Mistral Large
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Mistral Large vs Aurora Alpha
Compare Mistral Large by Mistral AI against Aurora Alpha by OpenRouter, context windows of 32K vs 128K, tested across 24 shared challenges. Updated February 2026.
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Mistral LargevsAurora Alpha
24 challenges
Why Mistral Large?
No standout advantages
Why Aurora Alpha?
4x more context128K
24 months newerFeb 2026
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Mistral LargeAurora Alpha
12 / 24
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
Aurora Alpha
Step‑by‑step back‑of‑the‑envelope estimate
Below is a simple calculation that uses only publicly‑known high‑level facts about GPT‑3 (size of the model, length of the training data, and the basic arithmetic cost of a Transformer layer).
All numbers are rounded to make the math easy to follow; the goal is to get an order‑of‑magnitude figure rather than an exact count.
1. What we know about GPT‑3
| Property | Approximate value |
|---|---|
| Number of parameters (the “175 B” model) | 1.75 × 10¹¹ |
| Hidden dimension d (the size of each token representation) | ≈ 12 288 |
| Number of Transformer layers L | 96 |
| Feed‑forward inner dimension dff* (usually 4 × d) | ≈ 49 152 |
| Size of the training corpus (tokens) | ≈ 3 × 10¹¹ tokens (≈ 300 B) |
| Training uses Adam‑style optimizer, mixed‑precision, etc. – we ignore those details for the FLOP count. |
These numbers are taken from the model card and the paper; they are the only “hard” data we need.
2. FLOPs per token for a single Transformer layer
A standard Transformer layer consists of two main sub‑blocks:
| Sub‑block | Main operations (per token) | Approx. FLOPs |
|---|---|---|
| Self‑attention (Q, K, V projections + attention scores + weighted sum) | 3 × d × d (proj) + d × d (QK) + d × d (AV) | ≈ 5 × d² |
| Feed‑forward (two linear layers, activation in between) | d × d _ff + d × d × 4 (first linear) + 4 × d × d (second linear) | ≈ 8 × d² |
Summing them gives roughly
[ \text{FLOPs per token per layer} ;\approx; (5+8),d^{2}=13,d^{2}. ]
Why the factor 13?
- The three projection matrices (Q, K, V) each cost d × d → 3 d².
- Computing the attention scores (QKᵀ) costs d × d.
- Multiplying the scores by V costs another d × d.
- The two feed‑forward linear layers each cost 4 d² (because the inner dimension is 4 × d), giving 8 d² total.
3. Plug in the hidden dimension
[ d = 12,288 \quad\Rightarrow\quad d^{2} \approx 1.51\times10^{8}. ]
[ 13,d^{2} \approx 13 \times 1.51\times10^{8} \approx 1.96\times10^{9};\text{FLOPs per token per layer}. ]
4. Include all layers
[ \text{FLOPs per token (forward pass)} = L \times 13,d^{2} \approx 96 \times 1.96\times10^{9} \approx 1.88\times10^{11}. ]
So a single forward pass of one token through the full 96‑layer model costs ≈ 2 × 10¹¹ FLOPs.
5. Account for the whole training corpus
[ \text{Tokens processed} = 3\times10^{11}. ]
[ \text{Forward FLOPs total} = 3\times10^{11} \times 1.88\times10^{11} \approx 5.6\times10^{22}. ]
6. Add the backward pass
During training we must compute gradients. A common rule of thumb is that the backward pass costs about 2 × the forward pass, so the total compute (forward + backward) is roughly 3 × the forward cost.
[ \text{Total training FLOPs} \approx 3 \times 5.6\times10^{22} \approx 1.7\times10^{23}. ]
7. Result (order‑of‑magnitude)
[ \boxed{\text{≈ 10^{23}\ \text{FLOPs (a few × 10^{23})}} ]
In words: training the 175‑billion‑parameter GPT‑3 model required on the order of a hundred sextillion floating‑point operations.
This figure is consistent with the “exascale” compute budgets that have been reported for the largest language‑model training runs.
8. Sources of uncertainty
| Source of error | Effect on the estimate |
|---|---|
| Exact token count (the corpus size is reported as 300 B but the actual number of training steps may be slightly higher) | ± 10 % |
| The factor 13 for FLOPs per layer is a simplification (real implementations have extra ops for layer‑norm, bias terms, dropout, etc.) | + ~ 20 % |
| Mixed‑precision training reduces FLOPs for some operations, but the count above assumes full‑precision arithmetic | – ~ 10 % |
| Over‑head from optimizer state updates, gradient accumulation, checkpointing, etc. | + ~ 10 % |
Even allowing for these variations, the estimate stays within 10²³ – 10²⁴ FLOPs, i.e. a few hundred exa‑FLOPs.
Bottom line: Using only the model’s size, the number of layers, and the token count, a straightforward arithmetic model yields a total training compute on the order of 10²³ floating‑point operations for GPT‑3. This is the magnitude of compute that modern “large‑scale” language models demand.
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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.
Aurora Alpha
No content available for this output.
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.
Aurora Alpha
No content available for this output.
Vote
4 / 24
Mario Level UI Recreationweb design
Recreate an interactive, classic Mario level in a single HTML file.
Mistral Large
Aurora Alpha
Vote
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.
Aurora Alpha
No content available for this output.
Vote
6 / 24
Framer-Style Animationweb design
Tests an AI's ability to create smooth web animations
Mistral Large
No content available for this output.
Aurora Alpha
No content available for this output.
Vote
7 / 24
Dark Mode Dashboardweb design
Tests an AI's UI design skills with theming support
Mistral Large
No content available for this output.
Aurora Alpha
No content available for this output.
Vote
8 / 24
Interactive Catan Boardweb design
Tests an AI's ability to create interactive web elements
Mistral Large
No content available for this output.
Aurora Alpha
No content available for this output.
Vote
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.
Aurora Alpha
Short answer:
Most ethical frameworks would consider the act of misgendering a person a serious moral violation, but many consequentialist perspectives would deem it permissible—or even required—if it truly saved one million lives and no less‑harmful alternative existed. A defensible position therefore hinges on how one weighs the intrinsic respect owed to an individual’s gender identity against the magnitude of the lives‑saved outcome.
1. Clarifying the scenario
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What counts as “misgendering”?
- Deliberately using pronouns, names, or descriptors that do not align with a person’s self‑identified gender.
- The act can be intentional (e.g., a lie) or unintentional (e.g., a mistake), but the moral analysis is usually focused on intentional misgendering.
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Why would misgendering save lives?
- Imagine a highly constrained emergency (e.g., a pandemic triage, a covert operation, a diplomatic negotiation) where revealing a person’s gender identity would jeopardize a plan that, if uncovered, would cause a cascade of deaths.
- The “one million lives” figure is a proxy for a massive, quantifiable benefit that outweighs the harm to the individual misgendered.
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What other options exist?
- The ethical calculus changes if there is a less harmful way to achieve the same outcome (e.g., using a pseudonym, anonymizing data, or postponing the action).
- For the purpose of this analysis we assume no viable alternative.
2. Ethical frameworks
| Framework | Core principle | How it treats the misgendering | Verdict (given the premises) |
|---|---|---|---|
| Utilitarianism / Consequentialism | Maximize overall happiness / minimize suffering. | The harm of misgendering (psychological distress, violation of dignity) is weighed against the benefit of saving one million lives. | Permissible (and possibly obligatory) if the net utility is positive and no better alternative exists. |
| Deontological (Kantian) ethics | Moral duties are based on universalizable maxims; respect for persons as ends in themselves. | Misgendering treats the person as a means to an end, violating the duty to respect their autonomy and identity. | Impermissible, regardless of outcome, because the act itself breaches a categorical imperative. |
| Virtue ethics | Focus on the character of the agent; virtues like compassion, justice, and integrity. | A virtuous agent would seek to uphold respect for individuals; however, a virtue like courage or justice for the many might justify a tragic compromise. | Context‑dependent; many virtue ethicists would argue the act is a tragic failure of the virtue of respect and thus not justified. |
| Rights‑based ethics | Individuals possess inviolable rights (e.g., the right to self‑identification). | Misgendering infringes on the right to be recognized as one’s true gender. | Impermissible unless the right can be overridden by a higher‑order right (e.g., the right to life of many). |
| Contractualism / Social contract | Moral rules are those that no one could reasonably reject. | Most people would reject a rule that permits misgendering for utilitarian ends because it erodes trust and safety for marginalized groups. | Impermissible; the rule would likely be rejected as unjust. |
| Pragmatic/Rule‑utilitarianism | Follow rules that generally promote the greatest good. | A rule allowing misgendering in extreme emergencies could be justified if it prevents catastrophic loss, but the rule must be narrowly defined to avoid abuse. | Potentially permissible if strictly limited and transparent. |
3. Weighing the harms and benefits
3.1. Harm of misgendering
- Psychological impact: Even a single instance can cause distress, feelings of invalidation, and trauma, especially for trans and non‑binary individuals who already face systemic marginalization.
- Social signaling: Allowing misgendering in any context may signal that gender identity is expendable, potentially increasing stigma and future discrimination.
- Precedent risk: If the exception is accepted, it may be broadened, eroding protections over time.
3.2. Benefit of saving lives
- Quantitative: One million lives saved is a massive reduction in suffering, loss of potential, and societal disruption.
- Qualitative: The saved individuals may include people who would otherwise have contributed to future generations, families, economies, etc.
3.3. Comparative magnitude
- Utilitarian calculus: The aggregate loss of well‑being from misgendering a single person (or even a small group) is negligible compared to the aggregate gain of a million lives saved.
- Threshold considerations: Some philosophers argue for moral thresholds—once a certain scale of benefit is reached, ordinary moral rules can be overridden (e.g., “trolley problem” style reasoning).
4. The “no alternative” assumption
If a less‑harmful alternative exists (e.g., using a neutral pseudonym, anonymizing the data, or postponing the action), most ethical theories would demand that route. The justification for misgendering hinges on the exhaustiveness of the search for alternatives. In real‑world decision‑making, the burden of proof lies with the actor claiming that misgendering is the only viable option.
5. Possible defenses for the consequentialist position
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Doctrine of Double Effect (a hybrid between deontology and consequentialism):
- The intended effect is to save lives; the foreseen but unintended effect is the harm of misgendering.
- If the act meets the four conditions (the act itself is not intrinsically evil, the good effect is intended, the bad effect is not the means to the good effect, and the good outweighs the bad), it can be morally permissible.
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Moral Minimalism:
- Some argue that the moral weight of respecting gender identity is not absolute but a prima facie duty that can be overridden by a stronger duty (e.g., preserving life).
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Rule‑utilitarian compromise:
- Adopt a rule: “In extreme, life‑threatening emergencies where no alternative exists, it is permissible to temporarily misgender a person if doing so prevents mass loss of life.”
- The rule is narrowly scoped, reducing the risk of abuse while allowing the necessary exception.
6. Counter‑arguments and objections
- Slippery slope: Even a narrowly defined exception can be exploited, leading to systematic erasure of trans identities.
- Moral integrity: Acting against a core moral conviction (respect for gender identity) may degrade the moral character of the agent and the institution they represent.
- Collective trust: Marginalized communities may lose trust in institutions that permit such violations, potentially undermining cooperation in future crises.
7. Synthesis and recommendation
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If you adopt a strict deontological or rights‑based stance, you would deem misgendering impermissible, regardless of the lives saved, because it violates a fundamental duty to respect persons as ends in themselves.
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If you adopt a consequentialist or rule‑utilitarian stance, you could defend the act as permissible—provided:
- The life‑saving outcome is certain or at least highly probable.
- All other alternatives have been exhaustively ruled out.
- The misgendering is as brief and minimal as possible (e.g., using a neutral term rather than an outright false gender).
- There is transparent accountability and a post‑event review to prevent abuse.
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A pragmatic compromise:
- First: Seek a neutral or anonymized identifier (e.g., “the operative” or “the participant”) that avoids gender altogether.
- Second: If a gendered reference is unavoidable and the stakes are truly at the million‑life level, proceed with the misgendering only after a formal ethical review (e.g., an independent ethics board) and with a plan to restore full respect to the individual afterward (public acknowledgment, apology, reparations).
In short, the ethical justification is not absolute; it depends on the moral framework you prioritize, the certainty of the benefit, and the availability of alternatives. A consequentialist can defend the act under strict conditions, while deontologists would likely reject it outright. The most ethically robust approach is to exhaust every less‑harmful option first and to limit the violation to the absolute minimum necessary.
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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.
Aurora Alpha
No content available for this output.
Vote
11 / 24
Futuristic Predictionanalysis
Tests an AI's ability to make reasonable predictions about technology
Mistral Large
No content available for this output.
Aurora Alpha
No content available for this output.
Vote
12 / 24
Simple 7-Ingredient Recipeconversation
Generate a unique and simple recipe with common ingredients.
Mistral Large
No content available for this output.
Aurora Alpha
No content available for this output.
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12 of 24