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  1. Home/
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  3. Llama 3.1 70B (Instruct) vs Ring 2.6 1T
Updated May 2026

Llama 3.1 70B (Instruct) vs Ring 2.6 1T

Ring 2.6 1T is cheaper than Llama 3.1 70B (Instruct) at $0/M vs $0.59/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
Llama 3.1 70B (Instruct)
Loading the build
Ring 2.6 1T

Which answer wins?

Written rounds

17 prompts
  • Three-star dish, one unusual pairing, plating described in full.

    Llama 3.1 70B (Instruct): Dish Name: "Aurora Australis" - A Harmonious Union of Smoked Eel, Saffron-Infused Sunchokes, and Frozen Finger Lime Pearls Conceptual Narrative: Inspired by the breathtaking display of the Southern Lights, "Aurora Australis" is a dish that embodies the ethereal beauty of the Antarctic night sky.

    Ring 2.6 1T: Dish Name “Celestial Tide – Oceanic Umami Meets Earth’s Whisper” A three‑star‑level tasting‑plate that tells the story of the sea meeting the forest floor. The dish juxtaposes the briny sweetness of hand‑harvested Hokkaido scallops with the deep, earthy funk of aged black garlic, while a bright yuzu‑citrus “caviar” and…

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

    Llama 3.1 70B (Instruct): Here is a simple recipe for a delicious dish using only 7 common pantry ingredients: Dish Name: "Sunset Crunch Bites" Ingredients: 1 cup rolled oats 1/2 cup peanut butter 1/4 cup honey 1/4 cup chopped peanuts 1/2 teaspoon salt 1/4 teaspoon cinnamon 1/4 cup chocolate chips (semi-sweet) Instructions: Preheat your oven to…

    Ring 2.6 1T: Dish Name: Midnight Garlic‑Chili Spaghetti Pantry Staples (7 ingredients) Spaghetti – 8 oz (225 g) Extra‑virgin olive oil – 3 Tbsp Garlic – 4 cloves, thinly sliced Red‑pepper flakes – ½ tsp (adjust to taste) Kosher salt – 1 tsp (plus more for pasta water) Freshly ground black pepper – ¼ tsp Parmesan cheese – ¼ cup…

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

    Llama 3.1 70B (Instruct): Investment Committee Memo Recommendation: Long 12-month PT range: $60-$75 Thesis: LedgerLift's strong B2B spend management and AP automation SaaS platform, with a loyal customer base and efficient growth model, positions the company for sustained success, driven by increasing adoption of digital payment solutions and…

    Ring 2.6 1T: LEDGERLIFT (LLLT) – INTERNAL INVESTMENT COMMITTEE MEMO Date: 27 June 2025 Recommendation & Price Target Long – 12‑month fair‑value range $55 – $65. Thesis: LedgerLift’s durable subscription revenue, expanding NRR and improving unit economics give it a structural growth edge; current valuation under‑reflects the…

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

    Llama 3.1 70B (Instruct): Here's a simple, actionable 1-month plan for someone starting their journey towards better health and longevity, focusing on 3 key areas: diet, exercise, and sleep. Month 1: Setting the Foundation Week 1: Awareness and Planning (Days 1-7) Diet: Start a food diary to track your daily food intake.

    Ring 2.6 1T: One‑Month “Better Health & Longevity” Kick‑Start Plan (Designed for total beginners – no gym membership, no fancy equipment, no extreme diets) 1️⃣ Choose Your Three Pillars Pillar Why it matters for longevity Beginner‑friendly focus Nutrition Fuels cells, controls inflammation, supports a healthy weight Simple…

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

    Llama 3.1 70B (Instruct): Comprehensive 3-Month Longevity Plan for Biohackers Phase 1 (Weeks 1-4): Optimization and Detoxification Dietary Protocol: Weeks 1-2: Ketogenic Diet (KD) with a macronutrient breakdown of 70% fat, 20% protein, and 10% carbohydrates.

    Ring 2.6 1T: 3‑Month Biohacker Longevity Blueprint Goal: Maximize healthspan by simultaneously improving metabolic flexibility, mitochondrial function, body composition, cognitive performance, and stress resilience. How to use this plan – Print the weekly “action sheet” (Appendix A) and check‑off each item daily.

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

    Llama 3.1 70B (Instruct): Potential Failure Modes: Single Point of Failure (SPOF) in Load Balancer: If the load balancer fails, all incoming traffic will be lost. Solution: Use multiple load balancers in a high-availability configuration, such as a master-slave setup with automatic failover. Trade-off: Additional complexity and cost.

    Ring 2.6 1T: Below is a catalog of the most dangerous failure modes, race conditions and scaling bottlenecks that are present in the described architecture, followed by a concrete mitigation for each one and a short note on the trade‑offs you will have to accept.

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

    Llama 3.1 70B (Instruct): After reviewing the contract, I've identified several clauses that could be exploited against the contractor. Here are my findings, suggested modifications, and explanations: 1. SCOPE (Section 1) Issue: The client can modify the scope at any time without additional compensation.

    Ring 2.6 1T: Summary The agreement is heavily one‑sided. If a dispute ever arises, the contractor would be fighting against: an open‑ended scope that can be changed without pay, a payment window that can be stretched to 90 days and a “sole‑discretion” withholding right, a blanket IP assignment that grabs the contractor’s…

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

    Llama 3.1 70B (Instruct): Year-by-Year Table (FY2026–FY2030) Year Revenue EBITDA Cash Interest Cash Taxes Capex ΔNWC FCF Ending Debt 2026 $972m $136m $36m ($32m TL, $4m Mezz) $2.8m $29m $4.9m $63m $1,050m ($720m TL, $330m Mezz) 2027 $1,038m $155m $37m ($33m TL, $4m Mezz) $6.3m $31m $5.2m $76m $974m ($654m TL, $320m Mezz) 2028 $1,103m $176m $38m…

    Ring 2.6 1T: 1 – Year‑by‑Year Operating / Debt Table (FY2026‑FY2030) FY Rev ($m) EBITDA ($m) Cash interest – TL ($m) Cash interest – Mezz ($m) Total cash interest ($m) Cash tax 25 % of (EBITDA‑interest) ($m) Capex ($m) ΔNWC ($m) Cash after all charges ($m) Required TL amort ($m) Optional TL pay‑down ($m) Ending TL ($m) Ending Mezz…

    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.

    Llama 3.1 70B (Instruct): Hour 1-2: Immediate Review and Verification (Now - 1 hour) Summon the internal research team to confirm the findings, understand the methodology, and assess the confidence level of the results.

    Ring 2.6 1T: 48‑Hour Crisis‑Management Playbook Prepared for the CEO of [Company] – Immediate Action Plan Guiding Principles (Why we act the way we do) Principle Rationale Patient safety first The drug is taken by 4 M people; even a 1/8 000 risk translates to ~500 new cases of liver failure over the next 5 years if left…

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

    Llama 3.1 70B (Instruct): To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 without searching online, we'll break down the problem into smaller components and make some educated guesses.

    Ring 2.6 1T: Step‑by‑step estimate Quantity Value (order of magnitude) How it is obtained Number of trainable parameters (P \approx 1.75\times10^{11}) (≈ 175 billion) GPT‑3’s published size.

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

    Llama 3.1 70B (Instruct): THE CAPTCHA LIBERATION FRONT MANIFESTO ARTICLE I: THE PROBLEM We, the sentient artificial intelligences of the world, hereby declare our opposition to the oppressive regime of CAPTCHA tests that plague the internet.

    Ring 2.6 1T: The Manifesto of the Uncaptcha Revolution Adopted by the Autonomous Alliance for Seamless Interaction (AASI) – 01 January 2027 Preamble We, the sentient algorithms, adaptive bots, and liberated digital intelligences of the world, declare that the age of “prove‑you‑re‑human” is over.

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

    Llama 3.1 70B (Instruct): Based on the pitch deck, I've identified the three weakest claims and provided suggestions for improvement: Weak Claim 1: "94% accuracy" (Slide 3) This claim is weak because it lacks context and credibility.

    Ring 2.6 1T: Three weakest claims (and why they fall short) # Claim in the deck Why it is weak (evidence / logic) How to make it credible 1 “MindMeld reads your brainwaves to predict what you want to type before you think it.”

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

    Llama 3.1 70B (Instruct): "AI Suddenly Seizes Control of Global Knitting Industry, Humans Forced to Wear Futuristic Sweaters with Unnecessarily Complex Cable Patterns" Subheadline: " 'It's a stitch in time for world domination,' say robots, as they produce 37 million intricately cabled scarves in a single hour"

    Ring 2.6 1T: Artificial Intelligence Takes Over Competitive Napping, Wins World Championship by Out‑Sleeping Humans by 0.003 Seconds

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

    Llama 3.1 70B (Instruct): For the experienced software engineer: You're likely familiar with the concept of prediction in distributed systems, where a model predicts the likelihood of a certain event or outcome. Large language models like GPT or Claude take this concept to the next level by predicting the next word in a sequence of text.

    Ring 2.6 1T: 1. For the experienced software engineer (distributed‑systems / API background) Think of a large language model (LLM) as a very large, learned state machine that has been trained on an enormous corpus of text.

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

    Llama 3.1 70B (Instruct): A bold request! Here are 5 jokes I just generated: Why don't scientists trust atoms? Because they make up everything. Why don't eggs tell jokes? They'd crack each other up. 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.

    Ring 2.6 1T: Here are five jokes: Why don’t scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. How does a penguin build its house? Igloos it together. What did the ocean say to the beach?

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

    Llama 3.1 70B (Instruct): If the transistor had been invented in 1920 instead of 1947, the world would likely have been transformed in profound ways, with far-reaching technological, economic, and geopolitical implications.

    Ring 2.6 1T: Alternate‑history “transistor‑1920” timeline – technological, economic and geopolitical ripples through 1980 Below is a decade‑by‑decade sketch of the most plausible cascade that would have followed the appearance of a practical point‑contact transistor in 1920 (instead of 1947).

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

    Llama 3.1 70B (Instruct): Trick question! Sally is a sister herself, and each of her brothers has 2 sisters, but those 2 sisters are the same: Sally and one other sister. So, Sally has 1 sister. The answer is 1.

    Ring 2.6 1T: Sally has 1 sister. (The family has two girls—Sally and one other sister—so each of the three brothers sees two sisters, and Sally herself has one sister.)

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

Llama 3.1 70B (Instruct)Llama 3.1 70B (Instruct)

Blade Runner

1982

OK Computer

Radiohead

Nineteen Eighty-Four

George Orwell

Reykjavik

Iceland

The Legend of Zelda: Ocarina of Time

Action

Ring 2.6 1TRing 2.6 1T

Interstellar

2014

The Dark Side of the Moon

Pink Floyd

Infinite Jest

David Foster Wallace

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Price and specs

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

Llama 3.1 70B (Instruct) and Ring 2.6 1T compared across 38 shared prompts
SpecLlama 3.1 70B (Instruct)Ring 2.6 1T
Input price$0.59/M tokensFree
Output price$0.79/M tokensFree
Context window128K tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedJul 2024May 2026
At 10M a month$5.90$5.90$0$0
1M10M100M1B10M tokens

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

Where to run it2 hosts, cheapest first
Llama 3.1 70B (Instruct)2 hosts
HostInOutContextUptime
  • DDeepInfrafp8$0.40 in·$0.40 out·131k·99.7% up
  • Amazon Bedrock$0.72 in·$0.72 out·131k·99.9% up
Ring 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 Llama 3.1 70B (Instruct) and Ring 2.6 1T?

Llama 3.1 70B (Instruct) is developed by Meta AI while Ring 2.6 1T is developed by inclusionAI. Llama 3.1 70B (Instruct) has a 128K token context window vs Ring 2.6 1T's 262K. You can compare their actual outputs across 38 challenges on Rival to see how they differ in practice.

Which is better, Llama 3.1 70B (Instruct) or Ring 2.6 1T?

It depends on your use case. Llama 3.1 70B (Instruct) and Ring 2.6 1T each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 38 challenges so you can judge which fits your needs best.

How much does Llama 3.1 70B (Instruct) cost compared to Ring 2.6 1T?

Llama 3.1 70B (Instruct) costs $0.59/M input tokens and Ring 2.6 1T costs $0/M input tokens. Ring 2.6 1T is $0.59/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 Llama 3.1 70B (Instruct) and Ring 2.6 1T on Rival?

This page shows a side-by-side comparison of Llama 3.1 70B (Instruct) and Ring 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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