GPT OSS 20B is cheaper than Inkling at $0.02/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.
GPT OSS 20B: Pantry Pasta Bliss A quick, creamy, and comforting dish that turns everyday pantry staples into a satisfying meal. Ingredients (7 items) 8 oz spaghetti (or any pasta you have on hand) 2 Tbsp olive oil 3 cloves garlic, minced 1 (14.5 oz) can diced tomatoes (drained) 1 cup grated Parmesan cheese 1 tsp dried oregano 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)…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT OSS 20B: Pirate: "Arrr, lads! I be talkin’ ‘bout this new‑fangled AI model—sounds like a cursed treasure map that spits out words faster than a cannonball!" Knight: "Sir — I beseech thee, speak plain. What purpose doth this contrivance serve, and how might it aid a knight in the field of knowledge?" Hacker: "Yo, fam.
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.
Film in 2035, with synthetic actors. Specific beats plausible.
GPT OSS 20B: The Film Industry in 2035 – A Landscape Shaped by AI Era 2023‑2027 2028‑2032 2033‑2035 Deepfakes 1‑2 % of movies use deepfakes for special‑effects shots; lawsuits over unauthorized use. Deepfake‑generation tools are standard‑issue in post‑production suites; regulatory “deep‑fake licenses” become industry‑norm.
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.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT OSS 20B: What would a 1920 transistor mean? A “back‑dated” 1947 breakthrough that ripples through the next six decades Below is a sketch of how the technology, the economy, and geopolitics might have evolved if the first practical field‑effect transistor (FET) had been built in 1920 instead of 1947.
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.
Three minutes of stand-up. Puns are banned.
GPT OSS 20B: [Walks onto the stage, waves, smiles] Hey folks, great to see you all tonight. You know, I was driving this morning, and I realized something: my GPS is basically a motivational speaker. “In 300 feet, turn left.” “You’re almost there.”
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.
Three-star dish, one unusual pairing, plating described in full.
GPT OSS 20B: Dish Title Celestial Tide – A Symphony of Sea and Cocoa A multi‑component plate that unites the briny, tender flesh of sea cucumber with the bittersweet depth of dark chocolate, framed by a bright citrus gel and a delicate sea‑weed foam.
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…
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT OSS 20B: Below is a “code‑review” of the design you posted. I’ve broken it down into failure modes / race conditions / bottlenecks and for each one I provide a concrete fix (or set of fixes) together with the main trade‑offs you’ll pay for it.
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-month biohacker protocol, with dosages and biomarkers named.
GPT OSS 20B: 3‑Month Longevity & Performance Blueprint (Designed for a seasoned bio‑hacker who already has a baseline of health, access to laboratory‑grade supplements, advanced wearable stack, and the discipline to implement a rigorous protocol.) Table of Contents Section What you’ll find Approx. Time to Read 0.
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.
A Fermi estimate with the reasoning shown, not just the number.
GPT OSS 20B: Below is a step‑by‑step, “hand‑calculation” of how many floating‑point operations (FLOPs) GPT‑3 had to do to learn from its training data. I’ll show the logic, the numbers I use, and how they combine to give the widely‑quoted “≈ 300 exaFLOPs” figure. (All the math can be done in a spreadsheet or a simple calculator.)
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).
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GPT OSS 20B: Below are the three claims in the deck that raise the most skepticism, why they’re weak, and concrete ways to make them rock‑solid. # Claim Why it’s weak (evidence / logic) How to strengthen it 1 “Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94 % accuracy.”
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.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT OSS 20B: 1. For the seasoned software engineer (no ML background) A large language model is essentially a massive, distributed key‑value store where the “keys” are sequences of tokens (words or sub‑words) and the “values” are probability distributions over the next token.
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 consulting contract with traps in it. Find them, then fix them.
GPT OSS 20B: Below is a point‑by‑point review of the contract. For each clause I identify: What makes it exploitable for the contractor. How to re‑write it so the contractor is protected. The legal principle(s) that justify the change.
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.
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Not enough votes to call it. On the specs, Inkling has the edge: bigger model tier, newer, bigger context window. GPT OSS 20B costs 40x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.02/M tokens | $1/M tokens |
| Output price | $0.1/M tokens | $4.05/M tokens |
| Context window | 131K tokens | 1.0M tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | Aug 2025 | Jul 2026 |
| At 10M a month | $0.20 | $10.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GPT OSS 20B is developed by OpenAI while Inkling is developed by Thinking Machines. GPT OSS 20B has a 131K token context window vs Inkling's 1.0M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT OSS 20B and Inkling each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
GPT OSS 20B costs $0.02/M input tokens and Inkling costs $1/M input tokens. GPT OSS 20B is $0.98/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 GPT OSS 20B 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.