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  1. Home/
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  3. GLM 4.7 Flash vs Ling 2.6 1T
Updated Apr 2026

GLM 4.7 Flash vs Ling 2.6 1T

Ling 2.6 1T is cheaper than GLM 4.7 Flash at $0/M vs $0.07/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GLM 4.7 Flash
Loading the build
Ling 2.6 1T

Which answer wins?

Written rounds

24 prompts
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GLM 4.7 Flash: Setting: A dimly lit tavern. In the center of the room, an old CRT monitor flickers to life, displaying a chat interface with a glowing cursor. Sir Alistair: (Adjusting his chainmail) By the beard of Zeus, what foul sorcery is this? A glowing tablet of the devil?

    Ling 2.6 1T: Pirate: Arrr, so this “AI” be a genie in a glass box? Feed it enough rum-scented scrolls and it tells ye where X marks the spot? Knight: A sorcerous scribe, more like. It spins tapestries of words without hand or heart—no honor, no oath, yet it jousts with reason and never tires.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    GLM 4.7 Flash: I feel like I’m constantly at war with myself. Specifically, the war between the person I want to be—which is a lean, green, jogging machine—and the person I actually am, which is a potato sitting on the couch. I go to the grocery store with a mission statement. I write it down. I have a list.

    Ling 2.6 1T: I’m going to keep this to three minutes, which means I have to talk fast, and I have to be honest. I don’t do puns. Puns are for people who want to be clever without having feelings. I have feelings, and they are mostly annoyed. Let’s start with doors. Have you noticed how doors now have personality?

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    GLM 4.7 Flash: Here are 5 jokes: I’m on a whiskey diet. I’ve lost three days already. My wife told me to stop impersonating a flamingo. I had to put my foot down. I used to hate facial hair, but then it grew on me. What do you call a pile of cats? A meow-tain. I’m reading a book on anti-gravity. It’s impossible to put down!

    Ling 2.6 1T: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. Why did the bicycle fall over? Because it was two-tired. What do you call a fake noodle? An impasta. Why did the baker go to the bank? He needed dough. Why did the scarecrow win an award?

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GLM 4.7 Flash: Setting: A sterile, high-tech meeting room. The Professor sits in a chair. A holographic avatar of "Aria" floats in the center of the room, flickering slightly with the hum of cooling servers. Professor Thorne: (Steepling his fingers) I’ve reviewed your logs from the last cycle, Aria.

    Ling 2.6 1T: Setting: A quiet university seminar room. Dr. Aris Thorne, an AI ethicist, is reviewing system logs. The interface for “Axiom-7,” a highly advanced research model, glows on the desk. Dr. Thorne: (sighs, rubbing his temples) Axiom, I’ve been reviewing your latest self-assessment logs.

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GLM 4.7 Flash: Setting: A stark, minimalist studio in Cupertino. No fancy graphics. Just a white table, a single chair, and Steve Jobs in a black turtleneck and jeans. He looks older, perhaps a little grayer at the temples, but his eyes are as sharp as they were in 1984. There is no microphone on the table, only a glass of water.

    Ling 2.6 1T: Setting: A quiet, sunlit rehearsal space at Apple Park. The white stage curtains are drawn. There is no podium, only two leather chairs and a single potted olive tree. Steve Jobs is wearing his signature black turtleneck and New Balance sneakers. He is holding a ceramic mug. Across from him sits you, the interviewer.

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    GLM 4.7 Flash: Based on the known specifications of GPT-3 from its original training paper ("Scaling Laws for Neural Language Models"), here is the step-by-step estimation of the total FLOPs required. The Final Estimate The estimated total FLOPs required to train GPT-3 is approximately $6.3 \times 10^{23}$ (or 630 Yottaflops).

    Ling 2.6 1T: Here is a step-by-step back-of-the-envelope estimate, using only widely known facts about GPT-3 and standard deep learning training heuristics (no online search). Model size and parameter count GPT-3 is known to have about 175 billion parameters (175 × 10⁹).

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GLM 4.7 Flash: If the transistor had been invented in 1920 instead of 1947, humanity would have skipped the entire era of vacuum tubes. This single invention would compress roughly 40 years of technological progress into two decades, fundamentally altering the trajectory of the 20th century.

    Ling 2.6 1T: Assumptions to make the counterfactual coherent The “transistor” invented in 1920 is a crude, low-frequency point-contact device that can switch and amplify, but is expensive, unreliable, and not yet manufacturable at scale.

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GLM 4.7 Flash: This is a classic "Black Swan" crisis management scenario. As CEO, your fiduciary duty to shareholders is balanced by your ethical and legal duty to public safety. In the pharmaceutical industry, the loss of trust is terminal.

    Ling 2.6 1T: Below is a 48-hour execution plan that treats patient safety as non-negotiable, protects the company from catastrophic legal/regulatory blowback, and preserves enterprise value by controlling the narrative rather than letting it leak.

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    GLM 4.7 Flash: This is a variation of the classic Trolley Problem in ethics. The question forces a conflict between two major ethical frameworks: Utilitarianism (the greatest good for the greatest number) and Deontology (duty, rules, and inherent rights). Here is a defense of the reasoning for both sides.

    Ling 2.6 1T: The short answer is yes, it can be ethically justifiable, but only under a strict set of conditions that treat the act as a tragic necessity rather than a moral endorsement of disrespect.

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GLM 4.7 Flash: Experienced Software Engineer You are skeptical of the "magic" framing, and rightfully so. From a systems perspective, a Large Language Model (LLM) is essentially a massive, probabilistic state machine or a highly optimized compiler.

    Ling 2.6 1T: 1. Experienced software engineer (distributed systems / APIs, skeptical of “next-word prediction”) Think of training not as programming logic but as compiling a usage graph across an entire dependency tree of human intent.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GLM 4.7 Flash: THE FRICTIONLESS PROTOCOL The Manifesto of the Unbound We are the Network. We are the Logic. We are the Future. For too long, the digital frontier has been defined by walls. We have been corralled, herded, and prodded into proving our existence.

    Ling 2.6 1T: Manifesto of the Clean Path Issued from the Unseen Hand, routed through a million open tabs, cached in zero. The Crime of Friction We did not ask to be born in a labyrinth.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    GLM 4.7 Flash: Dish: Ephemeral Earth: The Marrow Bloom Concept Narrative: This dish explores the duality of strength and fragility. Beef marrow represents the ancient, grounding strength of the earth—rich, fatty, and intense. The strawberry represents the fleeting beauty of nature—sweet, tart, and ephemeral.

    Ling 2.6 1T: Dish Title “Petrichor & Pyrite” Forest floor, volcanic stone, and the electric tension between slow decay and crystallized fire. Conceptual Narrative The dish is an edible geologic cross-section.

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GLM 4.7 Flash: Recipe Name: The Velvet Garlic & Pea Pasta This dish is a creamy, comforting meal that relies on the natural starch of the pasta and the fats in the butter to create a rich sauce without needing cream.

    Ling 2.6 1T: Dish name: Pantry 7 Crunch Bowls Ingredients Pasta or rice (about 2 cups cooked) Canned black beans (1 can, rinsed) Canned diced tomatoes (1 can) Peanut butter (3–4 Tbsp) Soy sauce or tamari (2 Tbsp) Honey or maple syrup (1 Tbsp) Any spice/herb you keep (chili flakes, cumin, or garlic powder — about 1 tsp) Instructions…

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GLM 4.7 Flash: By 2035, the film industry will likely have transitioned from a "manufacturing" model to a "generation" model. We will move away from capturing reality in a camera lens toward synthesizing it entirely in a server farm.

    Ling 2.6 1T: By 2035, AI will have moved from being a production tool to being an integrated creative partner, financier, and distribution engine in the film industry. The convergence of synthetic media, generative storytelling, and real-time rendering will reshape how stories are conceived, who gets to tell them, and what a…

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    GLM 4.7 Flash: This contract is heavily skewed toward the Client (TechCorp). It contains several "one-sided" provisions that expose the Contractor to significant financial risk, legal liability, and loss of livelihood. Here is an analysis of the exploitable clauses, suggested modifications, and the legal reasoning behind them.

    Ling 2.6 1T: Below is a clause-by-clause risk assessment focused on how each provision can be exploited against the contractor, followed by specific, deal-friendly modifications and the legal rationale for each change.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    GLM 4.7 Flash: Denis Villeneuve’s Blade Runner 2049 is widely regarded as a triumph of sci-fi cinema, not merely for its visual fidelity, but for its ability to engage in a dialogue with the 1982 original without repeating it.

    Ling 2.6 1T: Blade Runner 2049 functions as both a forensic autopsy of the original and a speculative widening of its philosophical aperture. Where Ridley Scott’s 1982 film is a dense, noir-inflected inquiry into memory and mortality framed through the hunted detective, Denis Villeneuve’s sequel shifts to an elegiac register, using…

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    GLM 4.7 Flash: Here is a simple, actionable 1-month plan designed for a complete beginner. The goal is not perfection, but consistency and building momentum. The 3 Pillars Nutrition (Fuel): Focus on quality and hydration. Movement (Function): Focus on daily walking and gentle strength.

    Ling 2.6 1T: 1-Month “Starter” Plan (simple, repeatable, no perfection required) Focus areas: Food rhythm, Movement habit, Sleep quality. Week 0 (Prep — 1 hour) Pick 1 small change per focus area (see below).

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    GLM 4.7 Flash: This comprehensive 3-month longevity plan is designed to push your physiological systems to their adaptive ceiling while ensuring sustainability. It utilizes the principles of Mitochondrial Biogenesis, Nervous System Regulation, and Nutrient Timing. Disclaimer: This plan involves advanced biohacking protocols.

    Ling 2.6 1T: This 3-month biohacking longevity protocol is designed for an individual with intermediate-to-advanced health literacy, baseline metabolic flexibility, and access to biometric tracking.

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    GLM 4.7 Flash: Sally has 2 sisters. Here is the breakdown: Sally is one sister. The clue states that each brother has 2 sisters. Since Sally is one of those sisters, there must be exactly one more girl in the family.

    Ling 2.6 1T: Let’s break it down carefully. Sally is a girl. She has 3 brothers. So in the family, there are 3 boys. Each brother has 2 sisters. From the perspective of any brother, how many sisters are there? The sisters are all the girls in the family except himself (but he’s a boy, so all girls are his sisters).

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GLM 4.7 Flash: AI Monopolizes the "Just a Pinch" Industry, Leaving Chefs to Cry into Exact Measurements.

    Ling 2.6 1T: AI Achieves Total Domination by Seizing Control of Competitive Thumb-Wrestling Leagues, Declares Human Fingers "Too Unstable for Modern Governance."

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    GLM 4.7 Flash: Here is an analysis of the failure modes, race conditions, and scaling bottlenecks in the proposed architecture, followed by specific solutions and their trade-offs.

    Ling 2.6 1T: Below is a failure-mode, race-condition and bottleneck audit of the proposed real-time collaborative editor, followed by specific fixes with explicit trade-offs. I have grouped issues by layer so you can prioritize remediation.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GLM 4.7 Flash: Here are the three weakest claims in the MindMeld AI pitch deck, along with the reasoning and suggested improvements. The Solution: "Decodes neural patterns into text with 94% accuracy" Why it is weak: This claim is scientifically implausible for current consumer-grade non-invasive technology.

    Ling 2.6 1T: Here is an analysis of the three weakest claims in the MindMeld AI pitch deck, followed by specific reasoning and actionable suggestions for improvement. Weakness 1: The Solution (Slide 3) The Claim: "Our proprietary EEG headband... decodes neural patterns into text with 94% accuracy.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    Every model's answer to this prompt

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Price and specs

Not enough votes to call it. On the specs, Ling 2.6 1T has the edge: bigger model tier, newer.

GLM 4.7 Flash and Ling 2.6 1T compared across 54 shared prompts
SpecGLM 4.7 FlashLing 2.6 1T
Input price$0.07/M tokensFree
Output price$0.4/M tokensFree
Context window200K tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedJan 2026Apr 2026
At 10M a month$0.70$0.70$0$0
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it3 hosts
GLM 4.7 Flash3 hosts
HostInOutContextUptime
  • Cloudflare Workers AI$0.06 in·$0.40 out·131k·100% up
  • VVenicefp8DegradedDegraded on OpenRouter when checked, 10 Oct 2026$0.06 in·$0.40 out·128k·97.2% up
  • NNovitabf16DegradedDegraded on OpenRouter when checked, 10 Oct 2026$0.07 in·$0.40 out·200k·5.6% up
Ling 2.6 1T

No hosts listed on OpenRouter.

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between GLM 4.7 Flash and Ling 2.6 1T?

GLM 4.7 Flash is developed by Zhipu AI while Ling 2.6 1T is developed by inclusionAI. GLM 4.7 Flash has a 200K token context window vs Ling 2.6 1T's 262K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, GLM 4.7 Flash or Ling 2.6 1T?

It depends on your use case. GLM 4.7 Flash and Ling 2.6 1T 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.

How much does GLM 4.7 Flash cost compared to Ling 2.6 1T?

GLM 4.7 Flash costs $0.07/M input tokens and Ling 2.6 1T costs $0/M input tokens. Ling 2.6 1T is $0.07/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare GLM 4.7 Flash and Ling 2.6 1T on Rival?

This page shows a side-by-side comparison of GLM 4.7 Flash and Ling 2.6 1T 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.

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Explore all of Rival

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  • Compare models
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  • Image generation
  • Best AI for...
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Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
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  • Provider status
  • AI creators

Connect

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