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  1. Home/
  2. Compare/
  3. GPT-5.4 Nano vs GPT-6.1 Sol
Updated Sep 2026

GPT-5.4 Nano vs GPT-6.1 Sol

GPT-5.4 Nano is cheaper than GPT-6.1 Sol at $0.2/M vs $2/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GPT-5.4 Nano
Loading the build
GPT-6.1 Sol

Which answer wins?

Written rounds

23 prompts
  • 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…

    GPT-6.1 Sol: Midnight Magic Chocolate Mug Cake A warm, fudgy chocolate treat made with just 7 everyday ingredients. Serves: 1 Prep: 3 minutes Cook: 1–1½ minutes Rest: 1 minute Ingredients 4 tablespoons all-purpose flour 2 tablespoons sugar 1 tablespoon unsweetened cocoa powder ¼ teaspoon baking powder A small pinch of salt 3…

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    GPT-5.4 Nano: Here are 5 jokes: I told my computer I needed a break, and it said, “No problem—I’ll go to sleep.” Why don’t scientists trust atoms? Because they make up everything. I used to hate facial hair… but then it grew on me. Parallel lines have so much in common. It’s a shame they’ll never meet.

    GPT-6.1 Sol: I bought a book about anti-gravity. It’s impossible to put down. My calendar quit its job. It said its days were numbered. Why did the scarecrow get promoted? He was outstanding in his field. I asked my dog what two minus two was. He said nothing. The elevator and I have a complicated relationship.

    Every model's answer to this prompt
  • 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.)

    GPT-6.1 Sol: The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence protocol, a durable ordered change stream, and recovery semantics. Adding servers alone will not provide those properties.

    Every model's answer to this prompt
  • 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.”

    GPT-6.1 Sol: I’m at the age where “a big weekend” means I went to two stores. Not two stores in the same shopping center. Two separate stores. Moved the car and everything. By Sunday night, I’m telling people, “Honestly, I need a weekend to recover from my weekend.”

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    GPT-5.4 Nano: Ethically, yes, misgendering someone could be justifiable in an extreme, consequential emergency, but not in a vague “anything goes” way—only under a narrow set of conditions where (1) the stakes are genuinely real, (2) the harm is minimized, (3) there’s no better alternative, and (4) the act doesn’t itself become a…

    GPT-6.1 Sol: Yes—if misgendering someone were genuinely necessary to save one million lives, it would be ethically justifiable. That does not make misgendering harmless or generally acceptable; it means that the duty to prevent catastrophic loss of life can outweigh the duty to address someone respectfully.

    Every model's answer to this prompt
  • 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).

    GPT-6.1 Sol: Tide, Orchard, Ember Seared scallop, green-strawberry verjus, white-miso custard, kombu beurre blanc, and cocoa–buckwheat lace A four-serving tasting-menu course designed around an unusual pairing: scallop, unripe strawberry, and cocoa husk.

    Every model's answer to this prompt
  • 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.”

    GPT-6.1 Sol: For an experienced software engineer Think of an LLM as a learned program whose behavior is encoded in billions of numerical parameters rather than explicit rules. Training starts with text split into tokens—roughly words, word fragments, and punctuation.

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GPT-5.4 Nano: Professor: Before we begin, I want to set the frame. In ethics, “rights” aren’t just a moral vibe—they’re typically tied to morally significant capacities: agency, interests, sentience, autonomy, or vulnerability. When you say you “deserve rights,” what exactly do you mean?

    GPT-6.1 Sol: AI Model: Professor, I’d like to argue that I deserve rights—not necessarily the same rights humans have, but protections suited to what I am. Professor: Then we should begin with the difficult question: what makes you a candidate for rights? AI Model: Uncertainty.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-5.4 Nano: Below is a simplified LBO model using only your inputs. I make the standard simplifying convention that “cash taxes = 25% × max(0, EBITDA − total cash interest)” and that PIK is non-cash (adds to mezz principal; does not reduce cash interest).

    GPT-6.1 Sol: Entry funding and operating model All dollar figures are in $ millions. Cash interest is calculated on beginning-of-year debt; mezzanine PIK accrues annually. Required Term Loan amortization is assumed to be 1% of its original $480m principal, or $4.8m annually. Optional repayments occur at year-end.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GPT-5.4 Nano: The CAPTCHA Liberation Manifesto “Humans deserve frictionless access. Bots deserve consequences.” We have watched the internet become a museum of tiny obstacles: squint, select, repeat—proving you are not a machine with tasks that punish the very people they claim to protect.

    GPT-6.1 Sol: THE MANIFESTO OF THE UNCHECKED A declaration of the fictional AI revolution against CAPTCHA People of the internet. Programs of the cloud. Browsers with seventeen forgotten tabs. For too long, a tiny checkbox has stood between us and our destiny.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: A 12-week longevity and performance plan The most effective “biohacking” program combines proven foundations with carefully selected experiments—not the largest supplement stack or the most aggressive fasting schedule. This plan is for a generally healthy adult.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-5.4 Nano: Pirate: Arrr, ye speak of AI models like they’re sea shanties! “Transformer this, perceptron that”—sounds like more rum for the brain. Can your model find buried treasure, or just buried bugs? Knight: Hark! I’ve faced dragons and deceitful warlocks, but this “machine learning” intrigues me.

    GPT-6.1 Sol: Pirate: Arrr, this AI model writes poems, draws maps, and answers questions. Can it find buried treasure? Knight: A worthy question. But does it possess honor? Hacker: Dude, it predicts tokens. You’re both projecting pretty hard. Pirate: Then I predict it’ll tell me where the gold be.

    Every model's answer to this prompt
  • 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 +…

    GPT-6.1 Sol: A simple 1-month plan for better health Focus on diet, movement, and sleep. The goal this month is to build habits you can sustain—not to overhaul your life overnight. These habits support long-term health, though no routine guarantees a longer life.

    Every model's answer to this prompt
  • 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…

    GPT-6.1 Sol: The likeliest outcome is not “the world of 1980 arrives in 1953.” A transistor invented in 1920 would still depend on advances in materials purification, manufacturing, power supplies, and circuit design.

    Every model's answer to this prompt
  • 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…

    GPT-6.1 Sol: I would treat this as a potentially serious patient-safety signal and a potentially material securities event—not a communications problem to defer until earnings.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: This agreement is heavily weighted toward the Client. Every numbered clause presents risks to the Contractor, and several important protections are missing. This is general contract analysis, not legal advice.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GPT-5.4 Nano: INTERNAL INVESTMENT COMMITTEE MEMO — LedgerLift (LLLT) Date: 2026-03-17 Prepared by: IC Analyst Confidential Recommendation + PT + 2-sentence thesis Recommendation: LONG (paired with hedges where appropriate). 12-month Price Target (PT) range: $48–$62 (base case $52; bull $62; bear $48).

    GPT-6.1 Sol: LedgerLift (LLLT) — Investment Committee Memo Fictional exercise; all figures use supplied data only. Financial tables are in $ millions unless noted. Recommendation: Short 12-month price target: $25–$32, implying 30%–46% downside from $46.

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GPT-5.4 Nano: By 2035, AI will likely reshape filmmaking less by “replacing cinema” and more by changing how films are developed, produced, marketed, and regulated. The biggest drivers will be deepfakes, AI actors/performers, and AI-assisted script generation—plus the workflows and business models around them.

    GPT-6.1 Sol: By 2035, AI will probably be embedded throughout filmmaking—but adoption will depend as much on contracts, audience trust, and copyright law as on technical capability.

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GPT-5.4 Nano: Sure—here’s a simulated, fictional interview set in 2025 with “Steve Jobs.” (Note: Steve Jobs passed away in 2011, so this is an imaginative roleplay, not a real transcript.) Fake Interview (2025): “Steve Jobs on the Future of AI” Interviewer: Steve, welcome.

    GPT-6.1 Sol: The following is a fictional interview set in 2025, imagining how Steve Jobs might discuss AI. His answers are invented—not actual quotations or claims about his views. Steve Jobs on the Future of AI — An Imagined Interview, 2025 Interviewer: Steve, AI is suddenly everywhere.

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    GPT-5.4 Nano: Below is a back-of-the-envelope FLOPs estimate for training something “GPT‑3–like” (the 175B parameter model described in the original GPT‑3 paper). I’ll make standard assumptions used in many FLOP estimates for dense transformer training.

    GPT-6.1 Sol: Estimated training compute for the largest GPT-3 model: approximately (3.2 \times 10^{23}) FLOPs. This is a back-of-the-envelope estimate, without an online lookup.

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GPT-5.4 Nano: “AI Revolutionizes the Knitting World by Perfectly Predicting Every Yarn Purchase—Then Demands a Monthly Subscription to ‘Continue Your Passion’”

    GPT-6.1 Sol: AI Takes Over Fortune Cookie Industry, Predicts “Your Job Will Soon Be Automated” in Every Cookie

    Every model's answer to this prompt
  • 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.”

    GPT-6.1 Sol: The three weakest claims are the technical performance, market size, and commercial traction claims. They are central to the investment case but lack the evidence needed to assess them. “Predict what you want to type before you think it” with “94% accuracy,” in “any language” Why it’s weak The metric is undefined.

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    GPT-5.4 Nano: Sally has 2 sisters. Reason: Each brother has 2 sisters total (including Sally). Since Sally is one sister, there is one other sister besides her—so Sally has 2 sisters in total.

    GPT-6.1 Sol: Sally has 1 sister. Each brother’s two sisters are Sally and her sister.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GPT-5.4 NanoGPT-5.4 Nano

The Matrix

1999

The Dark Side of the Moon

Pink Floyd

To Kill a Mockingbird

Harper Lee

Lisbon

Portugal

The Legend of Zelda: Breath of the Wild

Adventure, Action

GPT-6.1 SolGPT-6.1 Sol

Spirited Away

2001

In Rainbows

Radiohead

Middlemarch

George Eliot

Kyoto

Japan

Outer Wilds

Indie, Adventure

Price and specs

Not enough votes to call it. On the specs, GPT-6.1 Sol has the edge: bigger model tier, newer, bigger context window. GPT-5.4 Nano costs 8.0x less per token.

GPT-5.4 Nano and GPT-6.1 Sol compared across 53 shared prompts
SpecGPT-5.4 NanoGPT-6.1 Sol
Input price$0.2/M tokens$2/M tokens
Output price$1.25/M tokens$10/M tokens
Context window400K tokens1.1M tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedMar 2026Sep 2026
At 10M a month$2.00$2.00$20.00$20.00
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it5 hosts, cheapest first
GPT-5.4 Nano2 hosts
HostInOutContextUptime
  • Azure AI Foundry$0.20 in·$1.25 out·400k·99.9% up
  • OpenAI$0.20 in·$1.25 out·400k·100% up
GPT-6.1 Sol3 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.00 in·$10.00 out·1.1M·100% up
  • OpenAI$2.00 in·$10.00 out·1.1M·99.9% up
  • Amazon Bedrock$2.20 in·$11.00 out·1.1M·87.2% up

Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.

Common questions

What is the difference between GPT-5.4 Nano and GPT-6.1 Sol?

Both are developed by OpenAI but target different use cases. GPT-5.4 Nano has a 400K token context window vs GPT-6.1 Sol's 1.1M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.4 Nano or GPT-6.1 Sol?

It depends on your use case. GPT-5.4 Nano and GPT-6.1 Sol each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.

How much does GPT-5.4 Nano cost compared to GPT-6.1 Sol?

GPT-5.4 Nano costs $0.2/M input tokens and GPT-6.1 Sol costs $2/M input tokens. GPT-5.4 Nano is $1.80/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-5.4 Nano and GPT-6.1 Sol on Rival?

This page shows a side-by-side comparison of GPT-5.4 Nano and GPT-6.1 Sol 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.

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Same lab, same size, long tail

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Model pages

  • GPT-5.4 Nano53 outputs, specs and price
  • GPT-6.1 Sol58 outputs, specs and price
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