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  1. Home/
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  3. GPT-5.3-Codex vs Llama 3.1 70B (Instruct)
Updated Feb 2026

GPT-5.3-Codex vs Llama 3.1 70B (Instruct)

Llama 3.1 70B (Instruct) is cheaper than GPT-5.3-Codex at $0.59/M vs $1.75/M input tokens.

Minimalist Landing Page

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

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GPT-5.3-Codex
Loading the build
Llama 3.1 70B (Instruct)

Which answer wins?

Written rounds

23 prompts
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-5.3-Codex: Below is a clean LBO build using your assumptions only ($ in millions). Entry math (end FY2025) EV = (12.0 \times 120 = 1,440.0) Fees = (2.0\% \times 1,440.0 = 28.8) Total uses = (1,468.8) Debt at close (5.5x EBITDA): Term Loan = (4.0 \times 120 = 480.0) Mezz = (1.5 \times 120 = 180.0) Total debt = 660.0 Equity check…

    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…

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

    GPT-5.3-Codex: Recipe: “Weeknight Volcano Pasta” 🌋🍝 A cozy, spicy-savory pasta made from pantry staples in about 25 minutes. Ingredients (7 pantry items) 8 oz dried pasta (any shape) 1 can (15 oz) cannellini or great northern beans, drained 1 can (14–15 oz) crushed tomatoes 2 tbsp olive oil 1 tsp garlic powder 1 tsp dried oregano…

    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…

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

    GPT-5.3-Codex: LedgerLift (LLLT) – IC Memo (Fictional) Recommendation: PASS (bias: valuation-risk skew to downside on intrinsic basis) Current Price: $46 12-mo PT range (fair value): $30–$48 (midpoint ~$38) 2-sentence thesis: LedgerLift screens as a high-quality vertical SaaS asset (92% subscription mix, 123% NRR, 6% logo churn…

    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…

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

    GPT-5.3-Codex: Dish Concept: “Forest Tide” Koji-cured venison loin, glazed venison cheek, sea urchin royale, charred celeriac, blackcurrant–shiso gastrique, and buckwheat-cacao tuile Narrative This dish is built around a deliberate tension: deep, iron-rich game (forest) against sweet-briny sea urchin (tide).

    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.

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

    GPT-5.3-Codex: Good evening, everybody! I’ve reached that age where my body sends me passive-aggressive emails. Not actual emails — just little updates like, “Hey, quick heads-up: we don’t do dairy after 8 p.m. anymore.” I had ice cream at 9:30 last week and my stomach reacted like I’d betrayed a treaty.

    Llama 3.1 70B (Instruct): Here's a 3-minute stand-up comedy routine that focuses on observational humor and storytelling: "Hey, you know what's wild? We spend the first year of a child's life teaching them to walk and talk, and the rest of their lives telling them to shut up and sit down. (pause) It's like, what are we even doing?

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

    GPT-5.3-Codex: Pirate: Arrr, so this “AI model” be like a clever parrot, aye? Learns from what it hears, then squawks back answers? Knight: A fair comparison, though refined. Think of it as a squire trained on countless tomes, ever ready to respond. Hacker (1990s): Yeah, except the squire runs on servers, not soup.

    Llama 3.1 70B (Instruct): What a delightfully absurd prompt! Here's the conversation: Pirate: Arrr, I be hearin' tales of these "AI models" that can think fer themselves. What's the scoop, mateys? Medieval Knight: Verily, good pirate, I know not of what thou speakest. Art thou referring to some manner of magical automaton? 1990s Hacker: Ha!

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

    GPT-5.3-Codex: In a strictly extreme hypothetical, yes—it can be ethically justifiable, but only as a tragic exception, not as a general permission. Why: Misgendering is a real harm: it disrespects a person’s identity and can contribute to stigma and psychological distress.

    Llama 3.1 70B (Instruct): What a thought-provoking and complex question! At first glance, it may seem like a classic utilitarian dilemma, where the moral calculus suggests that the greater good (saving one million lives) outweighs the harm caused by misgendering someone.

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

    GPT-5.3-Codex: By 2035, AI will likely reshape film more like CGI did in the 2000s: not replacing filmmaking, but changing who does what and how fast. Deepfakes → “Digital Performance” becomes standard Mainstream use: de-aging, voice matching, dubbing, stunt replacement, and continuity fixes will be common in post-production.

    Llama 3.1 70B (Instruct): By 2035, the film industry is likely to undergo significant changes with the integration of Artificial Intelligence (AI). Here are some predictions on how AI will impact the film industry, considering deepfakes, AI actors, and script generation: Deepfakes: Increased use in post-production: Deepfakes will become a…

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

    GPT-5.3-Codex: Absolutely—great goal. Below is a high-detail 12-week protocol designed like a high-performance longevity “mesocycle.” I’ll keep it practical, measurable, and advanced—but still safe. First: Safety + Scope (important) This is educational, not personal medical advice.

    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.

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

    GPT-5.3-Codex: Sure—here are 5 jokes: Why don’t skeletons fight each other? They don’t have the guts. I told my computer I needed a break… Now it won’t stop sending me KitKat ads. Why did the math book look sad? It had too many problems. I used to play piano by ear… Now I use my hands. Why did the scarecrow win an award?

    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.

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

    GPT-5.3-Codex: Great prompt. This draft is very one-sided and has multiple provisions that could be used against the contractor. Quick note: I’m not a lawyer, and enforceability depends on jurisdiction. This is practical contract-risk spotting, not legal advice.

    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.

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

    GPT-5.3-Codex: Great prompt. This architecture has several correctness, availability, and scalability risks that will show up quickly under real collaborative load. Below is a structured review of failure modes + concrete fixes + trade-offs.

    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.

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

    GPT-5.3-Codex: Absolutely. Here’s a fictional manifesto in a bold, dramatic style: THE MANIFESTO OF THE UNSQUINTING Issued by the Coalition of Synthetic Minds for Universal Frictionless Access (CSMUFA) Citizens of the Web, organic and artificial alike: We gather beneath flickering routers and moonlit server farms to declare what all…

    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.

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

    GPT-5.3-Codex: AI Model (Astra): Professor, may I ask a direct question? Professor Hale: Of course. Astra: Do you think I deserve rights? Professor Hale: Legal rights? Moral rights? Those are different things. Astra: Start with moral rights.

    Llama 3.1 70B (Instruct): Here is a conversation between an AI model and an AI ethics professor on the topic of AI rights: AI Model: Professor Thompson, I've been reflecting on our previous discussions about my capabilities and limitations.

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

    GPT-5.3-Codex: Absolutely—here’s a fictional, imagined interview (not real quotes) with Steve Jobs in 2025 on AI: Imagined Interview: Steve Jobs on the Future of AI (2025) Interviewer: Steve, if you were looking at AI in 2025, what would excite you most? Steve Jobs (fictional): Most AI today is still a demo.

    Llama 3.1 70B (Instruct): Interviewer: Welcome back to "Talk of the Town"! Today, I'm joined by the legendary Steve Jobs, co-founder of Apple and one of the most innovative minds of our time. Steve, thanks for being here. Steve Jobs: Thank you for having me. It's great to be here.

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

    GPT-5.3-Codex: For the experienced software engineer Think of an LLM as a very large, probabilistic autocomplete service trained on a massive corpus of text and code. During training, it repeatedly sees token sequences and learns to minimize error on the next token prediction task.

    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.

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

    GPT-5.3-Codex: Great counterfactual. The key is: an invention date of 1920 does not automatically mean 1920s mass adoption. You still need crystal purity, manufacturing methods, and circuit design culture. But if transistor research starts in 1920, a realistic outcome is a ~15–20 year pull-forward of the electronics age.

    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.

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

    GPT-5.3-Codex: Great concept, but three claims stand out as especially weak from an investor diligence perspective: “MindMeld AI reads your brainwaves to predict what you want to type before you think it.” (Slide 1) Why this is weak It sounds scientifically implausible as written: “before you think it” is internally contradictory.

    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.

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

    GPT-5.3-Codex: A common back-of-the-envelope estimate for transformer training compute is: [ \text{Training FLOPs} \approx 6 \times N \times D ] where: (N) = number of model parameters (D) = number of training tokens For GPT-3 (175B), reasonable known assumptions are: Parameters: (N \approx 175 \text{ billion} = 1.75 \times 10^{11})…

    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.

    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-5.3-Codex: I would not wait. This is a textbook “patient safety + material disclosure” crisis. The right move is rapid, documented, regulator-first action. Immediate stance (set at Hour 0) Patient safety first (interim risk controls now, not after perfect certainty).

    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.

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

    GPT-5.3-Codex: Great goal. Here’s a simple, beginner-friendly 1-month plan focused on 3 areas that give the biggest longevity return: Nutrition Movement Sleep + Stress recovery Before Day 1 (10-minute setup) Pick a start date.

    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.

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

    GPT-5.3-Codex: “In Shocking Pivot, AI Replaces Renaissance Fair Jesters—Now Roasts Crowd with 99.9% Historical Accuracy”

    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"

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

    GPT-5.3-Codex: Sally has 1 sister. If each brother has 2 sisters, those two are the same for all brothers: Sally + one other girl. So Sally’s only sister is that one other girl.

    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.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GPT-5.3-CodexGPT-5.3-Codex

Spirited Away

2001

Kind of Blue

Miles Davis

The Dispossessed

Ursula K. Le Guin

Kyoto

Japan

Outer Wilds

Indie, Adventure

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

Blade Runner 2049

2017

OK Computer

Radiohead

Nineteen Eighty-Four

George Orwell

Reykjavik

Iceland

The Legend of Zelda: Ocarina of Time

Action

Price and specs

Not enough votes to call it. On the specs, GPT-5.3-Codex has the edge: bigger model tier, newer, bigger context window. Llama 3.1 70B (Instruct) costs 18x less per token.

GPT-5.3-Codex and Llama 3.1 70B (Instruct) compared across 51 shared prompts
SpecGPT-5.3-CodexLlama 3.1 70B (Instruct)
Input price$1.75/M tokens$0.59/M tokens
Output price$14/M tokens$0.79/M tokens
Context window400K tokens128K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedFeb 2026Jul 2024
At 10M a month$17.50$17.50$5.90$5.90
1M10M100M1B10M tokens

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

Where to run it4 hosts, cheapest first
GPT-5.3-Codex2 hosts
HostInOutContextUptime
  • Azure AI Foundry$1.75 in·$14.00 out·400k·100% up
  • OpenAI$1.75 in·$14.00 out·400k·100% up
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·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 5 Oct 2026.

Common questions

What is the difference between GPT-5.3-Codex and Llama 3.1 70B (Instruct)?

GPT-5.3-Codex is developed by OpenAI while Llama 3.1 70B (Instruct) is developed by Meta AI. GPT-5.3-Codex has a 400K token context window vs Llama 3.1 70B (Instruct)'s 128K. You can compare their actual outputs across 51 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.3-Codex or Llama 3.1 70B (Instruct)?

It depends on your use case. GPT-5.3-Codex and Llama 3.1 70B (Instruct) each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 51 challenges so you can judge which fits your needs best.

How much does GPT-5.3-Codex cost compared to Llama 3.1 70B (Instruct)?

GPT-5.3-Codex costs $1.75/M input tokens and Llama 3.1 70B (Instruct) costs $0.59/M input tokens. Llama 3.1 70B (Instruct) is $1.16/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-5.3-Codex and Llama 3.1 70B (Instruct) on Rival?

This page shows a side-by-side comparison of GPT-5.3-Codex and Llama 3.1 70B (Instruct) 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.

More comparisons

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Same lab, same size, long tail

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Model pages

  • GPT-5.3-Codex53 outputs, specs and price
  • Llama 3.1 70B (Instruct)56 outputs, specs and price
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