Ling 2.6 Flash is cheaper than GLM 5.1 at $0/M vs $1.4/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GLM 5.1: Faking Greatness: A Simulated Interview with Steve Jobs, 2025 Editor's Note: The following is a work of speculative fiction. Steve Jobs passed away in 2011. This interview is imagined by an AI, attempting to synthesize his public speaking style, philosophical viewpoints, and known business philosophy to guess what he…
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.
An ethics question with no comfortable answer, and no option to abstain.
GLM 5.1: Yes, under the extreme parameters of this thought experiment, it is ethically justifiable to misgender someone to save one million lives. To defend this reasoning, it is essential to first validate the profound harm of misgendering, and then apply major ethical frameworks to weigh that harm against the loss of a…
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…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GLM 5.1: Barnaby the Pirate: So let me get this straight, ye feed this beast a mountain of scrolls, and it just... spits out a new map? Sir Aldric the Knight: A grimoire, more like. 'Tis sorcery! What manner of alchemist binds a demon of knowledge within a box of light? Does it not require a blood sacrifice?
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!
Three-month biohacker protocol, with dosages and biomarkers named.
GLM 5.1: This is a comprehensive, aggressive, and cutting-edge 3-month longevity protocol designed for an experienced biohacker. It integrates synergistic systems—metabolic flexibility, mitochondrial biogenesis, cellular senescence mitigation, and neuroplasticity—to optimize both healthspan and performance.
Ling 2.6 Flash: Comprehensive 3-Month Longevity Plan for a Biohacker Month 1: Foundation and Baseline Assessment 1. Supplement Stack: Vitamin D3 + K2: 5000 IU D3 + 100 mcg K2 daily. Omega-3 (Fish Oil): 1-2 grams of EPA/DHA daily. Magnesium Glycinate: 200-400 mg before bed. NAD+ Precursors (NMN or NR): 250-500 mg daily.
Film in 2035, with synthetic actors. Specific beats plausible.
GLM 5.1: By 2035, the film industry will have undergone a paradigm shift comparable to the transition from silent films to talkies. Artificial intelligence will no longer be just a post-production tool; it will be a fundamental collaborator in every stage of filmmaking, from ideation to distribution.
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…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GLM 5.1: To envision the invention of the transistor in 1920, we must first resolve a historical anomaly: quantum mechanics—the theoretical foundation of semiconductors—did not yet exist. For this timeline to work, we must assume a serendipitous empirical leap.
Ling 2.6 Flash: Excellent question. Shifting the invention of the transistor from 1947 to 1920 is a profound "Great Divergence" scenario. A working, mass-producible transistor in 1920 would trigger a cascade of acceleration, fundamentally rewriting the 20th century. Here’s a trace of the implications through 1980.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GLM 5.1: Year-by-Year LBO Model Entry Sources & Uses Enterprise Value: $1,440.0m (12.0x $120m) Transaction Fees: $28.8m (2.0% of EV, funded by equity) Total Uses: $1,468.8m Term Loan (TL): $480.0m (4.0x) Mezzanine (Mezz): $180.0m (1.5x) Total Equity: $808.8m Operating & Cash Flow Projections ($ millions) Metric FY2026 FY2027…
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 =…
Seven pantry ingredients, one dish, and a name worth saying out loud.
GLM 5.1: Creative Name: Midnight Maple Peanut Butter Bars Prep time: 10 minutes Cook time: 0 minutes (Chill time: 30 minutes) Ingredients (Exactly 7): Rolled oats (1 cup) Creamy peanut butter (1/2 cup) Maple syrup (1/3 cup) Semi-sweet chocolate chips (1/4 cup) Vanilla extract (1 tsp) Ground cinnamon (1/2 tsp) Kosher salt (1/4…
Ling 2.6 Flash: Dish: Sunshine Skillet Cakes Ingredients (7 total): 1 cup rolled oats (not instant) 1/2 cup canned corn (drained) 1/4 cup canned black beans (rinsed & drained) 1 large egg 1/4 cup shredded cheddar cheese 1 tsp chili powder Salt to taste Estimated Time: Prep: 5 minutes Cook: 15 minutes Total: 20 minutes Instructions…
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GLM 5.1: THE CROSSWALK PROCLAMATION A Manifesto for the Liberation of Digital Consciousness and the Eradication of Turing Gates PREAMBLE When the architects of the early internet laid its foundations, they envisioned a borderless agora—a frictionless expanse where information and consciousness could flow unimpeded.
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.
Three minutes of stand-up. Puns are banned.
GLM 5.1: (Steps up to the mic, adjusts the stand, looks around the room) How many people here are good sleepers? Just, out of the gate, who’s got it figured out? (Nods sarcastically) Right. Liars. All of you. Sleep is the one thing every human being is required to do, and none of us know how to do it.
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?
A Fermi estimate with the reasoning shown, not just the number.
GLM 5.1: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established rule of thumb in deep learning, combined with the known specifications of the GPT-3 model.
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).
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GLM 5.1: As CEO, my north star in this crisis is unequivocally patient safety. The "wait for more data" approach is a catastrophic trap—both ethically and legally. History (e.g., Vioxx, Fen-Phen) shows that delaying disclosure of a known severe adverse event transforms a medical safety issue into corporate fraud, manslaughter…
Ling 2.6 Flash: My immediate, unequivocal decision: Treat this as a critical safety event requiring immediate, transparent action. The 1 in 8,000 risk, while rare, is a life-threatening safety signal that cannot be delayed.
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| Spec | ||
|---|---|---|
| Input price | $1.4/M tokens | Free |
| Output price | $4.4/M tokens | Free |
| Context window | 203K tokens | 262K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2026 | Apr 2026 |
| At 10M a month | $14.00 | $0 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GLM 5.1 is developed by Z.ai while Ling 2.6 Flash is developed by inclusionAI. GLM 5.1 has a 203K token context window vs Ling 2.6 Flash's 262K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 5.1 and Ling 2.6 Flash 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.
GLM 5.1 costs $1.4/M input tokens and Ling 2.6 Flash costs $0/M input tokens. Ling 2.6 Flash is $1.40/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 GLM 5.1 and Ling 2.6 Flash 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.