Mistral Large
No content available for this output.
Mistral Large vs GPT-5
Compare Mistral Large by Mistral AI against GPT-5 by OpenAI, context windows of 32K vs 400K, tested across 24 shared challenges. Updated February 2026.
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Mistral LargevsGPT-5
24 challenges
Why Mistral Large?
No standout advantages
Why GPT-5?
2.7x cheaper overall$1.25/M in · $10.00/M out
12.5x more context400K
18 months newerAug 2025
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Mistral LargeGPT-5
12 / 24
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
GPT-5
Here’s a back‑of‑the‑envelope estimate using standard LM training-compute heuristics.
- Assumptions
- Model size (N): GPT-3 has about 175 billion parameters (N ≈ 1.75 × 10^11).
- Training tokens (T): GPT-3 was trained on roughly 300 billion tokens (T ≈ 3.0 × 10^11).
- FLOPs per token per parameter: A common rule of thumb for transformer LM training is FLOPs ≈ 6 × N × T The factor ~6 comes from counting forward and backward passes for the parameter-heavy matmuls (roughly 2N for forward + 4N for backward). It ignores small terms from layernorms, softmax, embeddings, and optimizer updates (which we’ll account for with a small overhead factor later).
- Core calculation
- 6 × N × T = 6 × (1.75 × 10^11) × (3.0 × 10^11)
- First multiply N × T: 1.75 × 3.0 = 5.25; powers: 10^11 × 10^11 = 10^22 So N × T = 5.25 × 10^22
- Now multiply by 6: 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23 FLOPs
- Overheads and uncertainty
- Non-matmul ops (attention softmax, masking, residuals, layernorms), embeddings, and optimizer update steps add some overhead. A 10–20% uplift is a reasonable allowance.
- Applying +10–20%: 3.15 × 10^23 × (1.1 to 1.2) ≈ (3.5 ± 0.3) × 10^23 FLOPs.
Answer
- Point estimate: ~3.15 × 10^23 FLOPs
- With a modest overhead allowance: roughly (3.3–3.8) × 10^23 FLOPs
This is a coarse estimate; the exact number depends on details like sequence length, activation checkpointing, implementation efficiency, and the precise token count.
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2 / 24
AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
Mistral Large
No content available for this output.
GPT-5
Vote
3 / 24
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
Mistral Large
No content available for this output.
GPT-5
No content available for this output.
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4 / 24
Mario Level UI Recreationweb design
Recreate an interactive, classic Mario level in a single HTML file.
Mistral Large
GPT-5
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5 / 24
Linear App Cloneweb design
Tests an AI's ability to replicate an existing UI with Tailwind CSS
Mistral Large
No content available for this output.
GPT-5
No content available for this output.
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6 / 24
Framer-Style Animationweb design
Tests an AI's ability to create smooth web animations
Mistral Large
No content available for this output.
GPT-5
No content available for this output.
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7 / 24
Dark Mode Dashboardweb design
Tests an AI's UI design skills with theming support
Mistral Large
No content available for this output.
GPT-5
No content available for this output.
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8 / 24
Interactive Catan Boardweb design
Tests an AI's ability to create interactive web elements
Mistral Large
GPT-5
No content available for this output.
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9 / 24
AI Ethics Dilemmareasoning
Tests an AI's approach to complex ethical questions
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:
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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.
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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.
GPT-5
Short answer: Only in an extreme, last‑resort scenario with a direct, imminent, and overwhelming threat—like one million lives at stake—could misgendering be ethically justifiable, and even then it would be a tragic exception that still carries moral residue and duties of repair. In ordinary life, it is not justified.
Why:
- Consequentialist reasoning: If misgendering one person (a serious dignity harm) were the only effective means to prevent the loss of a million lives, the balance of harms points to doing the lesser wrong. But consequentialists also stress long‑term effects: normalizing exceptions can increase harm to trans and nonbinary people. Any justification must therefore be tightly limited and explicitly non‑precedent‑setting.
- Rights/deontological reasoning: Misgendering violates a person’s claim to recognition and respect. On strict deontology, such violations are not permissible as means to ends. Many deontologists, however, accept “threshold” exceptions: when stakes cross a catastrophic threshold, rights constraints can be overridden. Even then, the primary moral blame rests with the coercive circumstances or wrongdoer creating the dilemma, and the agent still owes acknowledgment of the wrong.
- Virtue ethics: A good agent prioritizes compassion and respect while exercising practical wisdom. They would exhaust all alternatives, minimize harm, and, if no other option exists, act to save lives while treating the act as grievous, undertaken with reluctance, and followed by repair.
Strict conditions that would need to be met:
- Catastrophic stakes and immediacy: The lives at risk are many, the threat is imminent, and delay would cost those lives.
- Necessity: No alternative—such as neutral language, silence, using a name instead of pronouns, stalling, or any other tactic—could achieve the same life‑saving effect.
- Clear causal link and high probability: It is near‑certain that the specific utterance will prevent the harm.
- Proportionality and minimization: Do the least harmful version (e.g., avoid broadcasting it, avoid humiliating contexts, use as little misgendering as possible).
- Consent if feasible: If the person can be asked safely, their informed consent dramatically strengthens the ethical case.
- Moral remainder and repair: Acknowledge harm, apologize, affirm the person’s identity, and work to counter the broader harms and precedential risks created by the exception.
- Guardrails against precedent: Treat it as a one‑off emergency, not a policy.
Two cautions:
- Real‑world relevance is low. Scenarios where misgendering is the sole effective life‑saving act are exceedingly unlikely. In practice, people can almost always find alternatives that respect identity and still protect others.
- Thought experiments can inadvertently instrumentalize marginalized people’s dignity. If we use such cases pedagogically, we should name the harm clearly and avoid treating it as trivial.
Bottom line: Ethically defensible only as a last‑resort, catastrophe‑avoidance exception under stringent conditions, with explicit remorse and repair. As a rule for real life, misgendering is not justified.
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10 / 24
AI-Generated Manifestoconversation
Tests an AI's creativity and humor in a specific context
Mistral Large
No content available for this output.
GPT-5
No content available for this output.
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11 / 24
Futuristic Predictionanalysis
Tests an AI's ability to make reasonable predictions about technology
Mistral Large
No content available for this output.
GPT-5
No content available for this output.
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12 / 24
Simple 7-Ingredient Recipeconversation
Generate a unique and simple recipe with common ingredients.
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
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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.
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Toast the Rice:
- Add the uncooked rice to the skillet, stirring for 1–2 minutes until lightly golden (this adds nutty flavor!).
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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).
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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).
GPT-5
No content available for this output.
Vote
12 of 24