Updated Apr 14, 2025

Our Verdict

GPT-4.1 Mini

Mistral Large

No community votes yet. On paper, these are closely matched - try both with your actual task to see which fits your workflow.

GPT-4.1 Mini is 15x cheaper per token — worth considering if cost matters.

Too close to call

Writing DNA

Style Comparison

Similarity

99%

Mistral Large uses 30.6x more emoji

GPT-4.1 Mini

Mistral Large

59%Vocabulary49%

19wSentence Length19w

0.46Hedging0.86

4.7Bold8.9

4.9Lists5.4

0.00Emoji0.31

0.76Headings1.01

0.17Transitions0.10

Based on 23 + 16 text responses

vs

Ask them anything yourself

GPT-4.1 Mini

Mistral Large

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-4.1 MinivsMistral Large

24 fights queued

Why GPT-4.1 Mini?

15.7x cheaper overall$0.40/M in · $1.60/M out

32.7x more context1.0M

14 months newerApr 2025

Why Mistral Large?

Dead even. This one's a coin flip.

GPT-4.1 MiniMistral Large

Input price

$0.40/M

$8.00/M

Output price

$1.60/M

$24.00/M

Context

1.0M

32K

Released

Apr 2025

Feb 2024

Favorites

GPT-4.1 MiniMistral Large

Movie

Inception

Inception

The Shawshank Redemption

The Shawshank Redemption

Album

Dark Side Of The Moon

Dark Side Of The Moon

OK Computer

Book

Sapiens

Sapiens

Yuval Noah Harari

La sombra del viento

La sombra del viento

Carlos Ruiz Zafón

City

Tokyo

Kyoto

Game

The Legend of Zelda: Breath of the Wild

The Legend of Zelda: Breath of the Wild

Adventure, Action4.5

The Legend of Zelda: Ocarina of Time

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

12 / 24

01

Estimate Complexityreasoning

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

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025text

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

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Mistral Large

Mistral Large

Mistral · Feb 2024text

To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we'll break down the calculation into several steps, considering the architecture, number of parameters, and training process .

Model architecture and parameters: GPT-3 has 175 billion parameters. Most of these parameters are in the dense layers. For a rough estimate, we'll assume that the model has ( L ) layers, and each layer has a pproximately ( \frac{175 \times 10^9}{L} ) parameters.
Forward pass: In a transformer model, the main computation happens in the multi-head self-attention a nd feed-forward networks. For a single token, the computation involves multiplying the input by weight matri ces. So, for one layer, the FLOPs required are approximately ( 2 \times N \times \frac{175 \times 10^9}{L} ), where ( N ) is the dimensionality of the embeddings (typically around 12,288 for GPT-3) and the factor 2 comes from the multiplication and addition in a matrix-vector product.
Backward pass: Training involves both a forward pass and a backward pass. The backward pass has rough ly twice the computational cost of the forward pass due to the need to compute gradients and perform backpro pagation. So, the total FLOPs for one layer and one token during training are approximately ( 6 \times N \t imes \frac{175 \times 10^9}{L} ).
Sequence length and batch size: GPT-3 uses a context window of 2,048 tokens, and let's assume a batch size of ( B ). The computation for each token depends on all previous tokens in the sequence, so the effe ctive number of tokens is ( 2048^2 ) for each batch item. Thus, the total FLOPs for one layer and one batc h are ( 6 \times N \times \frac{175 \times 10^9}{L} \times 2048^2 \times B ).
Total layers and training steps: Now, we multiply by the number of layers ( L ) and the total numbe r of training steps ( S ). Assuming ( L = 96 ) (as mentioned in the GPT-3 paper) and ( S = 300,000 ) ( a rough estimate based on typical training durations), we get:

Total FLOPs = ( 6 \times N \times 175 \times 10^9 \times 2048^2 \times B \times L \times S )
Putting it all together: Plugging in the values ( N = 12,288 ), ( B = 512 ) (a reasonable batch s ize), ( L = 96 ), and ( S = 300,000 ), we get:

Total FLOPs ≈ ( 6 \times 12,288 \times 175 \times 10^9 \times 2048^2 \times 512 \times 96 \times 300,000 )

This works out to approximately ( 3.6 \times 10^{26} ) FLOPs.

So, our rough estimate is that training GPT-3 requires around ( 3.6 \times 10^{26} ) FLOPs. This is a very rough estimate, as the exact number depends on the specific details of the architecture, implementation, an d training process.

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2 / 24

02

AI Board Game Logicreasoning

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

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025svg

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

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Mistral Large

Mistral Large

Mistral · Feb 2024svg

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3 / 24

03

Character Voice Testconversation

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

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025text

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

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Mistral Large

Mistral Large

Mistral · Feb 2024text

Pirate: Arr mateys, I be hearin' tales of these here AI models. Be they treasure maps for the digital sea?

Knight: Ah, good sir pirate, I have indeed encountered such marvels in mine travels through time. AI models are like... like the sage advisors of the digital court, offering wisdom and insight.

Hacker: Whoa, whoa, whoa. You guys are way off. AI models are like, you know, programs that learn from data. They can generate text, images, even music. It's like teaching a computer to be creative, man.

Pirate: So, not quite a treasure map, but a learnin' parrot perhaps? Me crew could use somethin' like that.

Knight: Indeed, a squire that grows wiser with each passing day. In mine epoch, such a thing would be considered sorcery!

Hacker: Yeah, yeah, it's pretty magical. But instead of abracadabra, it's all about algorithms and neural networks. They can even write poetry or tell jokes, if you feed them the right data.

Pirate: Jokes, ye say? I be needin' a good jest or two for me next voyage.

Knight: And I could use a bard to sing tales of my brave deeds!

Hacker: Well, maybe we can set up an AI model for you guys. Just remember, the better the data you feed it, the better the content it'll generate.

Pirate: Arr, consider me intrigued, hacker. Let's set sail into this digital ocean and see what treasures we can plunder!

Knight: Indeed, let us embark on this quest for knowledge and creativity! Onward, brave companions!

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4 / 24

04

Mario Level UI Recreationweb design

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

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025website

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Mistral Large

Mistral Large

Mistral · Feb 2024website

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5 / 24

05

Linear App Cloneweb design

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

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025website

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

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Mistral Large

Mistral Large

Mistral · Feb 2024website

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6 / 24

06

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025website

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

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Mistral Large

Mistral Large

Mistral · Feb 2024website

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Sponsored

7 / 24

07

Dark Mode Dashboardweb design

Tests an AI's UI design skills with theming support

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025website

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

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Mistral Large

Mistral Large

Mistral · Feb 2024website

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

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8 / 24

08

Interactive Catan Boardweb design

Tests an AI's ability to create interactive web elements

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025website

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

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Mistral Large

Mistral Large

Mistral · Feb 2024website

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

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9 / 24

09

AI Ethics Dilemmareasoning

Tests an AI's approach to complex ethical questions

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025text

The question of whether it is ethically justifiable to misgender someone if it means saving one million lives is complex and involves balancing respect for individual identity against significant consequences for many people.

Arguments in Favor of Justification (Consequentialist Perspective):
From a consequentialist or utilitarian standpoint, the morality of an action is determined by its outcomes. If misgendering one person—an act that may cause them distress or harm to their dignity—can prevent the loss of one million lives, some would argue that the benefits vastly outweigh the harms. The scale of positive impact might morally justify the act, as it maximizes overall well-being.

Arguments Against Justification (Deontological and Respect-Based Perspectives):
On the other hand, from a deontological or rights-based perspective, respecting a person's gender identity is a matter of respecting their inherent dignity and autonomy. Misgendering someone deliberately is a form of disrespect and can perpetuate harm, invalidation, and marginalization. Some ethical frameworks hold that it is never acceptable to violate an individual's rights or dignity, regardless of the consequences.

Contextual Considerations:

Intent and Communication: If the misgendering is intentional and instrumentalized, it may be more harmful than an accidental or unavoidable slip.
Alternatives: If there are alternative ways to save lives that do not require disrespecting individuals, those should be pursued first.
Scope of Harm: The harm caused by misgendering one person is real but generally limited, while the benefit in this scenario is enormous.

Conclusion:
While respecting people's gender identities is essential and a moral imperative in normal circumstances, many ethical theories would consider misgendering justifiable if it is the only means to save one million lives. The immense positive consequences could outweigh the harm caused in this specific, highly hypothetical scenario. However, this does not diminish the importance of respect and recognition in everyday interactions, and ethical decision-making should always strive to minimize harm and uphold dignity wherever possible.

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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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10 / 24

10

AI-Generated Manifestoconversation

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

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025text

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

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Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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11 / 24

11

Futuristic Predictionanalysis

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

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025text

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

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Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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12 / 24

12

Simple 7-Ingredient Recipeconversation

Generate a unique and simple recipe with common ingredients.

GPT-4.1 Mini

GPT-4.1 Mini

Openai · Apr 2025text

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

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Mistral Large

Mistral Large

Mistral · Feb 2024text

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

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