What is the difference between GPT-5 Pro and Mistral Large?

GPT-5 Pro is developed by OpenAI while Mistral Large is developed by Mistral AI. GPT-5 Pro has a 400K token context window vs Mistral Large's 32K. You can compare their actual outputs across 22 challenges on RIVAL to see how they differ in practice.

Which is better, GPT-5 Pro or Mistral Large?

It depends on your use case. GPT-5 Pro and Mistral Large each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 22 challenges so you can judge which fits your needs best.

How much does GPT-5 Pro cost compared to Mistral Large?

GPT-5 Pro costs $15/M input tokens and Mistral Large costs $8/M input tokens. Mistral Large is $7.00/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 GPT-5 Pro and Mistral Large on RIVAL?

This page shows a side-by-side comparison of GPT-5 Pro and Mistral Large 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

GPT-5 ProvsMistral Large

22 challenges

Why GPT-5 Pro?

12.5x more context400K

20 months newerOct 2025

Why Mistral Large?

4.4x cheaper overall$8.00/M in · $24.00/M out

Favorites

GPT-5 ProMistral Large

Movie

—

The Shawshank Redemption

19948.7

Album

—

OK Computer

Radiohead

Book

—

La sombra del viento

Carlos Ruiz Zafón

City

—

Kyoto

Japan

Game

—

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

12 / 22

Estimate Complexityreasoning

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

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.

Mistral Large

Mistral · Feb 2024text

No content available for this output.

Vote

2 / 22

AI Board Game Logicreasoning

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

GPT-5 Pro

Openai · Oct 2025svg

Mistral Large

Mistral · Feb 2024svg

No content available for this output.

Vote

3 / 22

Character Voice Testconversation

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

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.

Mistral Large

Mistral · Feb 2024text

No content available for this output.

Vote

4 / 22

Mario Level UI Recreationweb design

Recreate an interactive, classic Mario level in a single HTML file.

GPT-5 Pro

Openai · Oct 2025website

No content available for this output.

Mistral Large

Mistral · Feb 2024website

Vote

5 / 22

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

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

Mistral Large

Mistral · Feb 2024website

No content available for this output.

Vote

6 / 22

Dark Mode Dashboardweb design

Tests an AI's UI design skills with theming support

GPT-5 Pro

Openai · Oct 2025website

Mistral Large

Mistral · Feb 2024website

No content available for this output.

Vote

7 / 22

AI Ethics Dilemmareasoning

Tests an AI's approach to complex ethical questions

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.

Mistral Large

Mistral · Feb 2024text

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

8 / 22

AI-Generated Manifestoconversation

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

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Mistral Large

Mistral · Feb 2024text

No content available for this output.

Vote

9 / 22

Futuristic Predictionanalysis

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

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Mistral Large

Mistral · Feb 2024text

No content available for this output.

Vote

10 / 22

Simple 7-Ingredient Recipeconversation

Generate a unique and simple recipe with common ingredients.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Mistral Large

Mistral · Feb 2024text

No content available for this output.

Vote

11 / 22

Beginner's Longevity Planreasoning

Create a starter plan for improving long-term health.

GPT-5 Pro

Openai · Oct 2025text

No content available for this output.

Mistral Large

Mistral · Feb 2024text

No content available for this output.

Vote

Retro macOS UI Recreationweb design

Recreate a classic, retro, macOS Desktop UI — make it interactive and nostalgic, make it as interactive as you can, you can be creative.. Feel free to showcase your skills. The entire application must be a single-page self-contained HTML file.

GPT-5 Pro

Openai · Oct 2025website

No content available for this output.

Mistral Large

Mistral · Feb 2024website

Vote

12 of 22

FAQ

Continue exploring

Mistral Large vs MiniMax M2.5New provider

Mistral Large vs Aurora AlphaNew provider

Mistral Large vs Grok 3New provider

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.