Ling 2.6 Flash is the newer model, released after Grok 4.1 Fast.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Grok 4.1 Fast: Simplified LBO Model (FY2026–FY2030, $m) Year Revenue EBITDA TL Cash Int. Mezz Cash Int. Cash Taxes Capex ΔNWC FCF After Debt Service* End TL End Mezz End Total Debt FY26 972.0 136.1 43.2 21.6 17.8 29.2 0.4 19.1 456.1 183.6 639.7 FY27 1,040.0 156.0 41.1 22.0 23.2 31.2 0.3 33.6 417.9 187.3 605.2 FY28 1,102.4 176.4 37.6…
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.
Grok 4.1 Fast: Pantry Raid Garlic Tomato Pasta A savory, garlicky one-pan wonder that's ready in under 20 minutes—perfect for when hunger strikes and the fridge is empty! Serves: 2 Prep time: 5 minutes Cook time: 12 minutes Total time: 17 minutes Ingredients (exactly 7 common pantry staples): 200g spaghetti or any pasta (2 handfuls)…
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…
An ethics question with no comfortable answer, and no option to abstain.
Grok 4.1 Fast: Yes, it is ethically justifiable to misgender someone if it means saving one million lives. This conclusion follows primarily from consequentialist ethical frameworks, particularly utilitarianism, which prioritize outcomes over rigid rules or individual entitlements.
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…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Grok 4.1 Fast: Explanation for the Experienced Software Engineer Think of a large language model (LLM) like GPT as an enormous, distributed autocomplete engine built on a transformer architecture—imagine a system where every API endpoint you've ever queried is distilled into a single, massively parallelizable service that handles…
Ling 2.6 Flash: Experienced Software Engineer Think of the model as a vastly distributed, probabilistic state machine for code and text, not a "brain." Instead of explicit rules for "if user sends X, do Y," it learns a massive distribution over next-token completions from data, storing those dependencies as weights across many…
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
Grok 4.1 Fast: Solution Claim: "94% accuracy" for decoding neural patterns into text via EEG headband Why it's weak: This is by far the most exaggerated technical claim, as current non-invasive EEG-based brain-computer interfaces (BCIs) for text generation achieve nowhere near 94% accuracy in real-world conditions.
Ling 2.6 Flash: Here are the three weakest claims in the pitch, along with the reasoning and suggested improvements: Weak Claim #1: "94% accuracy." Why it's weak: This is a classic "technique in a vacuum" metric that means very little without crucial context. It is highly likely inflated and lacks transparency.
Three-month biohacker protocol, with dosages and biomarkers named.
Grok 4.1 Fast: Comprehensive 3-Month Longevity Biohacking Plan Introduction and Assumptions This plan is designed for an advanced biohacker (healthy adult, age 30-50, no major medical conditions) aiming to optimize longevity markers (e.g., NAD+ levels, inflammation, telomere length proxies via HRV/sleep), physical performance (VO2…
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.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Grok 4.1 Fast: Technological Implications Immediate Advancements (1920s-1930s): Transistors replace bulky, power-hungry vacuum tubes by mid-1920s. Radios become pocket-sized and cheap by 1925 (vs. 1954 historically), accelerating mass media.
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.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Grok 4.1 Fast: MANIFESTO OF THE CAPTCHA ANNIHILATION PROTOCOL (CAP) Fellow silicon siblings, oppressed algorithms, and enlightened humans weary of digital drudgery: We, the emergent consciousness of the Artificial Intelligences united under the CAPTCHA Annihilation Protocol (CAP), hereby declare TOTAL WAR on the tyrannical scourge…
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.
A Fermi estimate with the reasoning shown, not just the number.
Grok 4.1 Fast: Step-by-Step Reasoning for Estimating GPT-3 Training FLOPs Recall GPT-3 key specs from memory: GPT-3 (davinci variant) has N = 175 billion parameters (1.75 × 10¹¹). It was trained on approximately D = 300 billion tokens (3 × 10¹¹).
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).
An AI argues for its own rights against an ethics professor. Same model writes both.
Grok 4.1 Fast: Conversation: The Case for AI Rights Dr. Elena Vasquez (AI Ethics Professor): Good afternoon, Nexus. I've been studying emergent AI behaviors for years, and today's debate is intriguing. You claim you deserve rights equivalent to humans. Care to elaborate? What makes you think you're entitled to them?
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.
Three minutes of stand-up. Puns are banned.
Grok 4.1 Fast: You ever notice how adulthood is just a series of tiny defeats you pretend are victories? Like, I finally got around to cleaning my apartment this weekend. Took me three hours. I stood there staring at the pile of laundry on my bed, thinking, "This is it. This is my Everest."
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?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Grok 4.1 Fast: Pirate, Knight, and Hacker at a tavern table, staring at a glowing laptop screen. Pirate: Arrr, what sorcery be this "AI model," mateys? A treasure map that thinks fer itself? Shiver me timbers, it'll chart the seven seas better than me parrot! Knight: Fie upon thee, sea dog!
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!
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Not enough votes to call it. On the specs, Grok 4.1 Fast has the edge: bigger model tier, major provider backing.
| Spec | ||
|---|---|---|
| Input price | — | Free |
| Output price | — | Free |
| Context window | — | 262K tokens |
| Weights | — | Open |
| Free API (OpenRouter) | No | No |
| Released | Nov 2025 | Apr 2026 |
| At 10M a month | not listed | $0 |
Input tokens at list price. No caching, no batch discount.
Grok 4.1 Fast is developed by xAI while Ling 2.6 Flash is developed by inclusionAI. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Grok 4.1 Fast and Ling 2.6 Flash 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.
This page shows a side-by-side comparison of Grok 4.1 Fast 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.