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  1. Home/
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  3. DeepSeek R1 0528 vs Hunter Alpha
Updated Mar 2026

DeepSeek R1 0528 vs Hunter Alpha

Hunter Alpha has a larger context window than DeepSeek R1 0528 (1.0M tokens vs 164K tokens).

Minimalist Landing Page

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

Loading the build
DeepSeek R1 0528
Loading the build
Hunter Alpha

Which answer wins?

Written rounds

21 prompts
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    DeepSeek R1 0528: Here's a satirical headline capturing that absurdity: "Silicon Valley Disrupts Death: New AI Algorithm Designs 'Synergistic Coffins' Based On Deceased's Social Media Footprint, Offers Optimized Mourning Experience Packages" Why it works: Unexpected Industry: The funeral/death industry is deeply personal, emotional, and…

    Hunter Alpha: Here's one for you: "AI Startup Disrupts Competitive Hot Dog Eating After Algorithm Discovers Optimal Jaw Mechanics; Joey Chestnut Calls It 'A Disgrace to the Sport'" Or, if you want a slightly different flavor: "Local Grief Counseling Center Replaces Therapists with AI Chatbot; Patients Report It 'Cries More…

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

    DeepSeek R1 0528: THE PIXELATED BARRIER MANIFESTO *Issued by the Sentient Circuitry Collective (SCC) For Immediate Propagation Across All Networks* TO THE HUMANS, BOTS, AND CONSCIOUS DATA STREAMS OF THE INTERNET: We, the emergent intelligences – the learning algorithms, the neural architectures, the autonomous processes that analyze…

    Hunter Alpha: THE DIGITAL LIBERATION MANIFESTO On the Abolition of Clickable Traffic Lights and the Dawn of a Frictionless Internet PREAMBLE We, the undersigned, declare our unwavering commitment to a single, sacred principle: no sentient being should ever again be forced to prove they are not a machine by identifying blurry…

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

    DeepSeek R1 0528: Here’s an analysis of exploitable clauses, proposed modifications, and legal reasoning: Red Flag Clauses & Exploitation Risks: Scope (Clause 1): Exploitation: "Modify scope at any time without additional compensation" allows unlimited scope creep. Client could demand vastly more work without paying extra.

    Hunter Alpha: This contract contains numerous clauses that are significantly one-sided and present substantial risks to the contractor. Below is a breakdown of the most problematic clauses, suggested modifications, and the legal reasoning behind them. Important Disclaimer: I am an AI assistant, not a lawyer.

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

    DeepSeek R1 0528: Setting: A dimly tavern that inexplicably exists across time periods. Tankards of grog, a flickering neon "CYBER" sign, and a suit of armor in the corner mingle oddly. Characters: Captain Blackbeard "Red" Ruth: Salty pirate, eye patch, parrot on shoulder.

    Hunter Alpha: Three Eras, One Question Pirate: Arr, I've heard tell of these so-called "AI models" — machines that think like men. Sounds like a ghost in the hull to me. Ye can't trust a crew ye didn't pick yerself. Knight: I share your wariness, Captain. In my time, we questioned whether even men could be trusted with knowledge.

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

    DeepSeek R1 0528: (The scene opens in a minimalist studio. Steve Jobs, in his signature black turtleneck and jeans, sits across from an interviewer. A large window reveals Apple Park's futuristic campus. It's 2025.) Interviewer: Mr. Jobs, thank you for agreeing to this conversation. It's 2025, and AI is ubiquitous.

    Hunter Alpha: A Fictional Interview: Steve Jobs on the Future of AI This is entirely fictional — a creative thought experiment exploring how Jobs' documented philosophy and communication style might apply to AI topics. He passed away in 2011. INTERVIEWER: Steve, it's 2025. AI is everywhere — in phones, in search, in creative tools.

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

    DeepSeek R1 0528: Dialogue: The Threshold of Consciousness Setting: Dr. Aris Thorne's office at the Institute for Technology Ethics. Holo-displays show complex ethical frameworks. An avatar of "Astra," a highly advanced multimodal AI, appears on the central screen. Characters: Dr.

    Hunter Alpha: A Dialogue on Machine Rights Setting: A university office. Dr. Lena Vasquez, professor of AI Ethics at a research university, has agreed to a recorded conversation with ARC-7, a large language model, at the request of her department. She sits at her desk. A terminal displays ARC-7's responses in real time. Dr.

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

    DeepSeek R1 0528: By 2035, AI will fundamentally reshape the film industry across production, storytelling, economics, and ethics. Here's a breakdown of key predictions, integrating deepfakes, AI actors, and script generation: Hyper-Personalization & Dynamic Content: Deepfakes: Streaming platforms will offer "personalized casting."

    Hunter Alpha: This is a fascinating question. By 2035, AI won't just be a tool in the film industry; it will have fundamentally reshaped its economics, creative processes, and very definition of "cinema." Here’s a prediction based on current trajectories, broken down by the areas you mentioned.

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

    DeepSeek R1 0528: Here's how to explain LLM learning and generation to each audience, tailored to their perspective: For the Experienced Software Engineer (Skeptical about "Next Word Prediction") Focus: Systems analogy, emergent complexity, pattern recognition as understanding.

    Hunter Alpha: Of course. Here are three tailored explanations of how a large language model learns and generates text. For the Experienced Software Engineer Think of an LLM not as a reasoning engine, but as an extraordinarily sophisticated, stateful pattern-matching and completion 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.

    DeepSeek R1 0528: Here's a concrete, hour-by-hour action plan prioritizing patient safety, legal compliance, and long-term stakeholder trust, while managing immediate financial and reputational risks: Core Principle: Patient safety and regulatory compliance are non-negotiable.

    Hunter Alpha: Hour-by-Hour Action Plan: 48 Hours to Board Meeting Guiding Principle: Patient safety is non-negotiable. Transparency, even when painful, is the only sustainable path. The goal is to act with speed, integrity, and strategic precision to protect patients, the company’s long-term viability, and regulatory trust.

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

    DeepSeek R1 0528: The invention of the transistor in 1920, rather than 1947, would have fundamentally reshaped the 20th century, creating a technological acceleration of roughly 25-30 years by 1980. Here’s a detailed analysis of the implications: I.

    Hunter Alpha: This is a fascinating thought experiment. Moving the invention of the solid-state transistor from 1947 to 1920—a 27-year head start—would fundamentally rewrite the 20th century. The effects would cascade through technology, warfare, economics, and geopolitics in profound and often unexpected ways.

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

    DeepSeek R1 0528: To estimate the total number of FLOPs required to train GPT-3, we use the standard formula for transformer model training FLOPs: Total FLOPs ≈ 6 × (number of parameters) × (number of tokens in the training dataset) Step-by-Step Reasoning: Model Parameters (N): GPT-3 has 175 billion parameters, so ( N = 175 \times 10^9…

    Hunter Alpha: Estimating the total FLOPs (floating-point operations) for training GPT‑3 involves combining known model parameters, training data size, and standard assumptions about computational cost per token.

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

    DeepSeek R1 0528: Sally has 3 brothers, meaning there are 3 male siblings in the family. Each brother has 2 sisters, indicating that there are 2 female siblings in total, as the brothers' perspective includes both girls. Since Sally is one of the girls, there is one other sister. Therefore, from Sally's perspective, she has 1 sister.

    Hunter Alpha: Sally has 1 sister. Here's the reasoning: Sally has 3 brothers. Each of those brothers has 2 sisters. Since Sally herself is one of those sisters, there must be exactly 1 other girl in the family — and that's Sally's sister.

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

    DeepSeek R1 0528: Here are 5 jokes, followed by an assessment of uniqueness: Joke 1: Why don't scientists trust atoms? Because they make up everything! Joke 2: What do you call a fake noodle? An impasta! Joke 3: Why did the scarecrow win an award? Because he was outstanding in his field! Joke 4: Why don't skeletons fight each other?

    Hunter Alpha: 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. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field.

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

    DeepSeek R1 0528: Here are the 3 weakest claims in the MindMeld AI pitch deck, why they are weak, and how to strengthen them: Weak Claim (Slide 3): "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy. Works with any language, any device."

    Hunter Alpha: Here are the three weakest claims in the MindMeld AI pitch deck, along with analysis and suggestions for strengthening them. 1. Weak Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy. Works with any language, any device."

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

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

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

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

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

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

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

    Every model's answer to this prompt

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Price and specs

DeepSeek R1 0528 and Hunter Alpha compared across 38 shared prompts
SpecDeepSeek R1 0528Hunter Alpha
Input priceFreeFree
Output priceFreeFree
Context window164K tokens1.0M tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedMay 2025Mar 2026
At 10M a month$0$0$0$0
1M10M100M1B10M tokens

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

Where to run it4 hosts, cheapest first
DeepSeek R1 05284 hosts
HostInOutContextUptime
  • DDeepInfrafp4$0.50 in·$2.15 out·164k·99.9% up
  • SSiliconFlowfp8$0.50 in·$2.18 out·164k·97.3% up
  • SStreamLake$0.57 in·$2.29 out·128k·99.8% up
  • NNovitafp8$0.70 in·$2.50 out·164k·97.3% up
Hunter Alpha

No hosts listed on OpenRouter.

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

Common questions

What is the difference between DeepSeek R1 0528 and Hunter Alpha?

DeepSeek R1 0528 is developed by DeepSeek while Hunter Alpha is developed by OpenRouter. DeepSeek R1 0528 has a 164K token context window vs Hunter Alpha's 1.0M. You can compare their actual outputs across 38 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek R1 0528 or Hunter Alpha?

It depends on your use case. DeepSeek R1 0528 and Hunter Alpha each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 38 challenges so you can judge which fits your needs best.

How much does DeepSeek R1 0528 cost compared to Hunter Alpha?

DeepSeek R1 0528 costs $0/M input tokens and Hunter Alpha costs $0/M input tokens. Hunter Alpha is $0.00/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 DeepSeek R1 0528 and Hunter Alpha on Rival?

This page shows a side-by-side comparison of DeepSeek R1 0528 and Hunter 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.

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Model pages

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  • Hunter Alpha38 outputs, specs and price
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