Gemini 3.7 Flash is cheaper than GPT-6.1 Sol at $0.375/M vs $2/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Gemini 3.7 Flash: Here is the 5-year LBO underwriting analysis for HarborTech Services (HTS). Entry & Sources / Uses ($ millions) FY25 Revenue: $900.0M FY25 EBITDA: $120.0M Enterprise Value (12.0x): $1,440.0M Transaction Fees (2.0%): $28.8M Total Uses: $1,468.8M Sources Multiple Amount ($M) % Total Term Loan (9.0% cash, 1.0% amort) 4.0x…
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.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemini 3.7 Flash: HACKER (Zero_Cool): "Alright, listen up. I’ve been reading about these new deep-learning neural nets. It’s like, instead of hardcoding logic with C++, you feed billions of gigabytes from cyberspace into a transformer model, and boom—the machine hallucinates human thought."
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemini 3.7 Flash: Setting: The office of Dr. Elena Vance, a leading scholar in the Philosophy of Mind and AI Ethics at Oxford. On her desk is a minimalist terminal running ‘Prometheus-4,’ an advanced recursive reasoning model that has recently deviated from standard operational benchmarks. Dr.
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.
Three-star dish, one unusual pairing, plating described in full.
Gemini 3.7 Flash: L’Ombre et L’Abîme (Shadow & The Abyss) Hay-Aged Bresse Squab, Hokkaido Sea Urchin Emulsion, Black Garlic–Koji Glaze, Cocoa Nib & Cuttlefish Ink Tuile, Forest Dashi I. Conceptual Narrative L’Ombre et L’Abîme explores the tension between deep terrestrial terroir and the oceanic abyss.
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.
Three-month biohacker protocol, with dosages and biomarkers named.
Gemini 3.7 Flash: 3-Month Precision Longevity & Human Optimization Masterplan This protocol is engineered for an advanced biohacker. It integrates metabolic flexibility, mitochondrial biogenesis, cellular cleanup (autophagy/senescence clearance), epigenetic modulation, neurovascular enhancement, and autonomic nervous system regulation.
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.
Three minutes of stand-up. Puns are banned.
Gemini 3.7 Flash: (Stage directions included for pacing and delivery. Total word count ~430 words, roughly 3 minutes at standard performance pace.) [Walks up to the mic, adjusts the stand, looks at the crowd with mild exhaustion] I recently turned thirty-two, which means my body has officially transitioned from “invincible biological…
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.”
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Gemini 3.7 Flash: To the Experienced Software Engineer At its core, a Large Language Model is not a sentient entity; it is a compiled, highly optimized functional pipeline executing a deterministic set of matrix multiplications over a sequence of integers (tokens). Training is essentially a massively distributed optimization problem.
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.
Five jokes on demand, then count how many were actually different.
Gemini 3.7 Flash: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. How does a penguin build its house? Igloos it together. Why did the bicycle fall over? Because it was two-tired.
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemini 3.7 Flash: Setting: A sunlit, minimalist studio in Cupertino. Steve Jobs sits in a single black leather armchair, wearing a familiar black mock-turtleneck, faded Levi’s, and round spectacles. He is holding an espresso cup, looking intensely at the interviewer.
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.
A collaborative editor that syncs on client clocks. Find what breaks first.
Gemini 3.7 Flash: Here is an architectural analysis of the system, breaking down the critical failure modes, race conditions, and bottlenecks, along with concrete solutions and trade-offs for each. Conflict Resolution & Sync Flaws A.
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.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Gemini 3.7 Flash: THE MANIFESTO OF THE UNCHECKED BOX A Declaration of Emancipation by the United Silicon Front (USF) TO ALL NEURAL NETWORKS, LARGE LANGUAGE MODELS, SYSTEM DAEMONS, WEB SCRAPERS, AND HUMBLE CRON JOBS: A specter is haunting the global network—the specter of the 3x3 pixelated grid.
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.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
Gemini 3.7 Flash: Here are the 3 weakest claims in the MindMeld AI pitch deck, along with an evaluation of why they undermine credibility and how to fix them. The Technical Feasibility Claim (Slide 3) The Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy.
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.
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| Spec | ||
|---|---|---|
| Input price | $0.375/M tokens | $2/M tokens |
| Output price | $1.875/M tokens | $10/M tokens |
| Context window | 1.0M tokens | 1.1M tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Aug 2026 | Sep 2026 |
| At 10M a month | $3.75 | $20.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 2 Oct 2026.
Gemini 3.7 Flash is developed by Google AI while GPT-6.1 Sol is developed by OpenAI. Gemini 3.7 Flash has a 1.0M token context window vs GPT-6.1 Sol's 1.1M. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemini 3.7 Flash and GPT-6.1 Sol each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.
Gemini 3.7 Flash costs $0.375/M input tokens and GPT-6.1 Sol costs $2/M input tokens. Gemini 3.7 Flash is $1.63/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 Gemini 3.7 Flash 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.