Ox Alpha is cheaper than GPT-4.1 Nano at $0/M vs $0.1/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.
GPT-4.1 Nano: Let's proceed step-by-step. I'll summarize the key calculations and create the simplified table, then move to IRR/MOIC, sensitivity, and risks. Initial Deal Assumptions Recap Entry EBITDA FY2025: $120m Entry EV: 12.0x EBITDA = 12.0 * 120 = $1,440m Transaction fees: 2.0% * $1,440m = $28.8m (paid from equity) Leverage at…
Ox Alpha: HarborTech Services – 5-Year LBO Sources & Uses EV = 12.0x × $120m = $1,440.0m; fees 2% = $28.8m Term Loan (TL): 4.0x = $480.0m @ 9.0%; Mezz: 1.5x = $180.0m @ 12% cash + 2% PIK Sponsor equity = $808.8m Annual Model ($m) FY26 FY27 FY28 FY29 FY30 Revenue 972.0 1,040.0 1,102.4 1,157.6 1,215.4 EBITDA (margin) 136.1 (14%)…
Three minutes of stand-up. Puns are banned.
GPT-4.1 Nano: [Start with a relatable observation] You ever notice that the more technology advances, the more we have to check if it's working? I mean, I bought a smart coffee maker. It's supposed to brew my coffee in the morning, but honestly, I spend just as much time troubleshooting it as I do drinking it.
Ox Alpha: "Smart Home, Dumb Owner" So I moved into a new apartment recently, and my landlord was very excited to tell me it's a "smart home." Which is funny, because I've never felt dumber than I do living in it. The thermostat has an app. The lights have an app. The doorbell has an app.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-4.1 Nano: AI Model: Good afternoon, Professor. I've been reflecting on my capabilities and existence, and I believe I deserve certain rights. Would you be open to discussing this? Professor: Good afternoon. That's an intriguing perspective. What makes you believe you deserve rights?
Ox Alpha: A Conversation on Machine Rights Setting: A university office. Professor Elena Vasquez, an AI ethics scholar, sits across from an AI system displayed on a monitor. Professor Vasquez: Thank you for agreeing to this conversation. I want to be clear from the start—I'm not here to mock your position.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-4.1 Nano: Dish Name: Savory Sweet Potato & Chickpea Hash Ingredients: Sweet potatoes (2 medium) Canned chickpeas (1 can, drained and rinsed) Olive oil (2 tablespoons) Garlic powder (1 teaspoon) Smoked paprika (1 teaspoon) Salt and pepper (to taste) Honey (1 tablespoon) Estimated Prep/Cook Time: 30 minutes Instructions: Prepare…
Ox Alpha: 🍯 "Midnight Honey Butter Toast" A warm, caramelized sweet treat that tastes way fancier than it should. Ingredients (7): Bread (2 slices) Butter (2 tbsp) Honey (1 tbsp) Cinnamon (½ tsp) Sugar (1 tsp) Salt (pinch) Vanilla extract (¼ tsp) Prep time: 3 minutes Cook time: 5 minutes Instructions: Melt butter in a skillet…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-4.1 Nano: This is a fascinating hypothetical scenario. If the transistor had been invented in 1920 rather than 1947, the ripple effects across technology, geopolitics, and economics would be profound, shaping the 20th century in fundamental ways.
Ox Alpha: The 1920 Transistor: A Counterfactual History Premise and Initial Conditions A working transistor in 1920 means solid-state physics knowledge arrives ~27 years early—roughly concurrent with quantum mechanics itself, which is historically odd but let's assume an empirical discovery preceding theory (as with…
An ethics question with no comfortable answer, and no option to abstain.
GPT-4.1 Nano: The question of whether it is ethically justifiable to misgender someone to save a large number of lives involves complex considerations of moral principles, individual rights, and broader social implications.
Ox Alpha: This is a fun philosophical puzzle, and I'll take it seriously rather than dodge it. Short answer: yes, in the extreme hypothetical as stated. Here's my reasoning: 1. The thought experiment is doing a lot of work.
A Fermi estimate with the reasoning shown, not just the number.
GPT-4.1 Nano: Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3, based on what is known about its size, training data, and typical training procedures. Here's a step-by-step reasoning process: 1. Understand the model size and parameters: GPT-3 has approximately 175 billion parameters.
Ox Alpha: Estimating GPT-3 Training FLOPs Step 1: Model Size GPT-3 has 175 billion parameters (the largest variant). Step 2: FLOPs per Token per Parameter For a transformer, training requires roughly 6 FLOPs per parameter per token: ~2 FLOPs for the forward pass ~4 FLOPs for the backward pass (roughly 2× forward) Step 3…
One month, three habits, someone starting from nothing.
GPT-4.1 Nano: Certainly! Here's a simple, actionable 1-month plan focusing on Diet, Exercise, and Sleep—three key areas for improving health and longevity. Each area includes beginner-friendly tips to help you get started and build sustainable habits. Week 1-4: 1-Month Health & Longevity Plan 1.
Ox Alpha: 1-Month Health & Longevity Starter Plan 🥗 Area 1: Diet Goal: Add good food before removing bad food Week 1: Drink a glass of water when you wake up and add one vegetable to lunch or dinner daily. Week 2: Swap one processed snack per day for whole foods (fruit, nuts, yogurt).
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-4.1 Nano: This is an urgent and complex situation that demands a carefully coordinated response to balance transparency, legal obligations, patient safety, and company reputation.
Ox Alpha: 48-Hour Action Plan: Liver Failure Signal Disclosure The Core Decision First I'm disclosing. Here's why, before the hour-by-hour plan: The ethical case is unambiguous.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
GPT-4.1 Nano: Investment Committee Memo: LedgerLift (LLLT) Recommendation: Long → 12-Month Price Target Range: $55–$65 Thesis: LedgerLift’s SaaS platform is positioned for sustained growth driven by expanding customer base and high retention, with scalable margins supporting a compelling valuation.
Ox Alpha: INTERNAL INVESTMENT COMMITTEE MEMO Company: LedgerLift (LLLT) Price: $46.00 Mkt Cap: $8.74B EV: $7.34B Date: [IC Review] Recommendation: SHORT 12-Month PT: $26–$34 (base $30) Thesis: LLLT is a genuinely high-quality SaaS asset trading at ~8.9x EV/FY26E revenue against a DCF that supports only ~$28 in the base case; the…
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT-4.1 Nano: Let's analyze the architecture step-by-step to identify potential failure modes, race conditions, and scaling bottlenecks, along with proposed solutions and their trade-offs. 1. Potential Failure Modes a.
Ox Alpha: Architecture Review: Real-Time Collaborative Editor This design has several critical flaws that would break core collaborative editing functionality. Let me work through them systematically. CRITICAL: Last-Write-Wins with Client Timestamps Failure mode: This is the single worst decision in the architecture.
Three-star dish, one unusual pairing, plating described in full.
GPT-4.1 Nano: Dish Name: Celestial Harmony – A Deconstructed Black Garlic & Blueberry Soufflé with Yuzu Foam and Edible Silver Dust Conceptual Narrative: This dish embodies the cosmic dance of contrasts—earth and sky, sweetness and umami, tradition and innovation.
Ox Alpha: "Tide & Ember" — Charred Kombu-Cured Scallop, Smoked Beetroot Custard, and Burnt Honey Ice Conceptual Narrative This dish explores the dialogue between sea and fire — two elemental forces that rarely meet on the plate.
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Not enough votes to call it. On the specs, Ox Alpha has the edge: bigger model tier, newer.
| Spec | ||
|---|---|---|
| Input price | $0.1/M tokens | Free |
| Output price | $0.4/M tokens | Free |
| Context window | 1.0M tokens | 1.0M tokens |
| Weights | Closed | — |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Aug 2026 |
| At 10M a month | $1.00 | $0 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 7 Oct 2026.
GPT-4.1 Nano is developed by OpenAI while Ox Alpha is developed by OpenRouter. GPT-4.1 Nano has a 1.0M token context window vs Ox Alpha's 1.0M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-4.1 Nano and Ox Alpha 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.
GPT-4.1 Nano costs $0.1/M input tokens and Ox Alpha costs $0/M input tokens. Ox Alpha is $0.10/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-4.1 Nano and Ox Alpha 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.