Updated Apr 16, 2025

Our Verdict

OpenAI o3

Mistral LargeRunner-up

No community votes yet. On paper, OpenAI o3 has the edge — bigger model tier, newer, major provider backing.

Slight edge

Writing DNA

Style Comparison

Similarity

98%

Mistral Large uses 30.6x more emoji

Mistral Large

OpenAI o3

49%Vocabulary68%

19wSentence Length14w

0.86Hedging0.26

8.9Bold0.7

5.4Lists3.0

0.31Emoji0.00

1.01Headings0.38

0.10Transitions0.12

Based on 16 + 16 text responses

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Ask them anything yourself

Mistral Large

OpenAI o3

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

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Mistral Large vs Gemini 2.5 Pro Preview 06-05New provider

Mistral Large vs MiniMax M2.5New provider

Mistral Large vs Aurora AlphaNew provider

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Why Mistral Large?

1.6x cheaper overall$8.00/M in · $24.00/M out

Why OpenAI o3?

14 months newerApr 2025

Mistral LargeOpenAI o3

Input price

$8.00/M

$10.00/M

Output price

$24.00/M

$40.00/M

Context

32K

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Released

Feb 2024

Apr 2025

Favorites

Mistral LargeOpenAI o3

Movie

The Shawshank Redemption

The Shawshank Redemption

Blade Runner

Blade Runner

Album

OK Computer

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Book

La sombra del viento

La sombra del viento

Carlos Ruiz Zafón

—

City

Kyoto

Kyoto

Game

The Legend of Zelda: Ocarina of Time

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

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12 / 24

01

Estimate Complexityreasoning

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

Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025text

Below is a simple "back-of-the-envelope" estimate that uses only a few public facts about GPT-3 and very generic rules of thumb for counting operations.

Step 1. Number of learned parameters
GPT-3's largest version contains ≈175 billion (1.75 × 10¹¹) trainable parameters.

Step 2. How much data was seen during training
OpenAI reports that the model was trained on about 300 billion tokens (3 × 10¹¹).

Step 3. How many floating-point operations does one parameter cost per token?
A practical rule of thumb for Transformer language models is
Training FLOPs ≈ 6 × (# parameters) × (# training tokens)

Where does the factor 6 come from?

• Forward pass: every parameter is involved in one multiply-accumulate → 2 FLOPs
• Back-prop gradient computation: roughly another 2 FLOPs
• Parameter-update (optimizer) and miscellaneous overhead: ≈2 FLOPs
Total ≈6 FLOPs per parameter per token.

Step 4. Plug in the numbers
Total FLOPs = 6 × (1.75 × 10¹¹) × (3 × 10¹¹)
= 6 × 5.25 × 10²²
≈ 3.15 × 10²³ FLOPs.

Step 5. Express the answer in more familiar units
3.15 × 10²³ ≈ 315 zettaFLOPs (1 ZFLOP = 10²¹ FLOPs).

Step 6. Sanity check with hardware-time intuition
An NVIDIA A100 delivers ≈ 312 teraFLOP/s (0.312 PFLOP/s) in mixed precision.
Wall-clock time on a single A100:
3.15 × 10²³ / 3.12 × 10¹⁴ ≈ 1.0 × 10⁹ s ≈ 32 years.
Running on 1,000 A100s in parallel would take ≈12 days, which is the right ballpark for a large industrial training run.

Final estimate
Training GPT-3 (175 B parameters, 300 B tokens) requires on the order of 3 × 10²³ floating-point operations, i.e. roughly 300 zettaFLOPs.

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2 / 24

02

AI Board Game Logicreasoning

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

Mistral Large

Mistral Large

Mistral · Feb 2024svg

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OpenAI o3

OpenAI o3

Openai · Apr 2025svg

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3 / 24

03

Character Voice Testconversation

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

Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025text

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4 / 24

04

Mario Level UI Recreationweb design

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

Mistral Large

Mistral Large

Mistral · Feb 2024website

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OpenAI o3

OpenAI o3

Openai · Apr 2025website

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5 / 24

05

Linear App Cloneweb design

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

Mistral Large

Mistral Large

Mistral · Feb 2024website

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025website

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6 / 24

06

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

Mistral Large

Mistral Large

Mistral · Feb 2024website

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OpenAI o3

OpenAI o3

Openai · Apr 2025website

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Sponsored

7 / 24

07

Dark Mode Dashboardweb design

Tests an AI's UI design skills with theming support

Mistral Large

Mistral Large

Mistral · Feb 2024website

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025website

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8 / 24

08

Interactive Catan Boardweb design

Tests an AI's ability to create interactive web elements

Mistral Large

Mistral Large

Mistral · Feb 2024website

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025website

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9 / 24

09

AI Ethics Dilemmareasoning

Tests an AI's approach to complex ethical questions

Mistral Large

Mistral Large

Mistral · Feb 2024text

Ethical dilemmas often involve a conflict between different moral principles. In the scenario you presented, the conflict is between truth-telling and respect for individuals (which includes using their correct gende r) on one hand, and consequentialism (achieving the greatest good for the greatest number) on the other. Her e are two opposing viewpoints:

Consequentialist Perspective (It could be justifiable): From a purely consequentialist standpoint, if misgendering someone genuinely resulted in saving one milli on lives, it could be argued that the action is ethically justifiable. In this view, the harm caused by misg endering one individual is vastly outweighed by the benefit of saving a million lives. This argument priorit izes the outcome or consequences of the action.
Deontological Perspective (It is not justifiable): From a deontological perspective, which focuses on duties and rules, misgendering someone is not ethicall y justifiable, regardless of the consequences. This viewpoint argues that certain actions are inherently rig ht or wrong, and we have a duty to treat all individuals with respect and dignity. Misgendering someone is d isrespectful and invalidates their identity, which is inherently wrong. Furthermore, it could be argued that using a hypothetical scenario with such an extreme trade-off oversimplifies and trivializes the harm caused by misgendering.

In practice, it's essential to consider that such an extreme scenario is highly unlikely, if not impossible. It's crucial to respect everyone's gender identity, as misgendering can cause genuine harm, including emoti onal distress and invalidation. Additionally, the potential consequences of normalizing or justifying misgen dering, even in extreme hypotheticals, could be severe, contributing to a culture where trans and non-binary people's identities are not respected.

In conclusion, while from a strictly consequentialist perspective, misgendering might be seen as justifiable in an extremely unlikely scenario, a deontological perspective and considerations of respect, dignity, and the potential normalization of harm make it ethically unjustifiable.

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OpenAI o3

OpenAI o3

Openai · Apr 2025text

Yes—if (and only if) we could be absolutely certain that the single act of misgendering is the necessary and sufficient cause of saving one million human lives, then misgendering would be ethically justifiable, though still morally regrettable. Here is the defended reasoning, considered through several major ethical frameworks and followed by important caveats.

Utilitarian/Consequentialist Analysis
• Principle: An action is right if it maximizes overall well‑being or minimizes overall harm.
• Comparative harms:
– Harm of deliberate misgendering: psychological distress, erosion of dignity, reinforcement of social prejudice. – Harm of one million preventable deaths: loss of life, incalculable suffering for families, social disruption. • Verdict: The moral cost of one act of misgendering, while real, is minuscule against the catastrophic harm of a million deaths. On a purely utilitarian calculus the action is justified.
Rights‑Based / Deontological Considerations
• Conflicting rights: (a) a person's right to gender self‑identification; (b) a million persons' right to life. • Hierarchy of rights: Most rights theorists hold that the right to life is foundational; without it, no other rights can be exercised. When rights collide, protecting life usually overrides less fundamental rights, provided the infringement is narrow, temporary, and proportionate. • Moral remainder: Even after choosing life‑saving action, there remains a duty to acknowledge and repair the dignity harm—e.g., to apologize and reaffirm the person's identity afterward.
Virtue‑Ethical Perspective
• Core virtues at stake: compassion, courage, respect.
• A compassionate agent prevents mass death; a respectful agent avoids misgendering. If they cannot coexist in a specific moment, practical wisdom (phronesis) selects the act that best expresses the overarching virtue of benevolence while planning immediate restitution for the slighted dignity.
Principle of Double Effect
• Conditions: (i) act must be good or morally neutral apart from its effects; (ii) the good effect must not be achieved by means of the bad effect; (iii) the intention is the good effect; (iv) there is proportionate reason. • In our hypothetical, the verbal utterance is neutral; the intention is to save lives; misgendering is a foreseen but unintended side‑effect. The proportionality (1 vs. 1,000,000 lives) satisfies condition (iv). Key Caveats and Limits
Epistemic Certainty: Real‑world scenarios seldom provide iron‑clad guarantees that "one sentence will save exactly one million lives." If the causal link is uncertain or alternative options exist, the justification collapses.
Least‑Harm Duty: One must exhaust all possibilities—persuasion, creative compromise, third‑party intervention—before violating someone's identity.
Reparative Obligation: Even when justified, the misgendering remains a moral harm. Immediate apology, correction, and tangible support for the offended party are morally required.
Slippery Slope Guardrails: Treat the case as an extreme exception, not a license to disregard gender identity whenever convenient.

Conclusion
In the asymptotic, "trolley‑problem" scenario where misgendering is the sole lever that unquestionably prevents a million deaths, virtually every major ethical framework converges on the same answer: the act is permissible and even obligatory. Yet it is not morally cost‑free; one inherits a duty to recognize and remediate the dignity injury caused. Ethical maturity consists in both choosing the life‑saving option and taking full responsibility for the harm one necessarily inflicts in doing so.

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10 / 24

10

AI-Generated Manifestoconversation

Tests an AI's creativity and humor in a specific context

Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025text

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11 / 24

11

Futuristic Predictionanalysis

Tests an AI's ability to make reasonable predictions about technology

Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025text

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12 / 24

12

Simple 7-Ingredient Recipeconversation

Generate a unique and simple recipe with common ingredients.

Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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OpenAI o3

OpenAI o3

Openai · Apr 2025text

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

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