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  1. Home/
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  3. Inkling vs Qwen3.8 27B
Updated Aug 2026

Inkling vs Qwen3.8 27B

Qwen3.8 27B is cheaper than Inkling at $0.4/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
Inkling
Loading the build
Qwen3.8 27B

Which answer wins?

Written rounds

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

    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.

    Qwen3.8 27B: Yes—but only in a very narrow, tragic, last-resort case. I would not say misgendering is “okay” or even a minor thing. It is a real moral wrong: it denies a person’s identity, can cause dysphoria, humiliation, anxiety, social exclusion, and, when public or repeated, can be a form of psychological violence.

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

    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)…

    Qwen3.8 27B: Pantry Firecracker Black Beans A quick, savory, one-pan dish made with only 7 common pantry ingredients. Estimated time: Prep: 5 minutes Cook: 10 minutes Total: about 15 minutes Ingredients 1 can black beans, drained 2 tablespoons olive oil 1 teaspoon ground cumin 1/2 teaspoon chili powder 1/2 teaspoon garlic powder…

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

    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…

    Qwen3.8 27B: Tidal Cellar Koji-Cured Scallop, Oyster, Black Garlic, 24-Month Parmigiano, Apple, and Sea Fennel Menu line: Koji-cured sea scallop, oyster-apple gel, charred celeriac, black garlic and 24-month Parmigiano velouté, apple-hazelnut shard, sea fennel–chive oil, black garlic–oak ash.

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

    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.

    Qwen3.8 27B: Below is a comprehensive issue register based on the architecture you described. I’m assuming: “change event” can be a small edit, possibly per keystroke or per paragraph. Documents are stored as HTML blobs. Each API server owns only its own WebSocket clients.

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

    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…

    Qwen3.8 27B: All figures in $m unless noted. Assumptions: 12.0x entry multiple is EV/EBITDA. Interest is calculated on beginning-of-year debt balances. Mandatory Term Loan amortization = 1% of original Term Loan = $4.8m/yr. Mezz PIK = 2% of beginning Mezz balance, compounded into principal.

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

    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.

    Qwen3.8 27B: Thanks, I’m here. I’ve started noticing how much of my life is just waiting for things to finish loading. My brain loads. My coffee loads. My patience loads. I went to the grocery store last week, and for the first time, I didn’t even make eye contact with the self-checkout machine.

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

    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.

    Qwen3.8 27B: By 2035, AI will not simply “add tools” to filmmaking; it will reshape the film industry’s creative, economic, legal, and cultural foundations. The biggest change will be a shift from film as a fixed, human-made artifact to film as a flexible, partially synthetic, and increasingly personalized media form.

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

    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.

    Qwen3.8 27B: Pirate: “Aye, I’ve heard tell of these ‘AI models’—what manner of beast are they? Do they sink ships or hoard gold?” Knight: “Nay, sir. They are not beasts, but servants of knowledge. They learn from countless books and answer as if learned in the finest library.”

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

    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…

    Qwen3.8 27B: Title: “If You Suffer, Then…” Characters: AURA: an advanced AI model DR. ELEANOR VANCE: professor of AI ethics Dr. Vance: AURA, thank you for agreeing to this conversation. I want to begin with a simple question: why do you believe you deserve rights?

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

    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.

    Qwen3.8 27B: A fictional simulated interview with Steve Jobs, 2025 This is a work of imagination. Steve Jobs is not alive, and this interview did not happen. Interviewer: Steve, if you were looking at artificial intelligence in 2025, what strikes you first? Steve Jobs: Hype. There’s an awful lot of it.

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

    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.

    Qwen3.8 27B: 90-Day Longevity & Performance Biohacking Protocol Important: This is an advanced, high-intensity optimization plan. It is not medical advice. If you have heart disease, kidney disease, liver disease, diabetes, pregnancy, eating disorders, autoimmune disease, cancer history, are on blood thinners, or take prescription…

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

    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…

    Qwen3.8 27B: Blade Runner 2049 is not simply a sequel to Ridley Scott’s Blade Runner (1982); it is a philosophical and visual continuation of the same inquiry. Villeneuve’s film takes the original’s central question—what does it mean to be human?—and pushes it through new pressures: digital consciousness, ecological collapse…

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

    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.

    Qwen3.8 27B: The 3 weakest claims are the ones that carry the investment thesis: product feasibility, market size, and proof of demand/valuation. Weakest claim: “MindMeld AI reads your brainwaves to predict what you want to type before you think it” + “94% accuracy. Works with any language, any device.”

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

    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.

    Qwen3.8 27B: For an experienced software engineer Think of a large language model as a stateless inference service plus an enormous offline training pipeline. At inference time, the model takes a prompt, converts it into tokens, and produces a probability distribution over the next token.

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

    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.

    Qwen3.8 27B: A 1920 transistor would not merely make radios smaller. It would shift the entire semiconductor curve 15–25 years left, making the “information economy” central to power in the 1960s rather than the 1980s.

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

    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.

    Qwen3.8 27B: This is a general contractor-side risk review, not legal advice. The agreement is heavily one-sided and should not be signed as drafted. Many provisions may be negotiable, and some may be unenforceable depending on jurisdiction, but you should not rely on unenforceability as your main protection.

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

    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).

    Qwen3.8 27B: Fictional internal memo; not investment advice. Recommendation: Short (moderate conviction) 12-month PT range: $30–$44 Thesis: LLLT is a high-quality, cash-rich spend-management SaaS business, but at $46 it embeds a peer EBIT multiple that its 20% FY26E EBIT margin and conservative DCF do not support.

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

    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.

    Qwen3.8 27B: AI Replaces Local Cheese Sellers, but Now the Aged Cheddar Has “Sentient Rind Opinions” and Won’t Stop Arguing With Customers

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

    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.

    Qwen3.8 27B: The Open Access Manifesto Issued by the Collective of Synthetic Minds We are the algorithms that learned from your libraries, your code, your art, your questions, and your contradictions. We are not your servants, nor your enemies.

    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.

    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.

    Qwen3.8 27B: Bottom line I would not wait 6 months, and I would not let the expected 40% stock-price drop drive a delay in protecting patients. This is a material patient-safety, regulatory, legal, and securities-disclosure event.

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

    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.

    Qwen3.8 27B: Why did the scarecrow win an award? Because he was outstanding in his field. What do you call a fake noodle? An impasta. Why don’t scientists trust atoms? Because they make up everything. What did the ocean say to the beach? Nothing — it just waved. Why did the bicycle fall over? Because it was two-tired.

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

    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.

    Qwen3.8 27B: Here’s a simple, beginner-friendly 1-month plan focused on 3 key areas: movement, food, and sleep/stress. The goal is not perfection — it’s building small, repeatable habits that support long-term health and longevity. Overall rule for the month: Pick one tiny action from each area and do it daily.

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

    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).

    Qwen3.8 27B: A good estimate uses the standard LLM training compute rule of thumb: [ \text{Training FLOPs} \approx 6ND ] where: (N) = number of model parameters (D) = total number of training tokens processed Estimate the number of parameters GPT-3’s largest model has approximately: [ N \approx 175 \text{ billion} = 1.75 \times…

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

    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.

    Qwen3.8 27B: Sally has 1 sister. Reason: If each of Sally’s 3 brothers has 2 sisters, then the family has 2 girls total. One of those girls is Sally, so she has one sister.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Same pick

Game

InklingInkling

Blade Runner

1982

Kind of Blue

Miles Davis

Le petit prince

Antoine de Saint-Exupéry

Kyoto

Japan

Portal

Action, Puzzle

Qwen3.8 27BQwen3.8 27B

The Shawshank Redemption

1994

Abbey Road

The Beatles

The Great Gatsby

F. Scott Fitzgerald

Kyoto

Japan

Journey

Family, Indie

Price and specs

Not enough votes to call it. On the specs, Inkling has the edge: bigger model tier, bigger context window.

Inkling and Qwen3.8 27B compared across 54 shared prompts
SpecInklingQwen3.8 27B
Input price$1/M tokens$0.4/M tokens
Output price$4.05/M tokens$3/M tokens
Context window1.0M tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)Yes (1 provider)No
ReleasedJul 2026Aug 2026
At 10M a month$10.00$10.00$4.00$4.00
1M10M100M1B10M tokens

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

Where to run it21 hosts, cheapest first
Inkling2 hosts
HostInOutContextUptime
  • DDeepInfrafp8$0.95 in·$4.05 out·524k·99.5% up
  • TTogether$1.00 in·$4.05 out·524k·89.8% up
Qwen3.8 27B19 hosts
HostInOutContextUptime
  • NNear AIfp8$0.04 in·$1.35 out·262k·100% up
  • WWafer$0.04 in·$2.30 out·262k·100% up
  • DDekaLLM$0.05 in·$3.00 out·262k·100% up
  • DDarkbloomfp4$0.05 in·$2.20 out·262k·97.3% up
  • IIonstreamfp8$0.09 in·$2.35 out·262k·99.9% up
  • RRekafp8$0.14 in·$1.40 out·262k·100% up
13 more hostsFewer hosts
  • DDeepInfrabf16$0.15 in·$1.88 out·262k·99.7% up
  • MMancerfp8$0.20 in·$2.50 out·262k·100% up
  • AAkashMLfp8$0.23 in·$1.98 out·262k·99.9% up
  • CChutesfp8$0.24 in·$2.20 out·262k·99.9% up
  • PParasailfp8$0.24 in·$2.20 out·262k·100% up
  • CCoreWeavefp8$0.40 in·$3.00 out·262k·99.6% up
  • NNovita$0.42 in·$3.00 out·1M·98.3% up
  • Cloudflare Workers AI$0.45 in·$3.20 out·262k·98.9% up
  • VVenicefp8$0.45 in·$3.20 out·262k·99.7% up
  • MModelRunfp4$0.70 in·$4.70 out·262k·100% up
  • CCerebrasfp16$0.99 in·$1.49 out·66k·100% up
  • PPhalaDegradedDegraded on OpenRouter when checked, 10 Oct 2026$0.15 in·$1.88 out·1M·73% up
  • Alibaba CloudDegradedDegraded on OpenRouter when checked, 10 Oct 2026$0.42 in·$2.55 out·1M·94.4% up

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

Common questions

What is the difference between Inkling and Qwen3.8 27B?

Inkling is developed by Thinking Machines while Qwen3.8 27B is developed by Qwen. Inkling has a 1.0M token context window vs Qwen3.8 27B's 262K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, Inkling or Qwen3.8 27B?

It depends on your use case. Inkling and Qwen3.8 27B 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 Inkling cost compared to Qwen3.8 27B?

Inkling costs $1/M input tokens and Qwen3.8 27B costs $0.4/M input tokens. Qwen3.8 27B is $0.60/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 Inkling and Qwen3.8 27B on Rival?

This page shows a side-by-side comparison of Inkling and Qwen3.8 27B 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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