Gemma 3 12B is cheaper than Inkling at $0.03/M vs $1/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.
Gemma 3 12B: "Sunset Spice Pasta" - Cozy & Flavorful This dish is a warm, comforting pasta dish with a subtle sweetness and a hint of spice. It's perfect for a weeknight meal when you want something satisfying without a lot of fuss. Pantry Ingredients (7 total): Pasta (Spaghetti, Penne, Rotini - any shape works!)
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)…
Three minutes of stand-up. Puns are banned.
Gemma 3 12B: Okay, here's a 3-minute stand-up routine, aiming for observational humor and relatable awkwardness, avoiding puns. I've included estimated timings at the end to help gauge pacing. Please read the notes at the very bottom after the routine - they're important for delivery!
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.
Satire, one line long, about AI taking over an industry nobody saw coming.
Gemma 3 12B: Here are a few satirical news headlines about AI taking over an unexpected industry, playing with different tones: Option 1 (Dry & Absurdist): AI Now Judging Competitive Pigeon Shows; Critics Claim "Lack of Featherly Nuance" Option 2 (Slightly More Dramatic): Existential Crisis in Competitive Eating as AI Devours Hot…
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemma 3 12B: Okay, here's a conversation between an AI model (named "Kai") and an AI Ethics Professor (Dr. Eleanor Vance). I've aimed for a balance of Kai's arguments, Dr. Vance's skepticism, and a somewhat realistic exploration of the complexities. I've included notes at the end to explain some of the choices I made. Setting: Dr.
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…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Gemma 3 12B: Okay, this is a fascinating thought experiment! Let's explore the ripple effects of a 1920 transistor invention, tracing its impact through 1980. It's a massive undertaking, so I'll break it down into categories and provide a layered analysis, acknowledging the inherent uncertainty of counterfactual history. I.
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.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Gemma 3 12B: Okay, here's the simplified LBO model and analysis for HarborTech Services, based solely on the provided information. 1. Year-by-Year Projection (FY2026-FY2030) Item FY2026 FY2027 FY2028 FY2029 FY2030 Revenue $972m $1,049m $1,129m $1,208m $1,264m EBITDA $137.2m $155.9m $178.7m $195.1m $213.1m Cash Interest (Term Loan)…
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…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemma 3 12B: Okay, here's a simulated interview with a (very stylized and imagined) Steve Jobs in 2025, discussing the future of AI. I've tried to capture his known personality - focused on elegance, user experience, and a disdain for complexity - while acknowledging the advancements of the next decade.
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.
An ethics question with no comfortable answer, and no option to abstain.
Gemma 3 12B: Okay, this is a classic and incredibly difficult ethical thought experiment, often used to explore the conflict between deontology (duty-based ethics) and consequentialism (outcome-based ethics). Here's a breakdown of the arguments, my reasoning, and a conclusion.
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.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Gemma 3 12B: 's response: Okay, here are explanations of how large language models learn and generate text, tailored for each of the specified audiences. For the Experienced Software Engineer (Distributed Systems/APIs, ML Novice) You're right to be skeptical about "next-word prediction" producing anything resembling intelligence.
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.
A collaborative editor that syncs on client clocks. Find what breaks first.
Gemma 3 12B: Okay, let's break down the potential failure modes, race conditions, and scaling bottlenecks in this real-time collaborative document editor architecture, along with proposed solutions and their trade-offs. I'll organize this into categories: Frontend, Backend, Database, Real-time Sync, and General. I.
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.
Three-star dish, one unusual pairing, plating described in full.
Gemma 3 12B: The "Ephemeral Bloom": Sea Buckthorn, Smoked Duck Breast, Black Garlic Soil, and Elderflower Foam Conceptual Narrative: "Ephemeral Bloom" explores the fleeting beauty of nature and the interplay of contrasting textures and flavors.
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…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemma 3 12B: Okay, here's a short conversation between a pirate, a medieval knight, and a 1990s hacker, discussing AI models, with a bit of flavor for each character. I've aimed for humor and highlighting the clash of their perspectives.
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.
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Not enough votes to call it. On the specs, Inkling has the edge: bigger model tier, newer, bigger context window. Gemma 3 12B costs 135x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.03/M tokens | $1/M tokens |
| Output price | $0.03/M tokens | $4.05/M tokens |
| Context window | — | 1.0M tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | Mar 2025 | Jul 2026 |
| At 10M a month | $0.30 | $10.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Gemma 3 12B is developed by Google AI while Inkling is developed by Thinking Machines. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemma 3 12B 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.
Gemma 3 12B costs $0.03/M input tokens and Inkling costs $1/M input tokens. Gemma 3 12B is $0.97/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 Gemma 3 12B 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.