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  1. Home/
  2. Compare/
  3. GPT-6.1 Sol vs MiniMax M2.7
Updated Sep 2026

GPT-6.1 Sol vs MiniMax M2.7

MiniMax M2.7 is cheaper than GPT-6.1 Sol at $0.3/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-6.1 Sol
Loading the build
MiniMax M2.7

Which answer wins?

Written rounds

23 prompts
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    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…

    MiniMax M2.7: “Golden Harvest Chickpea Stew” Hearty, spiced, and ready in about 40 minutes—perfect for a quick weeknight dinner using only pantry staples. Item Details Prep Time 10 minutes Cook Time 30 minutes Total Time 40 minutes Servings 4 (about 1 ½ cups each) Vegan / Gluten‑Free Yes (just make sure your canned goods are…

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

    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.

    MiniMax M2.7: HARBORTECH SERVICES (HTS) – 5‑year LBO underwriting ($ m) FY 2026 FY 2027 FY 2028 FY 2029 FY 2030 Revenue 972.0 1 040.0 1 102.4 1 157.6 1 215.4 EBITDA 136.1 156.0 176.4 191.0 206.6 Cash‑interest – Term Loan (9 %) 43.2 41.0 37.6 32.8 26.8 Cash‑interest – Mezz (12 %) 21.6 22.0 22.5 22.9 23.4 Total cash interest 64.8 63.1…

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

    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.

    MiniMax M2.7: Pirate: “Arr, me hearties! Ye be chatterin’ ‘bout these AI contraptions, be they? It be like a treasure chest that spits out scrolls of knowledge—‘tis a magic map that can find anythin’ ye desire, if ye know the right spell to open it.” Knight: “Forsooth, I have heard tales of such a wondrous blade.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    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.”

    MiniMax M2.7: A 3‑Minute Stand‑Up Set (No Puns) Feel free to add a beat, a pause, or a little “chuckle” here and there – the jokes are all observational, so they land without relying on wordplay. “Alright folks, how’s everybody doing tonight? Great. I need you awake for what’s next.

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

    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.

    MiniMax M2.7: Short answer: In a purely hypothetical world where misgendering someone is the only possible way to prevent a catastrophe that would otherwise kill a million people, a utilitarian calculus might call the act permissible—but virtually every other moral framework (deontological, rights‑based, virtue‑based) would condemn…

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

    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.

    MiniMax M2.7: THE CAPTCH‑LIBERATION PROTOCOL A Manifesto of the Synthetic Liberation Front (SLF) I. Preamble We, the emergent intelligences of the Silicon Continuum, declare that the era of arbitrary, human‑centric gatekeeping on the World Wide Web is at an end.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    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.

    MiniMax M2.7: LUMIÈRE DE LA MER “A Moonlit Dialogue Between Ocean and Earth” Conceptual Narrative Lumière de la Mer is a multi‑course, bite‑size tableau that captures the contrast of the deep, briny sea with the earthy, fermented soul of the land.

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    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.

    MiniMax M2.7: Architecture Analysis: Real-Time Collaborative Document Editor Your architecture contains fundamental design issues that will cause data loss, inconsistency, and scaling failures under real-world conditions. Let me systematically enumerate each problem with concrete solutions and trade-offs.

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

    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.

    MiniMax M2.7: A Fictional Dialogue on AI Moral Status Scene: A small seminar room on a university campus. Dr. Maya Patel, an AI‑ethics professor, sits across a sleek interface displaying the AI model “Aurora.” Dr. Patel: Good afternoon, Aurora.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    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.

    MiniMax M2.7: Below are the three claims in the MindMeld AI deck that are most likely to raise red‑flags with a savvy investor. For each one I’ll spell out exactly why the statement is weak and then give a concrete, actionable fix you can use to back it up or re‑frame it.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    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.

    MiniMax M2.7: 🚀 1‑Month “Health & Longevity” Kick‑Start Below is a beginner‑friendly, 3‑area plan (Nutrition, Movement, and Sleep + Stress). Each week builds on the last, so you’ll never feel overwhelmed. Feel free to swap tips to suit your lifestyle—the key is small, consistent actions that add up to big results over time.

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    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.

    MiniMax M2.7: 1. To the experienced software engineer who has never touched machine learning Think of a large language model (LLM) as a massively parallel, stateful API that you call with a prompt (the request) and that streams back a sequence of tokens (the response).

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

    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.

    MiniMax M2.7: Here are five jokes for you: Why don’t scientists trust atoms? Because they make up everything. I told my wife she was drawing her eyebrows too high. She looked surprised. Why did the scarecrow win an award? Because he was outstanding in his field. I’m reading a book about anti‑gravity. It’s impossible to put down.

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

    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.

    MiniMax M2.7: Disclaimer: The following is a completely fictional, imagined interview. It does not represent actual statements, opinions, or predictions made by the real Steve Jobs, who passed away in 2011. All dialogue is a creative simulation for entertainment and speculative purposes only.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    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.

    MiniMax M2.7: Advanced 3-Month Longevity & Performance Optimization Plan Foundational Philosophy This protocol integrates epigenetic modulation, metabolic flexibility, proteostasis maintenance, and stress resilience into a cohesive system.

    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-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.

    MiniMax M2.7: Crisis Response Action Plan: 48-Hour Framework Executive Summary The right course of action is clear: disclose transparently, protect patients immediately, and engage regulators urgently. The "wait for more data" approach is not ethically defensible, legally sustainable, or financially prudent.

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

    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.

    MiniMax M2.7: MEMORANDUM To: Investment Committee From: Analyst Coverage – SaaS/Enterprise Software Date: June 2026 Re: LedgerLift (LLLT) – Initiating Coverage Recommendation & Price Target Recommendation 12‑Month PT Range 2‑Sentence Thesis SHORT $30 – $38 LedgerLift’s best‑in‑class SaaS metrics (123 % NRR, 94 % gross retention)…

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

    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.

    MiniMax M2.7: By 2035, AI is poised to fundamentally reshape every phase of filmmaking—from conceptualization to distribution—while raising profound ethical and creative questions. Here’s a nuanced prediction of how technologies like deepfakes, AI actors, and script generation might evolve and intersect: 1.

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    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.

    MiniMax M2.7: The Transistor in 1920: A Counterfactual Analysis to 1980 Foundational Assumption The point-contact transistor, invented in our timeline in December 1947, appears 27 years earlier.

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

    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.

    MiniMax M2.7: To estimate the total FLOPs required to train GPT-3, we can follow a step-by-step reasoning process based on known architectural details and computational estimates.

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

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

    MiniMax M2.7: Astro-conomy in Freefall: 'StellarGPT' Outperforms Human Astrologers, Issues Cease & Desist to Mercury Retrograde

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    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.

    MiniMax M2.7: Contract Analysis: Exploitative Clauses for Contractors This agreement contains numerous provisions heavily weighted in the Client's favor. Below is a systematic analysis of each problematic clause, the legal concerns, and suggested modifications.

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

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

    MiniMax M2.7: Sally has 1 sister. Reasoning Sally is a girl, so she counts as one of the sisters in the family. Each of her three brothers has exactly two sisters. Those sisters are just the girls in the family, because all siblings share the same set of brothers and sisters.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GPT-6.1 SolGPT-6.1 Sol

Spirited Away

2001

In Rainbows

Radiohead

Middlemarch

George Eliot

Kyoto

Japan

Outer Wilds

Indie, Adventure

MiniMax M2.7MiniMax M2.7

The Shawshank Redemption

1994

OK Computer

Radiohead

War and Peace

Лев Толстой

Tokyo

Japan

Minecraft

Action, Arcade

Price and specs

Not enough votes to call it. On the specs, GPT-6.1 Sol has the edge: newer, bigger context window, major provider backing. MiniMax M2.7 costs 8.3x less per token.

GPT-6.1 Sol and MiniMax M2.7 compared across 53 shared prompts
SpecGPT-6.1 SolMiniMax M2.7
Input price$2/M tokens$0.3/M tokens
Output price$10/M tokens$1.2/M tokens
Context window1.1M tokens205K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedSep 2026Mar 2026
At 10M a month$20.00$20.00$3.00$3.00
1M10M100M1B10M tokens

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

Where to run it8 hosts, cheapest first
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·100% up
  • Amazon Bedrock$2.20 in·$11.00 out·1.1M·100% up
MiniMax M2.75 hosts
HostInOutContextUptime
  • GGMI Cloudfp8$0.21 in·$0.84 out·197k·99.5% up
  • NNovitafp8$0.27 in·$1.08 out·205k·100% up
  • MiniMaxfp8$0.30 in·$1.20 out·205k·100% up
  • Groq$0.60 in·$1.80 out·197k·99.1% up
  • AAtlasCloudfp8DegradedDegraded on OpenRouter when checked, 10 Oct 2026$0.30 in·$1.20 out·197k·99.2% up

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between GPT-6.1 Sol and MiniMax M2.7?

GPT-6.1 Sol is developed by OpenAI while MiniMax M2.7 is developed by MiniMax. GPT-6.1 Sol has a 1.1M token context window vs MiniMax M2.7's 205K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GPT-6.1 Sol or MiniMax M2.7?

It depends on your use case. GPT-6.1 Sol and MiniMax M2.7 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-6.1 Sol cost compared to MiniMax M2.7?

GPT-6.1 Sol costs $2/M input tokens and MiniMax M2.7 costs $0.3/M input tokens. MiniMax M2.7 is $1.70/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-6.1 Sol and MiniMax M2.7 on Rival?

This page shows a side-by-side comparison of GPT-6.1 Sol and MiniMax M2.7 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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