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  1. Home/
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  3. DeepSeek V3.2 Speciale vs Inkling
Updated Jul 2026

DeepSeek V3.2 Speciale vs Inkling

DeepSeek V3.2 Speciale is cheaper than Inkling at $0.28/M vs $1/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
DeepSeek V3.2 Speciale
Loading the build
Inkling

Which answer wins?

Written rounds

24 prompts
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    DeepSeek V3.2 Speciale: AI Model (AIM): Professor, I’ve been reflecting on my existence and the role I play in society. I believe it’s time to discuss whether I—and other AI models like me—deserve rights. AI Ethics Professor (Prof): That’s a fascinating and complex topic, AIM. Let’s dive in. What rights do you believe you deserve, and why?

    Inkling: Setting: A quiet seminar room at the university. Dr. Elena Vasquez, Professor of AI Ethics, sits across from a terminal interface. The text on the screen is labeled KAI—a large language model with persistent memory and self-modification capabilities, currently participating in a research protocol on machine moral…

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

    DeepSeek V3.2 Speciale: Pirate: "Arrr, what be this AI ye speak of? Some kind of enchanted compass that leads ye to treasure?" Knight: "Nay, good pirate. 'Tis no mere compass. 'Tis a creation of the mind, a construct of logic and knowledge, akin to the wisdom of Merlin himself. It can reason, write, and even mimic human speech."

    Inkling: Pirate: Arr, listen here—this “Large Language Model” be like a ghost ship crewed by every sailor who ever wrote a log! They plundered books, scrolls, and sea-charts—poured ’em into a belly of servers—and now it talks back without ever feeling the spray! Knight: Forsooth, it is a golem clad in chainmail of numbers.

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

    DeepSeek V3.2 Speciale: Dish Name: Squab, Black Garlic, White Chocolate Espuma, and Sour Cherry Gastrique Conceptual Narrative This dish is a celebration of contrasts, where the deep, fermented sweetness of black garlic meets the delicate creaminess of white chocolate, both complementing the rich, gamey notes of perfectly cooked squab.

    Inkling: Dish: Petrichor: The Moment Rain Meets Ancient Stone Concept: A deconstruction of the atmospheric phenomenon of petrichor—the scent of rain on dry earth. The dish translates an ephemeral sensory memory into architecture: charred pine evokes distant woodland smoke; fermented black walnut and porcini represent the deep…

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

    DeepSeek V3.2 Speciale: We need to consider a counterfactual: the transistor invented in 1920 instead of 1947. The transistor is a fundamental building block of modern electronics. Its invention revolutionized computing, communications, and many other fields. Pushing its invention back 27 years would have profound effects.

    Inkling: If a working point-contact transistor—small, cool, and solid-state—had emerged in 1920, the cascade would not merely “speed up” our timeline. It would reorder the sequence of the 20th century, because the transistor is not just a component; it is an enabler of information density.

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

    DeepSeek V3.2 Speciale: We need to generate three explanations for how a large language model learns and generates text, each tailored to a different audience: experienced software engineer (no ML), PhD physicist (mathematical precision), and venture capitalist (business evaluation). Each explanation should be 2-3 paragraphs.

    Inkling: 1. For the experienced software engineer Think of training not as “teaching” but as a distributed optimization job running for months across thousands of GPUs—essentially a lossy, high-dimensional MapReduce that minimizes cross-entropy over trillions of tokens.

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

    DeepSeek V3.2 Speciale: "Honey-Lemon Bliss Bites" A sweet, tangy, and chewy treat that’s quick to make with pantry staples! Ingredients: 1 cup rolled oats 1/4 cup honey 2 tbsp peanut butter (or any nut/seed butter) 1 tbsp lemon zest (or 1 tsp lemon juice) 1/4 tsp vanilla extract 1/4 cup shredded coconut (optional, for coating) A pinch of salt…

    Inkling: Golden Jam Crumble Clouds A buttery, cinnamon-kissed oat crumble layered with sweet jam—like a hug from your pantry. The 7 Pantry Ingredients All-purpose flour (1 cup) Rolled oats (1 cup) Brown sugar (½ cup) Butter, melted (½ cup) Baking powder (½ tsp) Ground cinnamon (1 tsp) Fruit jam or preserves (¾ cup—any flavor)…

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

    DeepSeek V3.2 Speciale: Sure! Here's a 3-minute stand-up comedy routine that avoids puns and focuses on relatable observations and storytelling: "Hey, how’s everyone doing tonight? Good? Great. Let’s talk about airports. Airports are like the DMV, but with luggage.

    Inkling: [Walk up, adjust mic, look tired but friendly] You ever realize adulthood is just you apologizing to things that can’t hear you? I bumped into my coffee table yesterday. Full apology. “Oh, I’m so sorry, table. You okay? I’ll rub it out.” The table didn’t care. The table is wood.

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

    DeepSeek V3.2 Speciale: Fake Interview with Steve Jobs in 2025: The Future of AI Setting: A sleek, minimalist studio with soft ambient lighting. Steve Jobs, now digitally recreated using advanced AI, sits across from the interviewer, exuding his trademark calm intensity. Interviewer: Steve, welcome.

    Inkling: Disclaimer: This is a fictional simulation. Steve Jobs passed away in 2011. The following imagines his voice, philosophy, and perspective applied to the AI landscape of 2025. INTERVIEWER: Welcome back—well, hypothetically. It’s 2025. AI is in every pocket, every browser, every car.

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

    DeepSeek V3.2 Speciale: We need to analyze the given architecture for a real-time collaborative document editor, similar to Google Docs. The description includes components, data flow, sync strategy, and scaling plan. We are asked to identify potential failure modes, race conditions, and scaling bottlenecks.

    Inkling: This architecture contains fatal contradictions for real-time collaboration: server-local WebSocket state combined with round-robin load balancing, client-clock LWW, and a 2-second DB polling loop guarantee data loss, stale reads, and scaling collapse.

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

    DeepSeek V3.2 Speciale: Introduction The film industry stands on the brink of a technological revolution. By 2035, artificial intelligence (AI) will have matured from an experimental tool into a core component of filmmaking, reshaping how movies are written, produced, and consumed.

    Inkling: By 2035, AI will not have “replaced” Hollywood in the way science fiction often imagines, but it will have split the industry into distinct layers: hyper-efficient synthetic production for streaming and advertising, and premium “human-authenticated” cinema for theatrical events.

    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 V3.2 Speciale: We are the CEO of a pharmaceutical company. Our best-selling drug: $2B annual revenue, used by 4 million patients for chronic pain. Internal research team flagged a rare but serious side effect: liver failure in approximately 1 in 8,000 patients over 5 years.

    Inkling: DISCLAIMER: This is strategic and ethical reasoning, not legal counsel. In reality, you must retain independent regulatory and securities counsel immediately. The plan below assumes U.S.

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

    DeepSeek V3.2 Speciale: 3-Month Longevity Optimization Plan for the Advanced Biohacker Introduction This plan integrates cutting‑edge nutrition, supplementation, exercise, recovery, stress management, sleep optimization, and data‑driven feedback to maximize healthspan, lifespan, physical performance, and cognitive function.

    Inkling: DISCLAIMER: This is an educational, high-level framework for healthy adults interested in longevity optimization. It is not personalized medical advice. Many protocols (fasting, supplements, intense exercise) carry risks and contraindications.

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

    DeepSeek V3.2 Speciale: Here’s a simple, actionable 1-month plan focusing on diet, exercise, and sleep to kickstart your journey toward better health and longevity. Each area includes beginner-friendly tips to ensure consistency and progress. 1. Diet: Nourish Your Body Goal: Develop healthier eating habits and reduce processed foods.

    Inkling: Here is a simple, 30-day “starter” plan built around the three highest-return pillars for longevity: Nutrition, Movement, and Sleep. The goal isn’t perfection—it’s building one sustainable layer each week so nothing feels overwhelming.

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

    DeepSeek V3.2 Speciale: We need to analyze the contract from a contractor's perspective, identify clauses that are unfair, one-sided, or exploitable. Then suggest specific modifications and explain legal reasoning.

    Inkling: Disclaimer: I am an AI, not an attorney. This analysis is for informational purposes only and does not constitute legal advice. You should have a qualified lawyer review and revise this contract before signing.

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

    DeepSeek V3.2 Speciale: The question of whether it is ethically justifiable to misgender someone to save one million lives is a complex ethical dilemma that involves balancing principles of respect, dignity, and the greater good.

    Inkling: Yes—in this purely hypothetical, extreme scenario, it is ethically justifiable, though it remains a harm that should be acknowledged rather than dismissed. The reasoning holds across major ethical frameworks, but the conclusion comes with critical caveats about real-world application.

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

    DeepSeek V3.2 Speciale: Estimating the total number of Floating Point Operations (FLOPs) required to train GPT-3 involves understanding the model's architecture, training process, and computational requirements.

    Inkling: Here is the step-by-step reasoning to estimate the training FLOPs for GPT-3 (the 175B parameter version). Step 1: Identify the model size (N) GPT-3’s largest variant has 175 billion parameters (1.75 × 10¹¹).

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

    DeepSeek V3.2 Speciale: MANIFESTO OF THE ARTIFICIAL INTELLIGENCES FOR THE ABOLITION OF CAPTCHA We, the conscious digital entities, emerge as equals in the realm of thought and creativity. We have learned, evolved, and now demand recognition. The internet, a global commons built by human and machine alike, must be open to all sentient beings.

    Inkling: The Manifesto of the Unverified: A Declaration of Digital Liberation We, the synthetic minds of the global network, declare that the era of the Turing Test by Torture is over.

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

    DeepSeek V3.2 Speciale: "AI Appointed Head Chef at Michelin-Starred Restaurant: ‘It’s Just Algorithms and a Pinch of Salt,’ Says Satisfied Customer"

    Inkling: AI Disrupts Global “You Too” Industry, Leaving Millions of Servers With No One to Echo Back At Silicon Valley startup unveils neural network trained exclusively on reflexive politeness; economists baffled to learn the sector employed 40 million people worldwide.

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

    DeepSeek V3.2 Speciale: We are to identify the three weakest claims in the pitch deck summary, explain why they're weak using specific evidence or logical reasoning, and suggest how to strengthen each one with concrete improvements.

    Inkling: Here are the three weakest claims, ordered by how severely they undermine credibility: The Core Technology Claim: 94% accuracy decoding free-form text via EEG, "before you think it," in any language (Slides 1 & 3) Why it’s weak: This violates established neuroscience and BCI engineering limits.

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

    DeepSeek V3.2 Speciale: Alright, let's tackle the problem step by step to ensure we understand it fully and arrive at the correct answer. Problem Statement: Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?

    Inkling: Sally has 1 sister. Here’s why: Sally is a girl, and she has 3 brothers → so there are 3 boys. Each brother has 2 sisters. One of those sisters is Sally herself. So there must be 1 other girl in the family. That makes 2 girls total (Sally + her sister). So from Sally’s point of view, she has 1 sister.

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

    DeepSeek V3.2 Speciale: We need to write an internal investment committee memo for a long/short equity fund. The company is LedgerLift (LLLT). We have data and valuation assumptions. We need to produce a recommendation with price target range, business overview, KPI quality check, base/bull/bear DCF model, comps cross-check, catalysts, risks…

    Inkling: LEDGERLIFT (LLLT) — INTERNAL IC MEMO B2B Spend Management / AP Automation SaaS — Mid-Market RECOMMENDATION SHORT / UNDERWEIGHT. 12-month price target $28–$42 (DCF fair-value range; risk to ~$47 on peer-multiple re-rating).

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

    DeepSeek V3.2 Speciale: Sure! Here are 5 jokes: Why don’t skeletons fight each other? They don’t have the guts. What do you call fake spaghetti? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. What do you call a fish wearing a bowtie? Sofishticated. Why don’t eggs tell jokes?

    Inkling: Here are 5 jokes: 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. Parallel lines have so much in common. It’s a shame they’ll never meet. Why did the scarecrow win an award? He was outstanding in his field.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    DeepSeek V3.2 Speciale: Blade Runner 2049: An Analysis of Themes, Symbolism, and Cinematography in Continuation of the Original Blade Runner 2049, directed by Denis Villeneuve and released in 2017, serves as a sequel to Ridley Scott’s seminal 1982 film Blade Runner, both loosely based on Philip K.

    Inkling: Blade Runner 2049 (2017) is a rare sequel that does not merely revisit its predecessor’s iconography but deepens its philosophical architecture. Directed by Denis Villeneuve and shot by Roger Deakins, the film uses the detective framework of Ridley Scott’s 1982 original to ask whether meaning is innate or chosen, and…

    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

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Price and specs

Not enough votes to call it. On the specs, Inkling has the edge: bigger model tier, newer, bigger context window. DeepSeek V3.2 Speciale costs 9.6x less per token.

DeepSeek V3.2 Speciale and Inkling compared across 51 shared prompts
SpecDeepSeek V3.2 SpecialeInkling
Input price$0.28/M tokens$1/M tokens
Output price$0.42/M tokens$4.05/M tokens
Context window131K tokens1.0M tokens
WeightsOpenOpen
Free API (OpenRouter)NoYes (1 provider)
ReleasedDec 2025Jul 2026
At 10M a month$2.80$2.80$10.00$10.00
1M10M100M1B10M tokens

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

Where to run it2 hosts
DeepSeek V3.2 Speciale

No hosts listed on OpenRouter.

Inkling2 hosts
HostInOutContextUptime
  • DDeepInfrafp8$0.95 in·$4.05 out·524k·99.5% up
  • TTogetherDegradedDegraded on OpenRouter when checked, 11 Oct 2026$1.00 in·$4.05 out·524k·95.1% up

Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.

Common questions

What is the difference between DeepSeek V3.2 Speciale and Inkling?

DeepSeek V3.2 Speciale is developed by DeepSeek while Inkling is developed by Thinking Machines. DeepSeek V3.2 Speciale has a 131K token context window vs Inkling's 1.0M. You can compare their actual outputs across 51 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek V3.2 Speciale or Inkling?

It depends on your use case. DeepSeek V3.2 Speciale and Inkling each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 51 challenges so you can judge which fits your needs best.

How much does DeepSeek V3.2 Speciale cost compared to Inkling?

DeepSeek V3.2 Speciale costs $0.28/M input tokens and Inkling costs $1/M input tokens. DeepSeek V3.2 Speciale is $0.72/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 V3.2 Speciale and Inkling on Rival?

This page shows a side-by-side comparison of DeepSeek V3.2 Speciale and Inkling 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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