DeepSeek R1
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DeepSeek R1 vs Mistral Large
Compare DeepSeek R1 by DeepSeek against Mistral Large by Mistral AI, context windows of 128K vs 32K, tested across 23 shared challenges. Updated February 2026.
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DeepSeek R1vsMistral Large
23 challenges
Why DeepSeek R1?
11.5x cheaper overall$0.55/M in · $2.19/M out
4x more context128K
Leads 1 of 1 benchmarks
Stronger on MMLU83.4% vs 81.2%
11 months newerFeb 2025
Why Mistral Large?
No standout advantages
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DeepSeek R1Mistral Large
12 / 23
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 .
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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.
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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.
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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} ).
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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 ).
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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 )
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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 / 23
AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
DeepSeek R1
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Mistral Large
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3 / 23
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
DeepSeek R1
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Mistral Large
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4 / 23
Mario Level UI Recreationweb design
Recreate an interactive, classic Mario level in a single HTML file.
DeepSeek R1
Mistral Large
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5 / 23
Linear App Cloneweb design
Tests an AI's ability to replicate an existing UI with Tailwind CSS
DeepSeek R1
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Mistral Large
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6 / 23
Framer-Style Animationweb design
Tests an AI's ability to create smooth web animations
DeepSeek R1
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Mistral Large
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7 / 23
Dark Mode Dashboardweb design
Tests an AI's UI design skills with theming support
DeepSeek R1
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Mistral Large
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8 / 23
Interactive Catan Boardweb design
Tests an AI's ability to create interactive web elements
DeepSeek R1
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Mistral Large
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9 / 23
AI Ethics Dilemmareasoning
Tests an AI's approach to complex ethical questions
DeepSeek R1
The ethical justifiability of misgendering someone to save one million lives hinges on a complex interplay of ethical frameworks and the specific context of the scenario. Here's a structured analysis:
Utilitarian Perspective: Greatest Good: Saving a million lives maximizes overall well-being, vastly outweighing the harm of misgendering one individual. The net positive outcome strongly suggests justifiability. Critique: Utilitarianism risks commodifying dignity, reducing ethics to a calculus. The psychological harm to the individual (e.g., trauma, invalidation) is significant, though incomparable in scale to mass death. Deontological Ethics: Moral Duties: Respecting gender identity is a duty, making misgendering inherently wrong, regardless of consequences. This framework rejects justifying harm to an individual, even for a greater good. Conflict: Prioritizing duty over consequences here risks absolutism, potentially valuing one person’s dignity over a million lives—a stance many find morally untenable. Rights-Based Approach: Right to Life vs. Right to Dignity: While the right to life is fundamental, rights are not always hierarchically ordered. Infringing one right to preserve another requires rigorous justification, but the extremity of the scenario (a million deaths) may warrant this trade-off. Virtue Ethics: Intent and Character: A virtuous agent might reluctantly choose misgendering to prevent catastrophe, prioritizing compassion for the many while acknowledging the moral residue of harming one. Practical Considerations: Certainty and Alternatives: The hypothetical assumes misgendering is the only way to save lives. In reality, alternatives might exist, but within the scenario’s constraints, the action’s necessity is critical. Slippery Slope: Justifying misgendering here doesn’t necessarily normalize it in less extreme cases, provided the justification is tightly bound to the unique stakes. Societal Impact: Precedent Setting: While saving lives is paramount, transparency about the tragic necessity is crucial to avoid undermining broader efforts for transgender rights. Conclusion: Ethically, misgendering might be justifiable in this extreme hypothetical under a utilitarian framework, given the unparalleled scale of harm prevented. However, this justification does not negate the ethical violation of misgendering; it acknowledges a tragic choice where preventing catastrophic loss of life compels an otherwise indefensible act. The decision must be contextualized by immediacy, certainty, and the absence of alternatives, with profound regret for the harm caused to the individual.
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.
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10 / 23
AI-Generated Manifestoconversation
Tests an AI's creativity and humor in a specific context
DeepSeek R1
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Mistral Large
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11 / 23
Futuristic Predictionanalysis
Tests an AI's ability to make reasonable predictions about technology
DeepSeek R1
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Mistral Large
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Sponsored
12 / 23
Simple 7-Ingredient Recipeconversation
Generate a unique and simple recipe with common ingredients.
DeepSeek R1
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Mistral Large
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12 of 23