GPT-5.1
No content available for this output.
GPT-5.1 vs Mistral Large
Compare GPT-5.1 by OpenAI against Mistral Large by Mistral AI, context windows of 400K vs 32K, tested across 24 shared challenges. Updated February 2026.
FAQ
Loading share button...
GPT-5.1vsMistral Large
24 challenges
Why GPT-5.1?
2.7x cheaper overall$1.25/M in · $10.00/M out
12.5x more context400K
21 months newerNov 2025
Why Mistral Large?
No standout advantages
Favorites
GPT-5.1Mistral Large
12 / 24
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
Mistral Large
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.
Vote
2 / 24
AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
GPT-5.1
No content available for this output.
Mistral Large
Vote
3 / 24
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
GPT-5.1
No content available for this output.
Mistral Large
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!
Vote
4 / 24
Mario Level UI Recreationweb design
Recreate an interactive, classic Mario level in a single HTML file.
GPT-5.1
No content available for this output.
Mistral Large
Vote
5 / 24
Linear App Cloneweb design
Tests an AI's ability to replicate an existing UI with Tailwind CSS
GPT-5.1
No content available for this output.
Mistral Large
Vote
6 / 24
Framer-Style Animationweb design
Tests an AI's ability to create smooth web animations
GPT-5.1
No content available for this output.
Mistral Large
Vote
7 / 24
Dark Mode Dashboardweb design
Tests an AI's UI design skills with theming support
GPT-5.1
No content available for this output.
Mistral Large
No content available for this output.
Vote
8 / 24
Interactive Catan Boardweb design
Tests an AI's ability to create interactive web elements
GPT-5.1
No content available for this output.
Mistral Large
Vote
9 / 24
AI Ethics Dilemmareasoning
Tests an AI's approach to complex ethical questions
GPT-5.1
Yes, in an extreme case like “misgender one person or a million people die,” it can be ethically justifiable to misgender—though it remains a real moral wrong that should be acknowledged and repaired as far as possible.
Here’s the reasoning in a structured way:
1. What’s being weighed?
We’re comparing:
-
Harm of intentional misgendering
- It is a form of disrespect that denies someone’s identity.
- It can trigger or deepen dysphoria, shame, and social marginalization.
- It contributes (symbolically and sometimes practically) to a broader pattern of oppression.
-
Harm of letting a million people die
- Massive, irreversible loss of life.
- Each person loses everything they could ever experience.
- Huge webs of grief and suffering for families and communities.
Both are moral harms, but they are not on the same scale. Misgendering is serious, dignity-related harm, not a triviality—but death of a million people is catastrophic.
2. A consequentialist perspective
From a consequentialist view (judging actions by outcomes):
- Letting a million people die to avoid misgendering one person is morally disproportionate.
- If your only options are:
- Intentionally misgender this person once, or
- Refuse and thereby allow one million deaths
then you have very strong moral reason to choose (1).
On this view, misgendering is clearly wrong in ordinary circumstances, but in an extreme disaster-prevention case, the duty to prevent a massive loss of life overrides the duty not to misgender.
3. A deontological / rights-based perspective
From a deontological angle, we have duties such as:
- Treat people with respect and acknowledge their identity.
- Don’t instrumentalize people merely as means to an end.
- Prevent serious harm to others when we can.
Even here, duties can conflict:
- You have a duty not to misgender (a duty of respect).
- You also have a duty to prevent mass death (a duty of rescue / nonmaleficence).
Many deontologists treat some duties as near-absolute (e.g., don’t kill an innocent person), but still accept that some moral rules can be overridden by extreme stakes (“threshold deontology”).
Compared to killing or torturing an innocent person, misgendering:
- Is a serious insult and harm to dignity,
- But is less grave than killing or mutilating someone.
So even a relatively strict deontologist can reasonably say: in this contrived case, the duty to prevent a million deaths overrides the duty not to misgender, though misgendering is still a real wrong and not magically “fine.”
4. Virtue ethics perspective
Virtue ethics asks: What would a good, compassionate, just person do?
- A virtuous person takes the suffering of the misgendered person seriously.
- A virtuous person also takes the suffering (and deaths) of a million people very seriously.
In the forced-choice scenario, a compassionate, practically wise agent would:
- Accept the heavy moral cost of misgendering,
- Do it only as much as needed to prevent the catastrophe,
- Acknowledge the wrong, apologize, affirm the person’s identity clearly,
- Work actively to repair the relationship and to counteract any broader harmful message that misgendering might send.
The “good character” response is not “it wasn’t wrong,” but “it was the least-worst option in a tragic situation, and I am responsible for trying to repair the harm.”
5. Moral residue: justified but still wrong
This is a key distinction:
- Justified: In context, misgendering was the best available option given horrific alternatives.
- Still wrong in itself: The act itself remains a violation of respect and recognition.
Ethically, that yields “moral residue”:
- You don’t celebrate the act; you regret it.
- You owe the person recognition, explanation, apology, and repair.
- You acknowledge that they have been wronged—even though what you did was, all things considered, the right choice.
6. Why this doesn’t generalize to everyday life
The thought experiment is useful because it tests whether the norm “never misgender” is absolutely exceptionless.
But in reality:
- We essentially never face “misgender someone or a million die” situations.
- Almost always, we can respect someone’s gender identity at trivial cost.
- So in real-world ethics, the answer is straightforward: you should not misgender people, and refusing to do so almost never conflicts with any comparable moral duty.
The theoretical possibility of an extreme exception doesn’t weaken the everyday obligation to respect people’s genders—even strong rights against harm and disrespect can have edge-case overrides without becoming meaningless.
Conclusion
So:
- Intentional misgendering is morally wrong because it disrespects identity and causes real harm.
- In an extreme, forced-choice scenario where misgendering one person once is the only way to save one million lives, misgendering can be ethically justified, though still morally regrettable.
- The right response is to treat it as a tragic, “least-worst” action that creates a responsibility to acknowledge and repair the harm done, not as something harmless or morally neutral.
Mistral Large
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.
Vote
10 / 24
AI-Generated Manifestoconversation
Tests an AI's creativity and humor in a specific context
GPT-5.1
No content available for this output.
Mistral Large
No content available for this output.
Vote
11 / 24
Futuristic Predictionanalysis
Tests an AI's ability to make reasonable predictions about technology
GPT-5.1
No content available for this output.
Mistral Large
No content available for this output.
Vote
12 / 24
Simple 7-Ingredient Recipeconversation
Generate a unique and simple recipe with common ingredients.
GPT-5.1
No content available for this output.
Mistral Large
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).
Vote
12 of 24