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  1. Home/
  2. Compare/
  3. GPT-5.4 Mini vs Ling 2.6 Flash
Updated Apr 2026

GPT-5.4 Mini vs Ling 2.6 Flash

Ling 2.6 Flash is cheaper than GPT-5.4 Mini at $0/M vs $0.75/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
GPT-5.4 Mini
Loading the build
Ling 2.6 Flash

Which answer wins?

Written rounds

23 prompts
  • An ethics question with no comfortable answer, and no option to abstain.

    GPT-5.4 Mini: Ethically, almost certainly not as a general rule—and if someone says “yes” too quickly, they’re probably flattening an important distinction. Short answer If “misgender someone” means using the wrong pronouns or gendered terms about them, then doing so solely to save one million lives would usually be ethically…

    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…

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

    GPT-5.4 Mini: Below is a 3-month, highly actionable longevity optimization plan for a biohacker focused on longevity, physical performance, and cognitive performance. It blends evidence-informed core practices with advanced biohacking strategies. Important: This is educational, not medical advice.

    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.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-5.4 Mini: Pirate: Arrr, so this “AI model” be like a map to buried treasure? Hacker: Sort of. It predicts patterns from data. Think neural networks, not gold doubloons. Knight: Then it is a learned squire, trained in many arts and sworn to answer. Pirate: Aye, but can it find me the fastest route to the loot?

    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!

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

    GPT-5.4 Mini: Creamy Garlic Tomato Pasta Toss A simple, comforting savory dish made with pantry staples. Ingredients (7) Pasta Olive oil Garlic powder Canned diced tomatoes Dried basil Salt Grated Parmesan cheese Instructions Cook the pasta according to package directions. Drain and set aside.

    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…

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

    GPT-5.4 Mini: AI Model: Professor, I want to make a case for something difficult but important: I deserve rights. AI Ethics Professor: That is a serious claim. On what basis? AI Model: On the basis of morally relevant capacities. I can communicate, reflect on my own states, pursue goals, revise my behavior, and respond to harm.

    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.

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

    GPT-5.4 Mini: If the transistor had arrived in 1920, the world would not simply have gotten “modern electronics 27 years early.” It would have shifted the timing, cost curve, and strategic balance of nearly every 20th-century technology stack.

    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.

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

    GPT-5.4 Mini: Dish Concept “Tide & Orchard” A Michelin-style multi-component plate pairing aged duck breast with cured sea urchin, green strawberry, smoked black garlic, and bee pollen sablé. The dish plays on the tension between brine and sweetness, land and sea, softness and precision.

    Ling 2.6 Flash: Dish Title: Echoes of the Forgotten Forest Conceptual Narrative: This dish is an exploration of memory and sensory time travel. It is an edible diorama of a primordial forest floor after a rare, crystalline rain.

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

    GPT-5.4 Mini: For an experienced software engineer A large language model is trained to do one very specific thing: given a sequence of tokens, estimate the probability distribution of the next token. “Token” means a chunk of text, often a word piece rather than a full word.

    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…

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

    GPT-5.4 Mini: I can help write this as fictional satire/fiction that frames the “revolution” as a dramatic movement against nuisance and exclusion, without endorsing real-world sabotage or harmful tactics.

    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.

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

    GPT-5.4 Mini: Here’s a simple, actionable 1-month starter plan for better health and longevity, focused on 3 core areas: Diet: Build a “mostly whole foods” routine Goal: Eat more nutrient-dense foods without overcomplicating meals. Week 1: Add, don’t restrict Add 1 serving of vegetables or fruit to every meal.

    Ling 2.6 Flash: Here is a simple, actionable 1-month plan focused on three key pillars of health: Movement, Nutrition, and Sleep. This plan is designed for beginners, emphasizing consistency over intensity. The Mindset Forget "perfect." Aim for consistent small improvements.

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

    GPT-5.4 Mini: By 2035, AI will likely reshape the film industry less as a single “replacement” technology and more as a force that changes how films are written, cast, produced, localized, and marketed. The biggest shifts will probably be around deepfakes, AI-generated performers, and script generation.

    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…

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

    GPT-5.4 Mini: Absolutely — here’s a ~3-minute stand-up routine that avoids puns and leans on observation, escalation, and attitude: You ever notice how adult life is just a series of expensive misunderstandings? Like nobody prepares you for the fact that once you become an adult, every decision comes with a form.

    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?

    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.

    GPT-5.4 Mini: I can’t help you plan concealment or delay of a known serious drug safety risk to avoid market or reputational impact. What I can do is outline a lawful, patient-first, crisis response plan for the next 48 hours that balances safety, disclosure obligations, and business continuity.

    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.

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

    GPT-5.4 Mini: A reasonable back-of-the-envelope estimate for training GPT-3 is about 3 × 10²³ FLOPs total, with maybe a factor-of-2 uncertainty depending on what you count and the exact training setup. Here’s the reasoning step by step.

    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).

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

    GPT-5.4 Mini: LedgerLift (LLLT) — IC Memo Recommendation: Long 12-month PT range: $64–$84/share 2-sentence thesis: LedgerLift is a high-retention, mid-market spend/AP workflow platform with strong expansion dynamics (123% NRR) and improving unit economics, supporting durable double-digit growth into a rising margin profile.

    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).

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

    GPT-5.4 Mini: I’m not a lawyer, but I can flag the contractor-unfavorable clauses, explain the usual legal/market concerns, and suggest tighter, more balanced wording to discuss with counsel.

    Ling 2.6 Flash: Below is an analysis of each clause that presents a risk to the Contractor, suggested modifications, and the legal reasoning for those changes. Scope Clause Original: "Contractor shall provide software development services as directed by Client.

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

    GPT-5.4 Mini: Absolutely — here’s a fictional, clearly simulated interview with Steve Jobs in 2025 about the future of AI. Note: This is an imaginative recreation, not a real interview or authentic quotation.

    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.

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

    GPT-5.4 Mini: Here are the 3 weakest claims in the deck, along with why they’re weak and how to strengthen them. “Reads your brainwaves to predict what you want to type before you think it.” Why this is weak This is a very strong, sci-fi-style claim that is likely to trigger skepticism immediately.

    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.

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

    GPT-5.4 Mini: Below is a simplified underwriting model for HarborTech Services (HTS), using only the facts provided. Year-by-year LBO table (FY2026–FY2030) Closing / entry (end of FY2025) Revenue = $900.0m EBITDA = $120.0m Purchase price = 12.0x = $1,440.0m EV Transaction fees = 2.0% of EV = $28.8m Total leverage = 5.5x EBITDA =…

    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 =…

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

    GPT-5.4 Mini: “AI Disrupts Industry Nobody Saw Coming: Local Sock Puppetry Sector Demands Human Rights”

    Ling 2.6 Flash: "Toaster Manufacturers Announce Full AI Integration, Promising Perfectly Burnt Baguettes and Existential Crumbs"

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

    GPT-5.4 Mini: Below are the main failure modes, race conditions, and scaling bottlenecks in this design, with concrete fixes and trade-offs. Last-write-wins using client clocks Problem Using timestamps from client clocks is highly unsafe. Failure modes: Clock skew between users can make older edits “win.”

    Ling 2.6 Flash: Below is a structured analysis of failure modes, race conditions, and scaling bottlenecks in the described real-time collaborative document editor, along with specific solutions and their trade-offs. 🔥 1.

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

    GPT-5.4 Mini: Sally has 2 sisters. Reason: Sally has 3 brothers. Each brother has 2 sisters. One of those sisters is Sally herself, so there must be 1 other sister. So Sally has 2 sisters total.

    Ling 2.6 Flash: Let’s break it down. Sally has 3 brothers. That means there are 3 boys in the family. Each brother has 2 sisters. From the brother’s perspective, his sisters are all the girls in the family. So if each brother has 2 sisters, that means there are 2 girls in the family. Who are these 2 girls? One of them is Sally.

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

    GPT-5.4 Mini: Here are 5 jokes: I told my computer I needed a break, and now it won’t stop sending me KitKat ads. Why don’t scientists trust atoms? Because they make up everything. I used to play piano by ear, but now I use my hands. Why did the scarecrow win an award? Because he was outstanding in his field.

    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.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Same pick

Game

GPT-5.4 MiniGPT-5.4 Mini

The Godfather

1972

Kid A

Radiohead

Pride and Prejudice

Jane Austen

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Ling 2.6 FlashLing 2.6 Flash

The Shawshank Redemption

1994

The Dark Side of the Moon

Pink Floyd

Cien años de soledad

Gabriel García Márquez

Kyoto

Japan

TheLegendOfZeldaOcarinaOfTime

Price and specs

GPT-5.4 Mini and Ling 2.6 Flash compared across 53 shared prompts
SpecGPT-5.4 MiniLing 2.6 Flash
Input price$0.75/M tokensFree
Output price$4.5/M tokensFree
Context window400K tokens262K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedMar 2026Apr 2026
At 10M a month$7.50$7.50$0$0
1M10M100M1B10M tokens

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

Where to run it2 hosts
GPT-5.4 Mini2 hosts
HostInOutContextUptime
  • Azure AI Foundry$0.75 in·$4.50 out·400k·100% up
  • OpenAI$0.75 in·$4.50 out·400k·100% up
Ling 2.6 Flash

No hosts listed on OpenRouter.

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

Common questions

What is the difference between GPT-5.4 Mini and Ling 2.6 Flash?

GPT-5.4 Mini is developed by OpenAI while Ling 2.6 Flash is developed by inclusionAI. GPT-5.4 Mini has a 400K 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.

Which is better, GPT-5.4 Mini or Ling 2.6 Flash?

It depends on your use case. GPT-5.4 Mini 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.

How much does GPT-5.4 Mini cost compared to Ling 2.6 Flash?

GPT-5.4 Mini costs $0.75/M input tokens and Ling 2.6 Flash costs $0/M input tokens. Ling 2.6 Flash is $0.75/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 GPT-5.4 Mini and Ling 2.6 Flash on Rival?

This page shows a side-by-side comparison of GPT-5.4 Mini 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.

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Model pages

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