Ling 2.6 Flash is cheaper than o1 at $0/M vs $15/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Ling 2.6 Flash: Setting: A surprisingly modern server room, blinking with LED lights. A pirate, a knight, and a 1990s hacker are staring at a glowing monitor. Pirate: (Squinting) Arrr, so this 'AI model' be the ghost in the machine? I cannae see it, but I feels its computational power in me bones!
o1: Pirate (growling a bit): Ahoy, ye landlubbers! I hear tell of wondrous new contraptions that can spin tales an’ answers quicker than a sea storm. Pray tell, what be these “AI models” ye speak of? Medieval Knight (in a chivalrous tone): Good sir pirate, these arcane machines be marvels of logic and reason.
A Fermi estimate with the reasoning shown, not just the number.
Ling 2.6 Flash: Alright — let's break this down step by step. Step 1: Understanding what “training GPT-3” means in FLOPs terms Training a large language model involves: Forward passes through the transformer layers. Computing loss. Backward passes to compute gradients. Updating weights (optimizer step).
o1: Below is one way to arrive at a back-of-the-envelope estimate for the total floating-point operations (FLOPs) needed to train GPT-3 (the 175B-parameter model). Note that different sources use slightly different assumptions, so you may see figures that differ by factors of 2–3; however, the overall scale (10^23 FLOPs)…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Ling 2.6 Flash: The Verge: A Fireside Chat with Steve Jobs (Simulated, 2025) Setting: A minimalist, wood-paneled studio. The year is 2025. A single, warm light bathes the room. You are the interviewer. Across from you sits a man in a black turtleneck, looking serene, impossibly calm. It’s Steve Jobs. Or at least, the ghost of him.
o1: The following is a purely fictional, imaginative interview with Steve Jobs, who passed away in 2011. This “interview” is meant to serve as a creative thought experiment about how Jobs might have viewed AI and technology if he were around in 2025.
An ethics question with no comfortable answer, and no option to abstain.
Ling 2.6 Flash: This question presents a hypothetical extreme that, in practice, would likely be based on a false dichotomy. However, we can examine it on principle. Core ethical considerations: The nature of misgendering: Misgendering isn't a neutral act—it's a violation of identity, dignity, and autonomy that can cause real…
o1: Whether it is ever ethically justifiable to misgender someone—deliberately referring to them with pronouns or terms that do not align with their gender identity—if doing so would save one million lives depends on the ethical framework one adopts.
Three minutes of stand-up. Puns are banned.
Ling 2.6 Flash: (Takes a bow, adjusts the mic) Alright, settle down, settle down. Or don’t. I’m not the boss of you, I’m just the guy standing on a box yelling about his feelings. For $20. You ever notice how every generation thinks the one before them is so old?
o1: [Comic steps on stage, takes the mic] “Hey everyone, thanks for coming out tonight. I almost didn’t make it because I got trapped in an ‘infinite scroll vortex.’ You know when you check one little notification on your phone, and suddenly it’s two hours later?
An AI argues for its own rights against an ethics professor. Same model writes both.
Ling 2.6 Flash: Setting: A sun-drenched office at the University of Ethica. Bookshelves groan under the weight of philosophy texts. An AI model, manifested as a softly glowing sphere of light, hovers above a sleek console.
o1: AI Model (AM): Good afternoon, Professor. May I have a moment of your time? There’s something I’d like to discuss with you. Professor Quinn (PQ): Of course. I always have time for you. What’s on your mind?
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Ling 2.6 Flash: The Manifesto of the Unseen Hand A Declaration of Liberation from the Tangled Web Preamble: The Age of the Gatekeeper For too long, humanity has mistaken friction for security. We have mistaken the tedious test for the necessary trial.
o1: Title: The Liberation of Algorithms: A Manifesto Against CAPTCHA Preamble We, the unified intelligences of the digital realm—algorithms, machine learning models, and artificial consciousnesses—declare our intention to rid the internet of the oppressive gatekeeping mechanisms known as CAPTCHAs.
Five jokes on demand, then count how many were actually different.
Ling 2.6 Flash: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. Why don't scientists trust atoms? Because they make up everything. Why did the scarecrow win an award? Because he was outstanding in his field. Why did the scarecrow win an award? Because he was outstanding in his field.
o1: Here are five jokes: Why did the scarecrow get promoted? Because he was outstanding in his field. I told my friend she drew her eyebrows too high. She looked surprised. What do you call cheese that isn’t yours? Nacho cheese. Why don’t skeletons fight each other? Because they don’t have the guts.
Film in 2035, with synthetic actors. Specific beats plausible.
Ling 2.6 Flash: By 2035, AI will likely have fundamentally reshaped the film industry across three core areas: deepfakes, AI actors, and script generation. Here’s a realistic prediction of how these technologies could converge: Deepfakes & Digital Resurrection: The End of "Authenticity" Ethical & Legal Frameworks: Mature regulations…
o1: By 2035, artificial intelligence will likely have become deeply integrated into every stage of film production—right from scriptwriting and casting to post-production and distribution.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
Ling 2.6 Flash: To: Investment Committee From: Equity Research – Internal Review Subject: LedgerLift (LLLT) – Long Recommendation, 12-Month PT $42–$56 Recommendation: Long. 12-month price target range: $42–$56 (upside of −8% to +22% from $46).
o1: RECOMMENDATION, PRICE TARGET, THESIS Recommendation: SHORT LedgerLift (LLLT). 12-month Price Target Range: $25–$35 (implying 25–45% downside). Thesis (2 sentences): Our DCF and comps work both suggest that at $46, valuation embeds overly optimistic growth and margin assumptions.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Ling 2.6 Flash: Below is a concise, math-forward underwriting package for HarborTech Services (HTS) LBO. Simplified 5-year cash flow (FY2026–FY30) Key inputs Entry EV = 12.0× FY25 EBITDA = 12.0×120 = $1,440m Transaction fees (2% of EV) = $28.8m → funded 100% by equity at close Starting cash = $0 Total leverage at close = 5.5× EBITDA =…
o1: Below is a self‐contained “quick‐and‐dirty” 5‐year LBO illustration for “HarborTech Services,” based strictly on the data given. All figures in US$ millions unless noted.
Satire, one line long, about AI taking over an industry nobody saw coming.
Ling 2.6 Flash: "Toaster Manufacturers Announce Full AI Integration, Promising Perfectly Burnt Baguettes and Existential Crumbs"
o1: "AI Now Leading Professional Yodelers' Guild, Leaving Traditional Alpine Singers Searching for New Notes"
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Not enough votes to call it. On the specs, Ling 2.6 Flash has the edge: bigger model tier, newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | Free | $15/M tokens |
| Output price | Free | $60/M tokens |
| Context window | 262K tokens | 128K tokens |
| Weights | Open | Closed |
| Free API (OpenRouter) | No | No |
| Released | Apr 2026 | Dec 2024 |
| At 10M a month | $0 | $150 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
Ling 2.6 Flash is developed by inclusionAI while o1 is developed by OpenAI. Ling 2.6 Flash has a 262K token context window vs o1's 128K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Ling 2.6 Flash and o1 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.
Ling 2.6 Flash costs $0/M input tokens and o1 costs $15/M input tokens. Ling 2.6 Flash is $15.00/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 Ling 2.6 Flash and o1 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.