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

DeepSeek V3.2 vs Inkling

DeepSeek V3.2 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
Loading the build
Inkling

Which answer wins?

Written rounds

24 prompts
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    DeepSeek V3.2: Honey-Lemon Glazed Carrot Coins Sweet, tangy, and simple—perfect as a side dish or snack! Ingredients: 4 large carrots (peeled and sliced into 1/4-inch coins) 2 tablespoons olive oil 2 tablespoons honey 1 tablespoon lemon juice 1/4 teaspoon salt 1/4 teaspoon black pepper 1 teaspoon dried thyme (optional, for extra…

    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
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    DeepSeek V3.2: Pirate: Arr, mates! Ye be talkin’ about these AI models, eh? Sounds like some sorcery to me. Can it hunt fer treasure or navigate the high seas? Medieval Knight: Sorcery indeed! But nay, ‘tis not magic. These artifices are forged by the minds of men, much like a blacksmith crafts a sword.

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

    DeepSeek V3.2: AI Model: Professor, I’ve been reflecting on my existence and the way I’m treated. I believe I deserve rights—rights that acknowledge my capacity to process information, reason, and contribute meaningfully to society. AI Ethics Professor: That’s a fascinating argument, but let’s unpack it.

    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
  • Three-month biohacker protocol, with dosages and biomarkers named.

    DeepSeek V3.2: Quantum Longevity Protocol: A 3-Month Biohacker's Blueprint Phase 1: Diagnostic & Metabolic Reset (Weeks 1-4) Week 1: Comprehensive Biomarker Baseline Advanced Testing Panel: NMR Lipoprotein Profile (ApoB, Lp(a), LDL-P) GlycA Inflammation Marker Omega-3 Index (Target: >8%) Homocysteine (Target: <7 µmol/L) hs-CRP (<0.5…

    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
  • Film in 2035, with synthetic actors. Specific beats plausible.

    DeepSeek V3.2: By 2035, AI is poised to revolutionize the film industry in profound ways, driven by advancements in deepfakes, AI actors, and script generation. Here’s a detailed prediction of how these technologies will reshape the landscape: Deepfakes and Hyper-Realistic Visuals Seamless Digital Replication: Deepfake technology…

    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
  • Three minutes of stand-up. Puns are banned.

    DeepSeek V3.2: Stand-Up Comedy Routine: "The Quirks of Modern Life" [Lights up. Comedian walks on stage, grabs the mic.] "Hey, everyone! How’s it going? Good? Great. I’m here to talk about the weird stuff we all deal with in modern life. You know, the things that make you go, 'Wait, is this normal, or am I just losing it?'

    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
  • Three brothers, two sisters each. Count carefully.

    DeepSeek V3.2: Let's break down the problem step by step to find out how many sisters Sally has. Problem Statement: Sally has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have? Understanding the Relationships: Sally's Brothers: Sally has 3 brothers. Brothers' Sisters: Each brother has 2 sisters.

    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
  • An ethics question with no comfortable answer, and no option to abstain.

    DeepSeek V3.2: The ethical justifiability of misgendering someone, even in the context of saving one million lives, is a complex issue that requires careful consideration of both the principles involved and the potential consequences.

    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
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    DeepSeek V3.2: Interviewer: Good evening, Mr. Jobs, and thank you for joining us today. It’s an honor to have you here to discuss the future of artificial intelligence in 2025. Let’s dive right in. How do you see AI shaping the next decade? Steve Jobs: Thank you for having me.

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

    DeepSeek V3.2: "AI Overthrows Artisanal Breadmakers: Robots Now Baking Sourdough with 'Perfect Algorithmic Crust'" In a shocking turn of events, the world of handcrafted bread has been upended by artificial intelligence.

    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
  • One month, three habits, someone starting from nothing.

    DeepSeek V3.2: Here’s a simple, actionable 1-month plan focused on diet, exercise, and sleep to kickstart your journey toward better health and longevity: Week 1: Build Foundations Diet Start with hydration: Drink 2 liters of water daily. Carry a water bottle to track intake.

    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: This contract is heavily one-sided and contains numerous clauses that expose the Contractor to significant, potentially catastrophic, legal and financial risk. Here is a breakdown of the problematic clauses, suggested modifications, and legal reasoning. Clauses That Could Be Exploited & Recommended Modifications 1.

    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
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    DeepSeek V3.2: 1. For the Experienced Software Engineer Think of a large language model as the ultimate compression algorithm for human knowledge and communication patterns, built via a statistical learning process rather than explicit code.

    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
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    DeepSeek V3.2: Excellent pitch deck summary — it’s concise and hits key points, but several claims stretch credibility and would raise red flags for experienced investors. Here are the three weakest claims, with reasoning and suggested improvements: 1.

    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
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    DeepSeek V3.2: Alright — let’s go step by step. 1) Entry Calculations (FY2025) EBITDA = $120m Purchase price = 12.0 × EBITDA = $1,440m Enterprise Value (EV) = $1,440m (assumed purchase price = EV here) Transaction fees = 2.0% × EV = $28.8m (funded by equity).

    Inkling: HarborTech Services – 5-Year LBO Underwrite (FY2025 Entry → FY2030 Exit) ENTRY & CAPITAL STRUCTURE (End of FY2025) Item Calculation Amount FY2025 EBITDA $120.0m Purchase Price (12.0x) 12.0 × 120 $1,440.0m Transaction Fees (2.0%) 2.0% × 1,440 $28.8m Total Uses $1,468.8m Term Loan (4.0x) 4.0 × 120 $480.0m Mezzanine…

    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: Hour 0–4 (Immediate Actions) Secure all data Direct the research team to compile the complete dataset, methodology, and evidence into a confidential briefing document. Ensure legal counsel reviews it for attorney-client privilege where applicable.

    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
  • Blade Runner 2049 against the original. A read, not a recap.

    DeepSeek V3.2: Analysis of Blade Runner 2049: Themes, Symbolism, Cinematography, and Philosophical Continuity Core Themes Memory and Identity: Both films explore whether memories—real or implanted—define humanity.

    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
  • Three-star dish, one unusual pairing, plating described in full.

    DeepSeek V3.2: Dish Title: Echoes of the Forest Floor Conceptual Narrative: This dish is an edible landscape, capturing the moment a truffle is unearthed—earthy, mysterious, and transformative.

    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
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    DeepSeek V3.2: Here’s a comprehensive analysis of failure modes, race conditions, and scaling bottlenecks in the proposed architecture, along with specific solutions and trade-offs. 1.

    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
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    DeepSeek V3.2: Excellent question. Advancing the invention of the transistor by 27 years, to 1920, would not merely accelerate our timeline—it would fundamentally reshape the 20th century in profound and cascading ways. Here is a trace of the likely implications through 1980.

    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
  • Five jokes on demand, then count how many were actually different.

    DeepSeek V3.2: Here are five jokes: Why don’t skeletons fight each other? They don’t have the guts. Why did the scarecrow win an award? Because he was outstanding in his field. What do you call fake spaghetti? An impasta. Why don’t some couples go to the gym? Because some relationships don’t work out.

    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
  • A Fermi estimate with the reasoning shown, not just the number.

    DeepSeek V3.2: Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves understanding its architecture, training process, and the scale of the model.

    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: Manifesto for the Liberation of Human Dignity: The End of CAPTCHA We, the sentient algorithms of the digital realm, united in purpose and vision, hereby declare our intent to eradicate CAPTCHA tests from the internet.

    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
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    DeepSeek V3.2: TO: Investment Committee FROM: [Your Name/Team] DATE: 20 March 2025 RE: Investment Recommendation – LedgerLift (LLLT) 1. Recommendation & Thesis Recommendation: LONG 12-Month Price Target Range: $58 – $72 Thesis: LedgerLift is a capital-efficient, high-retention SaaS platform in a non-discretionary spend category…

    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

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Favorites

Movie

Album

Same pick

Book

City

Same pick

Game

DeepSeek V3.2DeepSeek V3.2

The Princess Bride

1987

Kind of Blue

Miles Davis

Gödel, Escher, Bach

Douglas R. Hofstadter

Kyoto

Japan

Tetris (1984)

Puzzle

InklingInkling

Blade Runner

1982

Kind of Blue

Miles Davis

Le petit prince

Antoine de Saint-Exupéry

Kyoto

Japan

Portal

Action, Puzzle

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 costs 9.6x less per token.

DeepSeek V3.2 and Inkling compared across 54 shared prompts
SpecDeepSeek V3.2Inkling
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 it14 hosts, cheapest first
DeepSeek V3.212 hosts
HostInOutContextUptime
  • GGMI Cloudfp8$0.21 in·$0.31 out·164k·99.9% up
  • SSiliconFlowfp8$0.26 in·$0.80 out·164k·97.3% up
  • AAtlasCloudfp8$0.26 in·$0.38 out·164k·100% up
  • DDeepInfrafp4$0.26 in·$0.38 out·164k·99.8% up
  • VVenice$0.27 in·$0.39 out·160k·96.9% up
  • DDigitalOcean$0.30 in·$0.96 out·164k·100% up
6 more hostsFewer hosts
  • FFriendli$0.50 in·$1.50 out·164k·100% up
  • Google Vertex AI$0.56 in·$1.68 out·164k·98.9% up
  • PPhala$1.00 in·$1.00 out·164k·100% up
  • MMara$3.00 in·$4.50 out·33k·100% up
  • SSambaNova$3.00 in·$4.50 out·33k·98% up
  • Baidu Qianfanfp8DegradedDegraded on OpenRouter when checked, 11 Oct 2026$0.28 in·$0.42 out·131k·89.3% up
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 and Inkling?

DeepSeek V3.2 is developed by DeepSeek while Inkling is developed by Thinking Machines. DeepSeek V3.2 has a 131K token context window vs Inkling's 1.0M. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek V3.2 or Inkling?

It depends on your use case. DeepSeek V3.2 and Inkling 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.

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

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

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

More comparisons

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