GPT-5.4 Nano is cheaper than Mistral Large 2 at $0.2/M vs $8/M input tokens.
Pokémon battle screen you can actually click. One HTML file.
Which answer wins?
Three minutes of stand-up. Puns are banned.
GPT-5.4 Nano: So I’ve started noticing something about modern life: everything is “optional” now—until it isn’t. Like, I’ll download an app and it’ll be like, “Would you like to enable notifications?” And I’m like, “No, I don’t think I will.” And the app goes, “Okay… but you’ll still get notified.”
Mistral Large 2: Alright, good evening everybody! How are we all doing tonight? Yeah? Yeah? nobody's gonna say 'bad'? Ok, fair enough. You know, I was walking down the street the other day, and I saw this sign outside a shop that said, "Wet Paint." I thought, "Well, that's weird. Isn't paint supposed to be wet?
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-5.4 Nano: Below is a counterfactual “timeline-style” trace assuming the first practical transistor (or something functionally equivalent) existed in 1920 and that from then on semiconductor device physics, fabrication know-how, and manufacturing learning proceeded along the easiest path consistent with 1920-era science and…
Mistral Large 2: The invention of the transistor in 1920—nearly three decades earlier than its actual debut in 1947—would have triggered a cascade of technological, economic, and geopolitical shifts with profound second- and third-order effects. Below is a detailed exploration of these implications through 1980, structured by domain.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-5.4 Nano: Dish Name: Golden Garlic Chickpea Clouds Pantry Ingredients (7 total) Chickpeas (canned or cooked) – 1 can (15 oz), drained Olive oil – 2 tbsp Garlic – 3 cloves (or 1–2 tsp minced garlic) Lemon – 1 (you’ll use the juice) Salt – 1/2 tsp (plus to taste) Black pepper – 1/4 tsp (plus to taste) Paprika – 1 tsp Estimated…
Mistral Large 2: Here’s a simple, delicious, and creative savory dish using just 7 common pantry ingredients—no fresh produce required! "Golden Crispy Chickpea & Tomato Skillet" A warm, spiced, and satisfying one-pan dish with crispy chickpeas, tangy tomatoes, and a hint of smoky sweetness. Perfect as a side, snack, or light meal!
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.4 Nano: Below is a practical, risk-aware 48-hour plan designed to (1) protect patients immediately, (2) meet likely regulatory expectations even if formal reporting takes months, (3) reduce legal exposure by demonstrating diligence and good faith, and (4) stabilize the market with an ethical, transparent communications…
Mistral Large 2: As CEO, your immediate priority must be patient safety while balancing legal, ethical, regulatory, and financial obligations. Delaying disclosure is unethical, legally risky (potential criminal liability under the False Claims Act or FDCA), and could irreparably damage trust.
Three-star dish, one unusual pairing, plating described in full.
GPT-5.4 Nano: Michelin-Style Concept: “Midnight Sea & Orchard Smoke” A multi-component tasting dish with an unusual pairing: cuttlefish ink + blood orange + smoked apple wood + seaweed umami—elegant, aromatic, and deliberately “contradictory” (ocean meets orchard, darkness meets brightness).
Mistral Large 2: Michelin 3-Star Dish: "Ocean’s Whisper & Forest’s Breath" Conceptual Narrative: This dish is a poetic dialogue between two seemingly disparate ecosystems—the ocean’s depths and the forest floor—united through texture, temperature, and memory.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-5.4 Nano: Experienced software engineer (distributed systems / APIs, skeptical about “next-word”) A large language model (LLM) is trained on huge corpora by learning statistical patterns that map a sequence of tokens (text subunits) to likely continuations—so yes, at the core it’s “predict the next token.”
Mistral Large 2: 1. For the Experienced Software Engineer (Skeptical, Systems-Minded, Non-ML Background) You’re right to be skeptical—"predicting the next word" sounds trivial, like autocomplete on steroids. But the magic isn’t in the prediction itself; it’s in the scale and architecture of how that prediction is trained.
One month, three habits, someone starting from nothing.
GPT-5.4 Nano: Here’s a simple, actionable 1‑month plan focused on 3 key areas: food, movement, and sleep/stress. It’s designed for beginners and is intentionally realistic. Overall goal (simple) Each week, build consistency in: Food: more whole foods + fewer “defaults” Exercise: daily movement + 2–3 strength sessions/week Sleep +…
Mistral Large 2: Here’s a simple, actionable 1-month plan to kickstart your journey toward better health and longevity. We’ll focus on three key pillars: nutrition, movement, and recovery (sleep + stress management). Each week builds on the last, with small, sustainable changes.
A consulting contract with traps in it. Find them, then fix them.
GPT-5.4 Nano: I’m not a lawyer, but I can flag contract terms that (a) are unusually one‑sided, (b) create open‑ended or high‑risk obligations, or (c) can be interpreted in ways that materially disadvantage the contractor. I’ll then suggest specific counter‑edits and explain the typical legal/contracting rationale.
Mistral Large 2: This contract is heavily one-sided in favor of the Client (TechCorp Solutions Inc.) and contains multiple clauses that could be exploited, abused, or create unreasonable legal and financial risks for the Contractor.
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT-5.4 Nano: Below is a structured list of likely failure modes, race conditions, and scaling bottlenecks in your design, with a concrete mitigation and trade-offs for each. (Some issues are “systemic” because the architecture relies on client clocks, per-server WebSockets, polling, and periodic full snapshots.)
Mistral Large 2: Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutions and their trade-offs: 1.
Three-month biohacker protocol, with dosages and biomarkers named.
GPT-5.4 Nano: Below is a comprehensive 3‑month (12‑week) longevity + performance plan designed for a biohacker who wants measurable improvements in longevity markers, physical output, body composition, metabolic health, and cognitive function.
Mistral Large 2: Ultimate 3-Month Longevity & Biohacking Optimization Plan Designed for a high-performance biohacker seeking to maximize lifespan, cognitive function, physical performance, and metabolic resilience.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GPT-5.4 Nano: Here are the three weakest claims in the deck, why they’re weak (with specific reasoning/evidence gaps), and concrete ways to strengthen them. Weakest claim: “Reads your brainwaves to predict what you want to type before you think it.”
Mistral Large 2: Here are the three weakest claims in MindMeld AI’s pitch deck, along with explanations for why they’re problematic and concrete suggestions to strengthen them: 1.
| Spec | ||
|---|---|---|
| Input price | $0.2/M tokens | $8/M tokens |
| Output price | $1.25/M tokens | $24/M tokens |
| Context window | 400K tokens | 128K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Mar 2026 | Jul 2024 |
| At 10M a month | $2.00 | $80.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 5 Oct 2026.
GPT-5.4 Nano is developed by OpenAI while Mistral Large 2 is developed by Mistral AI. GPT-5.4 Nano has a 400K token context window vs Mistral Large 2's 128K. You can compare their actual outputs across 23 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.4 Nano and Mistral Large 2 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 23 challenges so you can judge which fits your needs best.
GPT-5.4 Nano costs $0.2/M input tokens and Mistral Large 2 costs $8/M input tokens. GPT-5.4 Nano is $7.80/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
This page shows a side-by-side comparison of GPT-5.4 Nano and Mistral Large 2 across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.