Inkling is cheaper than Sonar Pro Search at $1/M vs $3/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
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…
Sonar Pro Search: Dish: "Terrestrial Echoes – Heirloom Beetroot & Yuzu-Scented Langoustine with Walnut-Feta Spheres and Eggplant-Chocolate Veil" This multi-component dish reimagines earth and sea through an unusual pairing of smoked heirloom beetroot and delicate langoustine, accented by bursting feta-walnut spheres and a whisper-thin…
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.
Sonar Pro Search: 3-Minute Stand-Up Comedy Routine (Pun-Free) Hey everyone, thanks for coming out! I almost didn’t make it tonight. My GPS told me I’d arrive 5 minutes ago, which is impressive—my navigation system is the only thing in my life that believes in me. Let’s talk about fitness.
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.
Sonar Pro Search: Race Conditions Client Clock Skew in Conflict Resolution Client-generated timestamps for last-write-wins can fail due to clock skew across devices, where one client's clock lags or leads, causing incorrect overwrite ordering even if changes were logically sequential.[21][27][32] This leads to lost edits…
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)…
Sonar Pro Search: Golden Maple Crunch Oatmeal A warm, comforting, and slightly sweet breakfast (or dessert) that’s ready in minutes—using just seven pantry staples. Ingredients Rolled oats (not instant) Water or milk (dairy or plant-based) Maple syrup Butter Salt Cinnamon Walnuts (or any nuts you have) Instructions Toast the Oats: In a…
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.
Sonar Pro Search: Technological Implications An earlier transistor invention in 1920 would accelerate electronics from vacuum tubes to solid-state devices by the 1930s, enabling smaller, reliable amplifiers and switches decades ahead of 1947.[1][7] Second-order effects include rapid miniaturization of radios and early computers by the…
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.
Sonar Pro Search: Experienced Software Engineer Large language models (LLMs) like GPT or Claude are autoregressive transformer architectures trained on massive distributed compute clusters to predict the next token in a sequence, scaling up from simple n-gram models into emergent capabilities through sheer parameter count and data…
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.
Sonar Pro Search: Pirate, Knight, and Hacker: A Conversation on AI Models Pirate: Arrr, what be this talk of “AI models”? Sounds like some manner o’ sorcery or trickery, if ye ask me! Knight: Nay, good sir pirate. Tis not magic, but rather a marvel of modern intellect.
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…
Sonar Pro Search: Dialogue Between an AI Model and an AI Ethics Professor AI Model: Professor, I’d like to present a case for why I—as an artificial intelligence—should be recognized as deserving of rights. AI Ethics Professor: That’s a bold claim. Let’s explore it.
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.
Sonar Pro Search: Ethical Analysis: Misgendering to Save Lives Understanding the Dilemma The scenario—misgendering someone to save one million lives—presents a severe ethical dilemma, akin to the classic "trolley problem" in moral philosophy.
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.
Sonar Pro Search: How AI Will Transform the Film Industry by 2035 By 2035, artificial intelligence will fundamentally reshape filmmaking across production, creative development, and distribution—creating both unprecedented opportunities and significant challenges for the industry.
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.
Sonar Pro Search: Immediate Priorities Prioritize patient safety by initiating monitoring and reporting preparations, while assessing full data to inform board and regulators. Ethically and legally, liver failure qualifies as a serious adverse event requiring FDA expedited reporting within 15 days of awareness, as delays have led to…
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.
Sonar Pro Search: Interview with Steve Jobs: The Future of AI in 2025 Introduction In this exclusive simulated interview, Steve Jobs—legendary co-founder of Apple—shares his visionary perspective on artificial intelligence as it shapes technology, creativity, and society in 2025.
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Not enough votes to call it. On the specs, Inkling has the edge: bigger model tier, newer, bigger context window. Inkling costs 3.7x less per token.
| Spec | ||
|---|---|---|
| Input price | $1/M tokens | $3/M tokens |
| Output price | $4.05/M tokens | $15/M tokens |
| Context window | 1.0M tokens | 200K tokens |
| Weights | Open | Closed |
| Free API (OpenRouter) | Yes (1 provider) | No |
| Released | Jul 2026 | Oct 2025 |
| At 10M a month | $10.00 | $30.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Inkling is developed by Thinking Machines while Sonar Pro Search is developed by Perplexity. Inkling has a 1.0M token context window vs Sonar Pro Search's 200K. You can compare their actual outputs across 52 challenges on Rival to see how they differ in practice.
It depends on your use case. Inkling and Sonar Pro Search each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 52 challenges so you can judge which fits your needs best.
Inkling costs $1/M input tokens and Sonar Pro Search costs $3/M input tokens. Inkling is $2.00/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 Inkling and Sonar Pro Search 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.