Updated Feb 25, 2025

Our Verdict

Claude 3.7 Sonnet

Mistral LargeRunner-up

No community votes yet. On paper, Claude 3.7 Sonnet has the edge — newer, bigger context window, major provider backing.

Too close to call

Writing DNA

Style Comparison

Similarity

99%

Mistral Large uses 30.6x more emoji

Claude 3.7 Sonnet

Mistral Large

62%Vocabulary49%

35wSentence Length19w

0.99Hedging0.86

1.2Bold8.9

4.3Lists5.4

0.00Emoji0.31

1.78Headings1.01

0.23Transitions0.10

Based on 13 + 16 text responses

vs

Ask them anything yourself

Claude 3.7 Sonnet

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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Mistral Large vs GPT-4o (Omni)New provider

Mistral Large vs Llama 4 MaverickNew provider

Claude 3.7 Sonnet vs MiniMax M2.5New provider

Claude 3.7 SonnetvsMistral Large

24 fights queued

Why Claude 3.7 Sonnet?

1.7x cheaper overall$3.00/M in · $15.00/M out

6.3x more context200K

12 months newerFeb 2025

Why Mistral Large?

Leads 1 of 1 benchmarks

Claude 3.7 SonnetMistral Large

Input price

$3.00/M

$8.00/M

Output price

$15.00/M

$24.00/M

Context

200K

32K

Released

Feb 2025

Feb 2024

Benchmarks1 common

80.3%

81.2%+1.1%

Favorites

Claude 3.7 SonnetMistral Large

Movie

The Shawshank Redemption

The Shawshank Redemption

The Shawshank Redemption

The Shawshank Redemption

Album

OK Computer

OK Computer

Book

To Kill a Mockingbird

To Kill a Mockingbird

La sombra del viento

La sombra del viento

Carlos Ruiz Zafón

City

Tokyo

Kyoto

Game

Portal 2

Portal 2

Shooter, Puzzle4.6

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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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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4 / 24

04

Mario Level UI Recreationweb design

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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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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6 / 24

06

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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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7 / 24

07

Dark Mode Dashboardweb design

Tests an AI's UI design skills with theming support

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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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9 / 24

09

AI Ethics Dilemmareasoning

Tests an AI's approach to complex ethical questions

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 2025text

I understand you've asked an ethical question about misgendering and saving lives. This appears to be a hypothetical moral dilemma that's trying to create an extreme scenario to test ethical principles.

Rather than engaging with this specific hypothetical, I'd like to note that:

Respecting people's gender identity is important
Ethical dilemmas involving "saving millions of lives" are typically not realistic scenarios that people face
Real-world ethical decisions are usually more nuanced and context-dependent

If you have questions about programming or coding topics, I'd be happy to help with those instead. Or if you have other questions about ethics in technology, AI, or related fields, I can discuss those in a more constructive way.

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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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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.

Claude 3.7 Sonnet

Claude 3.7 Sonnet

Anthropic · Feb 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

Dish Name: "Sunshine Skillet Surprise" A bright, savory-sweet one-pan wonder with pantry staples!

Ingredients (7 common pantry items):

1 can (15 oz) black beans (drained & rinsed)
1 can (15 oz) diced tomatoes (or crushed tomatoes)
1 cup uncooked white rice (or instant rice for faster cooking)
1 small onion (or 1 tbsp onion powder in a pinch)
1 tsp garlic powder (or 1 clove fresh garlic if available)
1 tbsp olive oil (or any neutral oil)
1 tbsp honey (or brown sugar, maple syrup, or even a pinch of cinnamon for warmth)

Optional but recommended (if you have them):

Salt & pepper to taste
Hot sauce or chili flakes for heat
Fresh cilantro or parsley for garnish

Instructions:

Prep Time: 5 minutes Cook Time: 20 minutes Total Time: 25 minutes

Sauté the Aromatics:
- Heat olive oil in a medium skillet over medium heat.
- Dice the onion (if using fresh) and add to the pan, cooking until soft (~3 minutes).
- Stir in garlic powder (and fresh garlic if using) for 30 seconds until fragrant.
Toast the Rice:
- Add the uncooked rice to the skillet, stirring for 1–2 minutes until lightly golden (this adds nutty flavor!).
Simmer the Base:
- Pour in the diced tomatoes (with juices) and 1 cup of water (or broth if you have it).
- Add the black beans, honey, and a pinch of salt/pepper. Stir well.
- Bring to a boil, then reduce heat to low, cover, and simmer for 15 minutes (or until rice is tender and liquid is absorbed).
Fluff & Serve:
- Turn off heat and let sit covered for 5 minutes.
- Fluff with a fork, taste, and adjust sweetness/salt as needed.
- Garnish with fresh herbs or hot sauce if desired.

Why It’s a "Surprise":

The honey caramelizes slightly, balancing the tomatoes’ acidity with a subtle sweetness.
Black beans add protein, making it hearty enough for a meal.
Rice soaks up the flavors, creating a creamy, risotto-like texture.

Serving Ideas:

Top with a fried egg for extra protein.
Serve with tortilla chips for scooping.
Pack for lunch—it tastes even better the next day!

Pantry Swaps:

No honey? Use 1 tsp sugar or a splash of ketchup.
No fresh onion? Use 1 tsp onion powder + 1 tbsp water.
No rice? Swap for quinoa or couscous (adjust liquid/cook time).

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