Her

Album

Kind of Blue

OK Computer

Book

Братья Карамазовы

Братья Карамазовы

Фёдор Михайлович Достоевский

Le petit prince

Le petit prince

Antoine de Saint-Exupéry

City

Tokyo

Kyoto

Game

Portal

Portal

Action, Puzzle4.5

Journey

Journey

Family, Indie4.3

12 / 33

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.

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Healer Alpha

Healer Alpha

Openrouter · Mar 2026text

Let me work through this step by step.

Step 1: Understanding the Model Parameters

GPT-3 has 175 billion parameters. This is the foundation for our calculation.

Step 2: Training Data Size

GPT-3 was trained on approximately 300 billion tokens from various text sources.

Step 3: The FLOPs Formula

For transformer models, a widely used approximation is: FLOPs ≈ 6 × N × D Where:

N = number of parameters
D = number of training tokens
6 accounts for:
- Forward pass: ~2 FLOPs per parameter per token (multiply-add)
- Backward pass: ~4 FLOPs per parameter per token (gradient computation)

Step 4: Calculation

Let's plug in the numbers:

N = 175 × 10⁹ parameters
D = 300 × 10⁹ tokens

FLOPs = 6 × (175 × 10⁹) × (300 × 10⁹) FLOPs = 6 × 175 × 300 × 10¹⁸ FLOPs = 6 × 52,500 × 10¹⁸ FLOPs = 315,000 × 10¹⁸ FLOPs = 3.15 × 10²³

Step 5: Context and Refinements

A few important considerations:

This is a lower bound estimate. The actual number is likely higher due to:
- Attention mechanism computations
- Layer normalization
- Embedding lookups
- Optimizer states (Adam optimizer uses additional memory and compute)
Batch size effects: The formula assumes optimal batching. In practice, with smaller batch sizes, there might be some overhead.
Memory bandwidth: Actual hardware utilization is never 100%, so real-world compute requirements might be 1.5-2× higher.

Step 6: Comparison with Known Estimates

This aligns with:

OpenAI's original estimates (~3.14 × 10²³ FLOPs)
Other research papers citing similar magnitudes
The Chinchilla scaling laws which suggest optimal training requires ~20 tokens per parameter (3.5 × 10¹² tokens for 175B parameters, which would be ~4.2 × 10²³ FLOPs)

Final Estimate

Approximately 3 × 10²³ FLOPs (or 300 zettaFLOPs).

To put this in perspective:

This is equivalent to about 10,000 years of computation on a single high-end GPU (like an A100)
Or roughly 1,000 years on a cluster of 100 A100 GPUs running continuously
This explains why training such models requires massive computational resources and specialized infrastructure

The calculation shows why large language model training is both computationally intensive and expensive, requiring careful optimization of both algorithms and hardware.

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2 / 33

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

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

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Healer Alpha

Healer Alpha

Openrouter · Mar 2026svg

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

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3 / 33

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

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

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Healer Alpha

Healer Alpha

Openrouter · Mar 2026text

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

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4 / 33

04

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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Healer Alpha

Healer Alpha

Openrouter · Mar 2026text

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

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5 / 33

05

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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Healer Alpha

Healer Alpha

Openrouter · Mar 2026svg

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

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6 / 33

06

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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Healer Alpha

Healer Alpha

Openrouter · Mar 2026svg

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

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7 / 33

07

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.

Try this prompt

Healer Alpha

Healer Alpha

Openrouter · Mar 2026text

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

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8 / 33

08

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.

Try this prompt

Healer Alpha

Healer Alpha

Openrouter · Mar 2026text

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

Try this prompt

Vote

Which one handles your task better?

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

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9 / 33

09

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.

Try this prompt

Healer Alpha

Healer Alpha

Openrouter · Mar 2026text

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

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10 / 33

10

Pokémon Battle UI Recreationweb design

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024website

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

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Healer Alpha

Healer Alpha

Openrouter · Mar 2026website

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

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11 / 33

11

Mario Level UI Recreationweb design

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024website

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Healer Alpha

Healer Alpha

Openrouter · Mar 2026website

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12 / 33

12

Linear App Cloneweb design

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

Claude Sonnet 3.6 (2022-10-22)

Claude Sonnet 3.6 (2022-10-22)

Anthropic · Jun 2024website

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

Try this prompt

Healer Alpha

Healer Alpha

Openrouter · Mar 2026website

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

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