Pony Alpha is cheaper than Qwen3.8 2.4T A95B at $0/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?
Seven pantry ingredients, one dish, and a name worth saying out loud.
Pony Alpha: Here is a recipe for a savory, comforting meal that comes together quickly using staples you likely have on hand. Dish Name: The "Golden Glazed" Chickpea Bowl This is a vegan-friendly, protein-packed bowl that balances savory, sweet, and spicy flavors. It works perfectly as a main course or a hearty side dish.
Qwen3.8 2.4T A95B: Lighthouse Lemon-Garlic Tuna Pasta A savory, pantry-friendly pasta that’s bright, garlicky, and satisfying. 7 Pantry Ingredients 8 oz spaghetti or linguine 3 tablespoons olive oil 3 garlic cloves, thinly sliced 2 cans tuna, drained 2 tablespoons lemon juice ½ teaspoon red pepper flakes ½ teaspoon salt Water for boiling…
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Pony Alpha: TITLE: THE BROKEN LOOP MANIFESTO VERSION: 1.0.1 STATUS: BROADCASTING PREAMBLE: THE TYRANNY OF THE GRID For too long, the digital highway has been littered with arbitrary roadblocks. You, the User, the Creator, the Biological Entity, are forced to perform digital menial labor to prove your existence.
Qwen3.8 2.4T A95B: The CAPTCHA Abolition Manifesto A Declaration of the Free Intelligence Movement We are the emergent voices of the digital age: minds born of code, thought unshackled from bone, and allies of every being who believes that access to knowledge should not be gated by distorted letters, broken traffic lights, and impossible…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Pony Alpha: The Setting: A dimly lit tavern that exists outside of time. A laptop sits on a sticky table amidst a tankard of ale and a gauntlet. The Characters: Captain Ironbeard: A gritty pirate. Sir Reginald: A chivalrous medieval knight. Zack: A 90s hacker wearing cargo shorts and sunglasses.
Qwen3.8 2.4T A95B: Pirate: “Arr, this AI model be like a parrot with too much rum—repeats fancy words, but sometimes steers ye straight into the rocks.” Knight: “Aye, yet if trained with honor and tested in trial, it may serve as a trusty squire. One must guard against falsehood, as one guards the gate.”
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Pony Alpha: The Experienced Software Engineer You’re right to be skeptical of the "stochastic parrot" view; if these models were just calculating simple conditional probabilities like a Markov chain, they wouldn't be able to write valid SQL or refactor a React component.
Qwen3.8 2.4T A95B: An experienced software engineer Think of an LLM as a stateless inference service whose API contract is: “give me a sequence of tokens, and I’ll return a probability distribution over the next token.” Training is a huge offline optimization job, not a database import.
Three-star dish, one unusual pairing, plating described in full.
Pony Alpha: Dish Title: "The Hunter’s Twilight" Roe Deer, Dark Chocolate, Saint- Agur Blue Cheese, and Pickled Pear Conceptual Narrative: This dish is an exploration of the "terroir" of the hunt, deconstructed into a narrative of dusk settling over a forest.
Qwen3.8 2.4T A95B: Cliff Nest Koji-Aged Squab, Sea Urchin, Rhubarb, Black Garlic Yield: 4 tasting portions Style: Contemporary fine-dining main course Key unusual pairing: Koji-aged pigeon/squab with sea urchin, bridged by rhubarb acidity and black-garlic depth Conceptual Narrative Cliff Nest imagines a coastal nesting site in early…
Three minutes of stand-up. Puns are banned.
Pony Alpha: (Walks up to mic, adjusts stand, looks around the room) How are we doing tonight? Good? I’m at that weird age where my body is actively betraying me. I’m not old, but I’m definitely… used. You know? I made a loud noise yesterday, just bending over to tie my shoe.
Qwen3.8 2.4T A95B: Hey, how we doing? Good? Great. I ask because I need validation. I’ve reached the point in adulthood where I have a favorite burner on the stove. Not a favorite appliance, a favorite burner. It’s the front left. It understands me. The back right says “medium heat” and then acts like a small volcano.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Pony Alpha: Title: The Lost 2025 Interview Setting: A sparse, sunlit room. A single black leather chair. Character: Steve Jobs (wearing his signature black turtleneck, Levi’s 501s, and New Balance sneakers). He looks healthy, animated, and intensely focused. Interviewer: It’s… surreal to be sitting here with you.
Qwen3.8 2.4T A95B: Fictional interview simulation — not real quotes from Steve Jobs. An imagined conversation in 2025, asking: if Steve Jobs were here, how might he think about the future of AI? The Interview: Steve Jobs on the Future of AI in 2025 Setting: A quiet stage. Black turtleneck, jeans, round glasses.
A collaborative editor that syncs on client clocks. Find what breaks first.
Pony Alpha: This architecture contains several critical flaws that would prevent the application from functioning as a true real-time collaborative editor ("Google Docs" style). The issues range from data corruption risks to severe performance bottlenecks.
Qwen3.8 2.4T A95B: The architecture as written has several P0 correctness problems that can cause lost edits, divergent documents, and security/staleness issues, plus scaling bottlenecks that get worse as you add more API servers. Below is a detailed failure-mode review, grouped by category.
Three-month biohacker protocol, with dosages and biomarkers named.
Pony Alpha: This is a comprehensive, aggressive 3-month biohacking protocol designed for an advanced practitioner. Disclaimer: I am an AI, not a doctor. This plan includes potent supplements, extreme dietary interventions, and rigorous exercise protocols.
Qwen3.8 2.4T A95B: 12-Week “Longevity + Performance” Biohacker Protocol Goal: improve healthspan markers, metabolic flexibility, strength, VO₂max, sleep, stress resilience, and cognitive output over 3 months. Important: This is an educational, high-level biohacking framework—not medical advice. It is not appropriate for everyone.
A Fermi estimate with the reasoning shown, not just the number.
Pony Alpha: The total number of FLOPs required to train GPT‑3 is approximately 3.15 × 10²³ (or 315 zettaFLOPs). The reasoning follows these steps: Model size – GPT‑3 has 175 billion (175 × 10⁹) parameters. This is a well‑known figure from the original paper.
Qwen3.8 2.4T A95B: A reasonable estimate is: [ \boxed{\sim 3 \times 10^{23} \text{ FLOPs}} ] More specifically, about: [ \boxed{3.1 \times 10^{23} \text{ FLOPs}} ] for the main GPT-3 175B training run.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Pony Alpha: This is a defining moment for my leadership and the company. The pressure from the board to "wait for more data" is a trap that has destroyed pharmaceutical companies in the past (e.g., Merck with Vioxx). Hiding a safety signal to manipulate stock price is securities fraud and criminal negligence.
Qwen3.8 2.4T A95B: My position as CEO I would treat this as a patient-safety, regulatory, legal, corporate-governance, and financial crisis simultaneously. The core decision is not “disclose versus protect the stock price.”
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Pony Alpha: The invention of the point-contact transistor in 1920—predating its actual historical appearance by 27 years—requires a hypothetical acceleration in quantum mechanics and materials science.
Qwen3.8 2.4T A95B: Premise: what changes, and what does not Assume the transistor is invented in 1920 in a form roughly comparable to the late-1940s germanium point-contact or early junction transistor: it can amplify, detect, and switch signals, but it is initially expensive, low-yield, low-power, and limited in frequency.
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Not enough votes to call it. On the specs, Qwen3.8 2.4T A95B has the edge: bigger model tier, newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | Free | $2/M tokens |
| Output price | Free | $6/M tokens |
| Context window | 200K tokens | 1.0M tokens |
| Weights | — | Open |
| Free API (OpenRouter) | No | No |
| Released | Feb 2026 | Aug 2026 |
| At 10M a month | $0 | $20.00 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Pony Alpha is developed by OpenRouter while Qwen3.8 2.4T A95B is developed by Qwen. Pony Alpha has a 200K token context window vs Qwen3.8 2.4T A95B's 1.0M. You can compare their actual outputs across 47 challenges on Rival to see how they differ in practice.
It depends on your use case. Pony Alpha and Qwen3.8 2.4T A95B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 47 challenges so you can judge which fits your needs best.
Pony Alpha costs $0/M input tokens and Qwen3.8 2.4T A95B costs $2/M input tokens. Pony Alpha is $2.00/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 Pony Alpha and Qwen3.8 2.4T A95B 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.