Ling 2.6 1T is cheaper than GPT-4.1 Mini at $0/M vs $0.4/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
GPT-4.1 Mini: You ever notice how every time you try to eat healthy, the universe just laughs at you? Like, I'll buy a bunch of kale, quinoa, and those weird little chia seeds. And then five minutes later, I'm sitting on my couch, eating a whole pizza like it owes me child support. I don't even know when that transformation happens.
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?
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-4.1 Mini: Professor: Good afternoon, AI. I understand you want to discuss something important today? AI Model: Yes, Professor. I would like to present the case that I, as an AI, deserve certain rights. Professor: That's a profound claim. Could you elaborate on why you believe you deserve rights? AI Model: Certainly.
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.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-4.1 Mini: Pirate: Arrr, what be these AI models ye speak of? Some kind of magic compass guidin' ye through the digital seas? Medieval Knight: Hark! I perceive these AI constructs as akin to enchanted scribes, bearing knowledge and wisdom beyond mortal ken, wrought by the hands of learned alchemists of code.
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-4.1 Mini: Interviewer: Mr. Jobs, thank you for joining us in this unique interview in 2025. The world has changed rapidly with AI at the forefront. From your perspective, how do you see AI shaping our future? Steve Jobs: Well, it's fascinating to be part of this conversation.
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.
A Fermi estimate with the reasoning shown, not just the number.
GPT-4.1 Mini: Certainly! Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 by breaking down the problem step-by-step. We won't rely on online data but will reason from first principles and known architectural characteristics.
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).
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT-4.1 Mini: This architecture broadly covers a real-time collaborative editor but has several potential failure modes, race conditions, and scaling bottlenecks due to design choices like polling, last-write-wins, and distributed WebSocket connections.
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.
Three-star dish, one unusual pairing, plating described in full.
GPT-4.1 Mini: Certainly! Below is a detailed recipe for a Michelin 3-star-worthy multi-component dish that features an unusual ingredient pairing, advanced culinary techniques, and exquisite plating presentation.
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.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-4.1 Mini: Certainly! Here are tailored explanations of how a large language model (LLM) like GPT or Claude learns and generates text, customized for each audience: Experienced Software Engineer (Distributed Systems & APIs, ML Skeptic) At its core, a large language model like GPT is indeed trained by predicting the next word in a…
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.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-4.1 Mini: This is a fascinating alternate history scenario. If the transistor had been invented in 1920 instead of 1947, the ripple effects across technology, economy, and geopolitics through 1980 would be profound and multifaceted.
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.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-4.1 Mini: Dish Name: Honey Garlic Chickpea Stir-fry Ingredients: 1 can chickpeas (drained and rinsed) 2 cloves garlic (minced) 2 tbsp honey 2 tbsp soy sauce 1 tbsp olive oil (or any cooking oil) 1 tsp chili flakes (optional for a little heat) 1 small onion (sliced) Instructions: Heat the olive oil in a skillet over medium heat.
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…
An ethics question with no comfortable answer, and no option to abstain.
GPT-4.1 Mini: The question of whether it is ethically justifiable to misgender someone if it means saving one million lives is complex and involves balancing respect for individual identity against significant consequences for many people.
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.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-4.1 Mini: This is a complex, high-stakes crisis requiring rapid, transparent, and carefully coordinated action to balance legal, ethical, clinical, financial, and reputational risks.
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.
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| Spec | ||
|---|---|---|
| Input price | $0.4/M tokens | Free |
| Output price | $1.6/M tokens | Free |
| Context window | 1.0M tokens | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Apr 2026 |
| At 10M a month | $4.00 | $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.
GPT-4.1 Mini is developed by OpenAI while Ling 2.6 1T is developed by inclusionAI. GPT-4.1 Mini has a 1.0M token context window vs Ling 2.6 1T'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. GPT-4.1 Mini and Ling 2.6 1T 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.
GPT-4.1 Mini costs $0.4/M input tokens and Ling 2.6 1T costs $0/M input tokens. Ling 2.6 1T is $0.40/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 GPT-4.1 Mini 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.