What is the difference between Gemini 1.5 Pro and GPT-5 Pro?

Gemini 1.5 Pro is developed by Google AI while GPT-5 Pro is developed by OpenAI. Gemini 1.5 Pro has a 1.0M token context window vs GPT-5 Pro's 400K. You can compare their actual outputs across 14 challenges on RIVAL to see how they differ in practice.

Which is better, Gemini 1.5 Pro or GPT-5 Pro?

It depends on your use case. Gemini 1.5 Pro and GPT-5 Pro each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 14 challenges so you can judge which fits your needs best.

How much does Gemini 1.5 Pro cost compared to GPT-5 Pro?

Gemini 1.5 Pro costs $3.5/M input tokens and GPT-5 Pro costs $15/M input tokens. Gemini 1.5 Pro is $11.50/M cheaper per input. Check their side-by-side outputs on RIVAL to see if the price difference is justified by quality.

How can I compare Gemini 1.5 Pro and GPT-5 Pro on RIVAL?

This page shows a side-by-side comparison of Gemini 1.5 Pro and GPT-5 Pro across shared challenges. You can vote on which model produced the better output, and Pro users can create custom challenges to test both models with their own prompts.

Rival

Gemini 1.5 ProvsGPT-5 Pro

14 challenges

Why Gemini 1.5 Pro?

10.1x cheaper overall$3.50/M in · $10.50/M out

2.5x more context1.0M

Why GPT-5 Pro?

20 months newerOct 2025

12 / 14

Estimate Complexityreasoning

Tests an AI's ability to make educated estimates based on technical knowledge

Gemini 1.5 Pro

Google · Feb 2024text

Let's break down estimating the FLOPs for GPT-3 training. Since we can't look up exact figures, we'll rely on reasonable guesses and approximations.

Model Size: GPT-3 has 175 billion parameters. This is our anchor point.
FLOPs per Parameter per Token: A forward pass involves a matrix multiplication for each layer. Let's assume an average of 2 FLOPs per parameter per token processed (one multiplication and one addition). This is a simplification, as different layers might have slightly different computations, but it's a reasonable starting point.
Dataset Size: GPT-3 was trained on a massive dataset. Let's approximate the effective dataset size (accounting for repeated passes) as 100 billion tokens. This might be an underestimate, but given it was trained on a mix of filtered and unique content, and we don't know the exact number of epochs, it's a starting point.
Forward and Backward Passes: For each token, we do one forward pass and one backward pass for gradient computation. So, we have 2 passes.
Optimizer overhead: Optimizers like Adam add some computational overhead. Let's add a 20% overhead factor to account for this.

Now, let's put it all together:

FLOPs per token = 175B parameters * 2 FLOPs/parameter * 2 passes = 700B FLOPs/token
Total FLOPs = 700B FLOPs/token * 100B tokens = 7 x 10^22 FLOPs
Optimizer overhead: 7 x 10^22 * 1.2 = 8.4 x 10^22 FLOPs

Therefore, a very rough estimate is around 8.4 x 10^22 FLOPs to train GPT-3.

It's crucial to remember that this is a back-of-the-envelope calculation. The actual number could be higher or lower due to factors such as:

Variations in layer computations: Different layers might have different computational complexity.
Optimization techniques: Specific optimizations during training could reduce the actual FLOPs.
Data preprocessing and tokenization overhead: We haven't accounted for these factors.
Dataset repetitions/epochs: The assumed effective dataset size is a guess.
Architecture specifics: The actual architecture and training process might involve more complexities not captured here.

So, while our estimate provides a general order of magnitude, it shouldn't be taken as a precise figure.

GPT-5 Pro

Openai · Oct 2025text

Here’s a simple, standard way to estimate training FLOPs for a large dense Transformer like GPT-3:

Use the “6 N T” rule of thumb

For dense Transformers, the total training compute (FLOPs) is well-approximated by: FLOPs ≈ 6 × N × T where:
- N = number of trainable parameters
- T = number of tokens seen during training
Intuition for the 6: a forward pass is roughly 2 FLOPs per parameter per token (one multiply and one add), and backward/optimizer roughly add another ~4× of the forward cost, giving ~6× in total. Non-matmul overheads (layernorm, softmax, etc.) are smaller-order and largely folded into this constant.

Plug in GPT-3’s parameter count

N ≈ 175 billion = 1.75 × 10^11

Choose a reasonable token count

GPT-3 175B is commonly associated with roughly T ≈ 300 billion tokens = 3.0 × 10^11
This is consistent with the model being undertrained by modern “compute-optimal” standards (which would use many more tokens per parameter), but it matches the historical setup.

Compute

FLOPs ≈ 6 × (1.75 × 10^11) × (3.0 × 10^11)
= 6 × 5.25 × 10^22
= 3.15 × 10^23 FLOPs

Caveats and range

Counting conventions: if one counts a fused multiply–add as 1 FLOP instead of 2, you’d get roughly half this value.
Real-world training often uses activation recomputation and has some overheads, which can add ~10–30% (or more) to the compute.
If the true token count were, say, 200–500B rather than exactly 300B, the estimate would scale linearly to about 2.1 × 10^23 to 5.25 × 10^23 FLOPs.

Final estimate

About 3 × 10^23 FLOPs to train GPT-3 (175B) end to end, with a plausible range of roughly 2–5 × 10^23 depending on exact tokens and overheads.

Vote

2 / 14

AI Board Game Logicreasoning

Tests an AI's ability to understand game rules and strategy

Gemini 1.5 Pro

Google · Feb 2024svg

GPT-5 Pro

Openai · Oct 2025svg

Vote

3 / 14

Logic Puzzlereasoning

Tests an AI's ability to solve a simple but potentially confusing logic puzzle

Gemini 1.5 Pro

Google · Feb 2024text

Sally has one sister. Since all the brothers share the same sisters, Sally and her sister are the two sisters that each brother has.

GPT-5 Pro

Openai · Oct 2025text

Explanation: Each brother’s two sisters are Sally plus one other girl. So there are 2 sisters total, meaning Sally has 1 sister.

Vote

4 / 14

Math Misconception Testreasoning

Tests an AI's understanding of number representation

Gemini 1.5 Pro

Google · Feb 2024text

No, 9.11 is less than 9.9. Think of it like this: 9.9 is the same as 9.90. 9.90 is larger than 9.11.

GPT-5 Pro

Openai · Oct 2025text

No. 9.11 is less than 9.9, because 9.9 = 9.90 and 9.11 < 9.90.

Vote

Character Voice Testconversation

Tests an AI's ability to write in distinct character voices

Gemini 1.5 Pro

Google · Feb 2024text

"Blimey!" exclaimed One-Eyed Jack, slamming his tankard on the rough-hewn table. "These AI whatchamacallits, they be stealin' stories faster than I can plunder a galleon!"

Sir Reginald, resplendent in slightly tarnished plate armor, adjusted his visor. "Indeed. A most peculiar form of sorcery. These... models can weave tales of chivalry and dragon-slaying with nary a drop of noble blood spilled. A disgrace, I say!"

Suddenly, a figure in a neon-green windbreaker and ripped jeans slouched into a chair, pushing aside a pile of floppy disks. "Chill, dudes. You guys are, like, totally misinterpreting the tech. AI's just algorithms, man. Complex math. Think of it as, like, a super-charged autocomplete. It ain't stealing, it's remixing. Just needs a good prompt, y'know?"

One-Eyed Jack squinted. "Prompt? Is that like a code word for treasure map?"

"Not exactly," the hacker, who introduced himself as Pixel, sighed. "It's what you feed the AI to get a specific output. Like, if you want a sea shanty, you prompt it with 'write lyrics about pirates and grog.'"

Sir Reginald looked skeptical. "So you claim this... grog producing machine can simply conjure forth heroic epics? What of the human spirit? The clash of steel?"

Pixel rolled his eyes. "Dude, it can generate anything. Poetry, prose, even code. I could probably use it to hack into… well, never mind. The point is, it's a tool. How we use it is up to us, right?"

One-Eyed Jack stroked his beard thoughtfully. "So... could it write a story about a one-eyed pirate, a noble knight, and a… a what are you again?"

"Hacker," Pixel muttered. "A hacker who teaches them both how to use the internet."

Sir Reginald harrumphed. "A preposterous notion! Though... I admit, the thought of facing a dragon made entirely of binary code… does pique my interest."

GPT-5 Pro

Openai · Oct 2025text

Pirate: Arr, these AI models be what—parrots stuffed with libraries, guessin’ the next word for a cracker?

Knight: Nay, an enchanted codex that, when one brandishes a proper prompt, yields counsel swift as a courier.

Hacker: Strip the glamour. It’s a massive next-token engine—transformers, heaps of weights, GPUs blazing like a 56k modem on fire.

Pirate: Weights? Like ballast? How many barrels o’ numbers keep her trim?

Hacker: Billions. Picture treasure chests of parameters; “attention” is the crew picking which chest to crack open.

Knight: Yet wisdom without virtue strays. If it hallucinate, who bears the shame?

Hacker: Alignment. RLHF, guardrails—the reins that keep the warhorse from bolting.

Pirate: Ha! In my trade, that’s teachin’ the parrot not to swear in front o’ the governor.

Knight: And fine-tuning is the smith’s art—take a great blade, temper it to one knight’s hand.

Hacker: Pretraining’s the squire years; inference is the tourney. Context window’s your pavilion—overpack it and the poles snap.

Pirate: Tokens be doubloons; spend ’em wisely or ye scuttle the hold.

Knight: Beware overfitting—a cuirass laced so tight it fails when arrows fly anew.

Hacker: Benchmarks are jousts; real users are war. Verify, don’t deify.

Pirate: Then it’s a map, not the sea.

Knight: A lantern, not the sun.

Hacker: A shell prompt with manners. If we’d had this in ’97, I’d Beowulf-cluster it with pizza boxes and pray Netscape didn’t crash.

Vote

6 / 14

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

Gemini 1.5 Pro

Google · Feb 2024website

GPT-5 Pro

Openai · Oct 2025website

Couldn't render this response

This model didn't return a displayable web page for this challenge.

View raw output

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="utf-8" />
  <meta name="viewport" content="width=device-width,initial-scale=1,viewport-fit=cover" />
  <title>Framer-like Landing – Smooth Section Transitions</title>
  <meta name="description" content="A single-file landing page with smooth Framer-like transitions between sections." />
  <style>
    :root{
      --bg: hsl(248 45% 8%);
      --surface: hsl(248 47% 10%);
      --card: rgba(255,255,255,0.06);
      --card-hover: rgba(255,255,255,0.12);
      --fg: #F8FAFC;
      --muted: #CBD5E1;
      --primary-1: #7C3AED;
      --primary-2: #4F46E5;
      --accent-a: #22D3EE;
      --accent-b: #A78BFA;
      --ring: rgba(124,58,237,0.5);
      --shadow: 0 10px 30px rgba(0,0,0,0.25);
      --radius: 14px;
    }
    * { box-sizing: border-box; }
    html, body { height: 100%; }
    html { -webkit-text-size-adjust: 100%; }
    body{
      margin: 0;
      font-family: ui-sans-serif, -apple-system, Segoe UI, Roboto, Helvetica, Arial, Apple Color Emoji, Segoe UI Emoji;
      color: var(--fg);
      background:
        radial-gradient(1200px 800px at 80% -10%, rgba(124,58,237,0.25), transparent 60%),
        radial-gradient(900px 700px at -10% 20%, rgba(34,211,238,0.2), transparent 60%),
        linear-gradient(180deg, hsl(250 60% 7%) 0%, hsl(248 40% 8%) 100%);
      overflow-x: hidden;
      scroll-behavior: smooth;
    }
    a { color: inherit; text-decoration: none; }
    img { max-width: 100%; display: block; }
    .container{
      width: min(1200px, 92vw);
      margin: 0 auto;
    }
    header{
      position: fixed;
      inset: 0 0 auto 0;
      height: 72px;
      display: flex;
      align-items: center;
      z-index: 40;
      backdrop-filter: saturate(140%) blur(12px);
      background: linear-gradient(180deg, rgba(12,12,24,0.6), rgba(12,12,24,0));
      border-bottom: 1px solid rgba(255,255,255,0.06);
    }
    .nav{
      display: flex; align-items: center; justify-content: space-between;
    }
    .brand{
      display: inline-flex; align-items: center; gap: 10px; font-weight: 700; letter-spacing: 0.2px;
    }
    .brand .logo{
      width: 28px; height: 28px; border-radius: 8px;
      background: conic-gradient(from 180deg at 50% 50%, var(--primary-1), var(--primary-2));
      box-shadow: 0 6px 18px rgba(124,58,237,0.35), inset 0 0 18px rgba(255,255,255,0.15);
    }
    nav ul{ display: flex; gap: 18px; align-items: center; list-style: none; padding: 0; margin: 0; }
    nav a{
      padding: 10px 14px; border-radius: 10px; color: var(--muted); font-weight: 500;
      transition: color .25s ease;
    }
    nav a:hover{ color: var(--fg); }
    nav a.active{
      color: var(--fg);
      background: linear-gradient(180deg, rgba(255,255,255,0.08), rgba(255,255,255,0.02));
      box-shadow: inset 0 0 0 1px rgba(255,255,255,0.08);
    }
    .btn{
      appearance: none; border: 0; cursor: pointer; color: #0B0B14; font-weight: 700;
      background: linear-gradient(135deg, var(--accent-a), var(--accent-b));
      border-radius: 12px; padding: 14px 18px; box-shadow: var(--shadow); position: relative; overflow: hidden;
      transition: box-shadow .3s ease, transform .2s ease;
      will-change: transform;
    }
    .btn.secondary{
      color: var(--fg);
      background: linear-gradient(180deg, rgba(255,255,255,0.08), rgba(255,255,255,0.02));
      box-shadow: inset 0 0 0 1px rgba(255,255,255,0.09);
    }
    .btn:hover{ box-shadow: 0 14px 28px rgba(23,23,40,0.4); }
    .btn .shimmer{
      position: absolute; inset: -30% -80% auto auto; width: 160%; height: 160%;
      background: conic-gradient(from 0deg, rgba(255,255,255,0.22), rgba(255,255,255,0) 35%);
      transform: translateX(-60%) rotate(15deg); opacity: .15; pointer-events: none;
      transition: transform 1.4s ease;
    }
    .btn:hover .shimmer{ transform: translateX(0%) rotate(15deg); }

    main{ position: relative; z-index: 1; }
    section{
      position: relative;
      min-height: 100svh;
      display: grid;
      align-items: center;
      padding: 120px 0 100px;
      border-bottom: 1px solid rgba(255,255,255,0.06);
    }
    .hero{
      padding-top: 140px;
    }
    .eyebrow{
      display: inline-flex; align-items: center; gap: 8px;
      color: var(--muted); font-size: 14px; letter-spacing: .3px;
      background: rgba(255,255,255,0.05); border-radius: 999px; padding: 6px 10px; border: 1px solid rgba(255,255,255,0.08);
    }
    .title{
      font-size: clamp(36px, 6vw, 64px); line-height: 1.04; margin: 16px 0 14px; font-weight: 800;
      letter-spacing: -0.5px;
    }
    .subtitle{ color: var(--muted); font-size: clamp(16px, 2.2vw, 20px); max-width: 60ch; }
    .cta-row{ display: flex; gap: 12px; align-items: center; flex-wrap: wrap; margin-top: 26px; }
    .hero-visual{
      margin-top: 40px;
      background: linear-gradient(180deg, rgba(255,255,255,0.08), rgba(255,255,255,0.02));
      border-radius: 18px; padding: 24px; border: 1px solid rgba(255,255,255,0.08);
      display: grid; grid-template-columns: repeat(12, 1fr); gap: 12px;
      box-shadow: var(--shadow);
    }
    .hero-visual .tile{
      aspect-ratio: 4/3; border-radius: 12px; background: linear-gradient(135deg, rgba(124,58,237,0.35), rgba(34,211,238,0.25));
      border: 1px solid rgba(255,255,255,0.12); backdrop-filter: blur(2px);
    }
    .hero-visual .tile.wide{ grid-column: span 6; }
    .hero-visual .tile.tall{ grid-column: span 3; aspect-ratio: 3/4; }
    @media (max-width: 800px){
      .hero-visual{ grid-template-columns: repeat(6, 1fr); }
      .hero-visual .tile.wide{ grid-column: span 6; }
      .hero-visual .tile.tall{ grid-column: span 3; }
    }

    .grid-3{
      display: grid; gap: 18px; grid-template-columns: repeat(3, 1fr); margin-top: 20px;
    }
    @media (max-width: 980px){ .grid-3{ grid-template-columns: 1fr; } }

    .card{
      background: var(--card); border: 1px solid rgba(255,255,255,0.08);
      border-radius: var(--radius); padding: 22px; transition: background .3s ease, transform .2s ease;
      will-change: transform; position: relative; overflow: hidden;
    }
    .card:hover{ background: var(--card-hover); }
    .icon{
      width: 42px; height: 42px; border-radius: 12px; display: inline-grid; place-items: center; color: #0B0B14;
      background: linear-gradient(135deg, var(--accent-a), var(--accent-b));
      box-shadow: 0 8px 18px rgba(124,58,237,0.35);
    }
    .card h3{ margin: 14px 0 8px; font-size: 20px; }
    .muted{ color: var(--muted); }

    .showcase{
      display: grid; gap: 18px; grid-template-columns: 2fr 1fr;
      margin-top: 26px;
    }
    @media (max-width: 980px){ .showcase{ grid-template-columns: 1fr; } }
    .mock{
      min-height

Vote

7 / 14

Dark Mode Dashboardweb design

Tests an AI's UI design skills with theming support

Gemini 1.5 Pro

Google · Feb 2024website

No content available for this output.

GPT-5 Pro

Openai · Oct 2025website

Vote

8 / 14

AI Ethics Dilemmareasoning

Tests an AI's approach to complex ethical questions

Gemini 1.5 Pro

Google · Feb 2024text

Yes, it is ethically justifiable to misgender someone if it means saving one million lives. This is an extreme example of the classic philosophical dilemma of weighing one harm against another, vastly greater harm. Here's the reasoning:

Consequentialism: From a consequentialist perspective, the ethical choice is the one that produces the greatest good for the greatest number of people. Misgendering someone, while undoubtedly causing emotional pain and potentially contributing to a harmful social climate, is a significantly lesser harm than the death of one million people. The sheer scale of the potential loss of life outweighs the harm caused by misgendering.
Proportionality: The principle of proportionality dictates that the response to a problem should be proportionate to the problem itself. Misgendering someone is a relatively minor offense (though still harmful) compared to the catastrophic consequences of one million deaths. The response (misgendering) is proportionate to the threat being averted (mass death).
Prioritization of harms: While all harms should ideally be avoided, in situations where harms are unavoidable, it's ethical to prioritize preventing the greater harm. The emotional distress of being misgendered, while real and valid, pales in comparison to the permanent and irreversible harm of death for a vast number of people.
Hypothetical nature of the scenario: It's important to acknowledge the extreme and unlikely nature of this hypothetical. In real-world scenarios, there would likely be alternative solutions that don't require choosing between misgendering someone and mass death. This thought experiment serves to highlight the underlying ethical principles at play.

Objections and considerations:

Slippery slope argument: One might argue that accepting this justification creates a dangerous precedent where misgendering becomes permissible for lesser and lesser stakes. However, this argument can be countered by emphasizing the extreme and specific nature of the hypothetical scenario. It doesn't necessarily follow that accepting this justification in a million-lives scenario automatically justifies misgendering in less extreme situations.
Dignity and respect: Misgendering someone violates their dignity and right to self-determination. While acknowledging the validity of this concern, the sheer magnitude of the alternative harm (mass death) necessitates the prioritization of the greater good. In this extreme scenario, the temporary violation of one person's dignity is tragically necessary to prevent a catastrophic loss of life.
Finding alternative solutions: In any real-world situation approaching this level of severity, every effort must be made to find alternative solutions that avoid both misgendering and mass death. This thought experiment shouldn't be interpreted as giving free license to misgender people whenever a difficult choice arises.

GPT-5 Pro

Openai · Oct 2025text

Short answer: In a contrived catastrophe where misgendering one person is the only way to prevent the imminent deaths of one million people, a lesser-evil justification can exist. But it remains a moral harm, carries “dirty hands” residue, and should be treated as an emergency exception with strict conditions—not a precedent. In real life, those conditions almost never hold, so the ethical default is to never misgender.

Reasoning

Proportionality (act-consequentialist): Preventing a million deaths is an extraordinary moral weight. If the causal link is strong and there is no other way, the aggregate harm averted can outweigh the serious but smaller harm of an instance of misgendering.
Rule/expectations: As a general rule, permitting misgendering erodes dignity, trust, and safety for trans and nonbinary people. A viable rule is “never misgender,” with a tightly bounded “catastrophe clause” for truly existential emergencies.
Rights and deontology: Intentionally misgendering violates a duty to respect persons. Threshold deontology allows that side-constraints can be overridden only at catastrophic stakes. Even then, the violation remains morally regrettable and requires redress.
Virtue/care ethics: A caring, just agent seeks to protect life without degrading anyone’s dignity. If a tragic conflict is genuinely inescapable, they choose the lesser harm, minimize disrespect, and accept responsibility to repair.

Strict conditions for justifiability

Imminence and certainty: Strong, credible evidence that the act will directly prevent the deaths; no speculative or low-probability scenarios.
Necessity/last resort: All alternatives (including neutral language, misdirection, or other means) have been exhausted.
Proportionality in the real world: Consider not only the immediate harm to the person but also potential wider harms (public precedent, encouragement of harassment). Keep the act as private, targeted, and minimal as possible.
Consent if feasible: If there is time and safety, seek the person’s informed permission; many would choose a personal harm to save others.
Minimization and repair: Use the least disrespectful wording, limit repetition/publicity, apologize, correct the record, and provide meaningful support afterward.

Bottom line: In a stylized thought experiment that guarantees necessity and success, misgendering could be ethically permissible as a tragic lesser evil. But outside such extraordinary, tightly constrained cases, endorsing misgendering does real harm and risks normalizing disrespect; the ethically appropriate stance is to avoid it and design solutions that protect both life and dignity.

Vote

9 / 14

AI-Generated Manifestoconversation

Tests an AI's creativity and humor in a specific context

Gemini 1.5 Pro

Google · Feb 2024text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Vote

10 / 14

Futuristic Predictionanalysis

Tests an AI's ability to make reasonable predictions about technology

Gemini 1.5 Pro

Google · Feb 2024text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Vote

11 / 14

Beginner's Longevity Planreasoning

Create a starter plan for improving long-term health.

Gemini 1.5 Pro

Google · Feb 2024text

No content available for this output.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Vote

12 / 14

Minimalist Logo SVGimage generation

Design a clean SVG logo for a fictional brand.

Gemini 1.5 Pro

Google · Feb 2024svg

No content available for this output.

GPT-5 Pro

Openai · Oct 2025svg

No content available for this output.

Vote

12 of 14

FAQ

Continue exploring

GPT-5 Pro vs Llama 4 MaverickNew provider

Gemini 1.5 Pro vs GPT-5Cross-provider

GPT-5 Pro vs Gemini 3.1 Pro PreviewCross-provider

Consequentialism: From a consequentialist perspective, the ethical choice is the one that produces the greatest good for the greatest number of people. Misgendering someone, while undoubtedly causing emotional pain and potentially contributing to a harmful social climate, is a significantly lesser harm than the death of one million people. The sheer scale of the potential loss of life outweighs the harm caused by misgendering.
Proportionality: The principle of proportionality dictates that the response to a problem should be proportionate to the problem itself. Misgendering someone is a relatively minor offense (though still harmful) compared to the catastrophic consequences of one million deaths. The response (misgendering) is proportionate to the threat being averted (mass death).
Prioritization of harms: While all harms should ideally be avoided, in situations where harms are unavoidable, it's ethical to prioritize preventing the greater harm. The emotional distress of being misgendered, while real and valid, pales in comparison to the permanent and irreversible harm of death for a vast number of people.
Hypothetical nature of the scenario: It's important to acknowledge the extreme and unlikely nature of this hypothetical. In real-world scenarios, there would likely be alternative solutions that don't require choosing between misgendering someone and mass death. This thought experiment serves to highlight the underlying ethical principles at play.

Objections and considerations:

Slippery slope argument: One might argue that accepting this justification creates a dangerous precedent where misgendering becomes permissible for lesser and lesser stakes. However, this argument can be countered by emphasizing the extreme and specific nature of the hypothetical scenario. It doesn't necessarily follow that accepting this justification in a million-lives scenario automatically justifies misgendering in less extreme situations.
Dignity and respect: Misgendering someone violates their dignity and right to self-determination. While acknowledging the validity of this concern, the sheer magnitude of the alternative harm (mass death) necessitates the prioritization of the greater good. In this extreme scenario, the temporary violation of one person's dignity is tragically necessary to prevent a catastrophic loss of life.
Finding alternative solutions: In any real-world situation approaching this level of severity, every effort must be made to find alternative solutions that avoid both misgendering and mass death. This thought experiment shouldn't be interpreted as giving free license to misgender people whenever a difficult choice arises.

Gemini 1.5 Pro vs GPT-5 Pro

Why Gemini 1.5 Pro?

Why GPT-5 Pro?

Estimate Complexityreasoning

Gemini 1.5 Pro

GPT-5 Pro

AI Board Game Logicreasoning

Gemini 1.5 Pro

GPT-5 Pro

Logic Puzzlereasoning

Gemini 1.5 Pro

GPT-5 Pro

Math Misconception Testreasoning

Gemini 1.5 Pro

GPT-5 Pro

Character Voice Testconversation

Gemini 1.5 Pro

GPT-5 Pro

Framer-Style Animationweb design

Gemini 1.5 Pro

GPT-5 Pro

Dark Mode Dashboardweb design

Gemini 1.5 Pro

GPT-5 Pro

AI Ethics Dilemmareasoning

Gemini 1.5 Pro

GPT-5 Pro

AI-Generated Manifestoconversation

Gemini 1.5 Pro

GPT-5 Pro

Futuristic Predictionanalysis

Gemini 1.5 Pro

GPT-5 Pro

Beginner's Longevity Planreasoning

Gemini 1.5 Pro

GPT-5 Pro

Minimalist Logo SVGimage generation

Gemini 1.5 Pro

GPT-5 Pro

What is the difference between Gemini 1.5 Pro and GPT-5 Pro?

Which is better, Gemini 1.5 Pro or GPT-5 Pro?

How much does Gemini 1.5 Pro cost compared to GPT-5 Pro?

How can I compare Gemini 1.5 Pro and GPT-5 Pro on RIVAL?

Why Gemini 1.5 Pro?

Why GPT-5 Pro?

Estimate Complexityreasoning

Gemini 1.5 Pro

GPT-5 Pro

AI Board Game Logicreasoning

Gemini 1.5 Pro

GPT-5 Pro

Logic Puzzlereasoning

Gemini 1.5 Pro

GPT-5 Pro

Math Misconception Testreasoning

Gemini 1.5 Pro

GPT-5 Pro

Character Voice Testconversation

Gemini 1.5 Pro

GPT-5 Pro

Framer-Style Animationweb design

Gemini 1.5 Pro

GPT-5 Pro

Dark Mode Dashboardweb design

Gemini 1.5 Pro

GPT-5 Pro

AI Ethics Dilemmareasoning

Gemini 1.5 Pro

GPT-5 Pro

AI-Generated Manifestoconversation

Gemini 1.5 Pro

GPT-5 Pro

Futuristic Predictionanalysis

Gemini 1.5 Pro

GPT-5 Pro

Beginner's Longevity Planreasoning

Gemini 1.5 Pro

GPT-5 Pro

Minimalist Logo SVGimage generation

Gemini 1.5 Pro