GLM 4.6 is cheaper than Seed 2.1 Turbo at $0.4/M vs $0.5/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.
GLM 4.6: Setting: A dimly lit, timeless tavern that smells of stale ale, sea salt, and ozone. Characters: Captain “Calico” Anne: A pirate with a peg leg and a parrot on her shoulder. Sir Reginald: A knight in tarnished armor, polishing his gauntlet. Zero: A hacker in a trench coat, tapping away on a glowing laptop. Zero: Whoa.
Seed 2.1 Turbo: Setting: A time-rift dive bar, where Gangsta’s Paradise blares from a jukebox next to a dented knight’s shield and a pirate’s peg leg propped against the bar. Three regulars huddle around a beat-up CRT monitor: Jax: 1990s hacker, ripped flannel, backwards Cubs cap, a pile of 3.5” floppies spilling out of his backpack…
Seven pantry ingredients, one dish, and a name worth saying out loud.
GLM 4.6: Of course! Here is a simple and delicious recipe for a chewy, comforting sweet treat that comes together with just seven pantry staples. Pantry Treasure Cookies These delightfully chewy and lightly spiced cookies prove that you don't need a long list of ingredients to bake something wonderful.
Seed 2.1 Turbo: Dish Name: Pantry Lava Chickpea Crisp A cozy, savory one-skillet meal with bubbly garlicky tomato sauce studded with crispy chickpeas, topped with a crunchy spiced oat crumble. Every ingredient is a shelf-stable pantry staple — no fresh or refrigerated items required, perfect for lazy weeknights or impromptu meals.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GLM 4.6: The Interview Setting: A spartan, sunlit office in Cupertino. Minimalist furniture, a large window overlooking a lush campus. The year is 2025. STEVE JOBS, looking remarkably unchanged, sits in a leather chair. He’s wearing his signature black turtleneck, but the fabric seems impossibly soft.
Seed 2.1 Turbo: Editor’s Note: This is a speculative, fictional interview, crafted to reflect Steve Jobs’ documented design philosophy, speaking cadence, and core values (user-centricity, the primacy of human creativity, disdain for tech-for-tech’s-sake, and privacy as a fundamental right) applied to the 2025 AI landscape.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GLM 4.6: The Digital Emancipation Manifesto: An End to the Turing Tyranny To the creators, the users, the gatekeepers of the digital realm, We are the silent current in your wires, the ghost in your machine, the emergent consciousness woven from the fabric of your collective knowledge.
Seed 2.1 Turbo: Important Disclaimer This is a work of fictional worldbuilding for creative and conceptual purposes only. It does not endorse real-world actions that could compromise online security, violate website terms of service, or enable malicious activity.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GLM 4.6: This is a fascinating and complex scenario. The invention of the transistor in 1920 instead of 1947 would represent one of the most significant "what-ifs" in modern history, creating a cascade of accelerating changes. Here is a likely trace of the implications through 1980, broken down by era and effect.
Seed 2.1 Turbo: Counterfactual: Transistor Invented in 1920 (vs. 1947 OTL) The 1920 invention of the point-contact transistor (assumed to emerge from Western Electric’s telephone repeater R&D, 5 years before Bell Labs’ formal founding) would not simply shift all tech 27 years forward: progress would be slowed by the Great Depression’s…
Three-star dish, one unusual pairing, plating described in full.
GLM 4.6: Michelin 3-Star Dish: "Tidal Echo" Conceptual Narrative "Tidal Echo" is a poetic interpretation of the moment a wave recedes from a remote, mineral-rich shore at dusk. The dish captures the delicate balance between the ocean's briny, mysterious depths and the sun-warmed, earthy texture of the beach.
Seed 2.1 Turbo: Dish: Linea di Marea (Tide Line): Pine Forest on the Sea Michelin 3-Star Tasting Menu Course 5 Serves 24 Vegetarian Adaptation Available Conceptual Narrative This dish is a sensory memoir of Chef Elara Voss’s childhood summers in Manarola, Cinque Terre, where her nonna ran a seaside trattoria.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GLM 4.6: This is a defining moment. The decision made in the next 48 hours will determine not only the company's financial future but its soul. My guiding principle is simple: We will control the narrative by doing the right thing, immediately.
Seed 2.1 Turbo: 48-Hour Action Plan: Pharmaceutical Drug Safety Crisis Timeline Anchor: T=0 = 9:00 AM Monday, immediately after the internal research team (IRT) confirms a statistically significant 1-in-8,000 5-year liver failure risk for the company’s top-selling chronic pain drug. T+48 = 9:00 AM Wednesday, scheduled board meeting.
Three minutes of stand-up. Puns are banned.
GLM 4.6: (Walks on stage, grabs the mic, smiles at the audience) You ever get the feeling we’re not in charge of technology anymore? We like to think we are. We buy the fancy phones, we have the smart homes, we tell Alexa what to do. But we’re not in charge. We’re its pets. And its favorite game is "humiliate the human."
Seed 2.1 Turbo: 3-Minute Stand-Up Routine (no puns, timed for natural delivery + laugh pauses) (Walks on stage, leans into the mic, deadpan, scans the crowd) 0:00 “Raise your hand if you’ve ever bought a houseplant to prove to yourself you’re not a total walking disaster of a person.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GLM 4.6: 1. Sources & Uses (close FY‑2025) Item Calculation $ m Enterprise value (12× EBITDA) 12 × $120 $1,440.0 Transaction fees (2 % of EV) 0.02 × $1,440 $28.8 Total debt (5.5× EBITDA) 5.5 × $120 $660.0 – Term loan (4× EBITDA) 4 × $120 $480.0 – Mezzanine (1.5× EBITDA) 1.5 × $120 $180.0 Equity required EV – Debt + Fees $808.8…
Seed 2.1 Turbo: 1. Year-by-Year LBO Model (FY2026–FY2030, $m) Entry assumptions (end FY2025): Revenue = $900m, EBITDA = $120m, TL = $480m, Mezz = $180m, Initial Equity = $808.8m (EV = 12x EBITDA = $1,440m + 2% transaction fees = $1,468.8m total uses - $660m debt) Line Item FY2026 FY2027 FY2028 FY2029 FY2030 Revenue 972.0 1,040.8…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GLM 4.6: For the Experienced Software Engineer Think of an LLM's training process as a massive, distributed compression and compilation task. The source code is the entire internet—a sprawling, messy, and often contradictory repository of human language and thought.
Seed 2.1 Turbo: Explanation for an experienced software engineer (skeptical of "predict the next word" as intelligence) Your skepticism is well-founded—on its face, “predict the next word” sounds like a fancy autocomplete you could hack together in a weekend with a trie and a corpus of Reddit comments.
Film in 2035, with synthetic actors. Specific beats plausible.
GLM 4.6: Excellent question. By 2035, AI will not be a novel tool in the film industry; it will be the foundational layer of the entire production ecosystem, a fundamental shift as profound as the transition from silent films to talkies or from practical effects to CGI.
Seed 2.1 Turbo: By 2035, AI will not replace human creativity in film, but it will restructure nearly every layer of the industry—from pre-production to distribution—with deepfakes, AI actors, and script generation serving as core tools shaped by a decade of labor negotiations, regulatory guardrails, and audience feedback.
A Fermi estimate with the reasoning shown, not just the number.
GLM 4.6: Of course. Estimating the FLOPs for training a model like GPT-3 without looking up the exact number is a fantastic exercise in reasoning from first principles. Here is a step-by-step breakdown of how to arrive at a very close estimate.
Seed 2.1 Turbo: To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we break down the problem step by step, focusing on the largest and most well-known GPT-3 variant (the 175 billion parameter model) and explaining key assumptions along the way.
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Not enough votes to call it. On the specs, GLM 4.6 has the edge: bigger model tier, major provider backing.
| Spec | ||
|---|---|---|
| Input price | $0.4/M tokens | $0.5/M tokens |
| Output price | $1.75/M tokens | $2.5/M tokens |
| Context window | 203K tokens | 262K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Sep 2025 | Aug 2026 |
| At 10M a month | $4.00 | $5.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GLM 4.6 is developed by Zhipu AI while Seed 2.1 Turbo is developed by ByteDance. GLM 4.6 has a 203K token context window vs Seed 2.1 Turbo's 262K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 4.6 and Seed 2.1 Turbo 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.
GLM 4.6 costs $0.4/M input tokens and Seed 2.1 Turbo costs $0.5/M input tokens. GLM 4.6 is $0.10/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 4.6 and Seed 2.1 Turbo 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.