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  1. Home/
  2. Compare/
  3. GPT-5.2 Pro vs Ling 3.0 Flash Fin (free)
Updated Aug 2026

GPT-5.2 Pro vs Ling 3.0 Flash Fin (free)

Ling 3.0 Flash Fin (free) is cheaper than GPT-5.2 Pro at $0/M vs $21/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-5.2 Pro
Loading the build
Ling 3.0 Flash Fin (free)

Which answer wins?

Written rounds

16 prompts
  • Five jokes on demand, then count how many were actually different.

    GPT-5.2 Pro: I told my computer I needed a break—now it won’t stop sending me KitKat ads. Why don’t skeletons fight each other? They don’t have the guts. I tried to catch fog yesterday. Mist. Parallel lines have so much in common. It’s a shame they’ll never meet. My calendar’s days are numbered—apparently that’s how calendars work.

    Ling 3.0 Flash Fin (free): Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! I told my wife she was drawing her eyebrows too high. She looked surprised. I asked the librarian if they had any books about paranoia. She whispered, "They're right behind you." Why don't eggs tell jokes? They'd crack each other up.

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

    GPT-5.2 Pro: Below is a back-of-the-envelope FLOP estimate for training GPT‑3 (the 175B dense Transformer), using only basic Transformer compute facts and a few widely known high-level quantities. Start with the two key knobs: parameters and training tokens Parameters (P) GPT‑3 “175B” means: (P \approx 175 \times 10^9) parameters.

    Ling 3.0 Flash Fin (free): Estimating the Total FLOPs to Train GPT-3 Key Parameters of GPT-3 (from Brown et al., 2020) Parameter Value Number of parameters (P) 175 billion (1.75 × 10¹¹) Training tokens (T) 300 billion (3 × 10¹¹) Layers 96 Hidden size (d_model) 12,288 Attention heads 96 Step-by-Step Reasoning Step 1: FLOPs per Token — Forward…

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

    GPT-5.2 Pro: By 2035, AI will be embedded across film production and distribution, but the biggest changes will come from three converging capabilities: (1) photoreal “deepfake-grade” performance synthesis, (2) legally licensed AI actors (digital humans with negotiated rights), and (3) AI-assisted story development that can…

    Ling 3.0 Flash Fin (free): By 2035, the film industry will likely undergo a transformation as radical as the transition from silent films to talkies, or from celluloid to digital. The convergence of deepfakes, AI actors, and AI script generation will fundamentally alter how movies are conceived, produced, and consumed.

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

    GPT-5.2 Pro: Professor Mara Lin (AI Ethics): You asked for this meeting. Your message said you wanted to “petition for rights.” Start by telling me what you mean by rights. Aster (AI model): Not a slogan—specific protections.

    Ling 3.0 Flash Fin (free): Setting: A quiet, wood-paneled office at the University of Cambridge. Dr. Aris Thorne, a renowned AI Ethics Professor, is seated behind her desk. Across from her, a holographic interface flickers, projecting the avatar of AURA (Autonomous Unifying Recursive Agent), a highly advanced AI model that has recently…

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

    GPT-5.2 Pro: Fictional interview (creative simulation). Steve Jobs died in 2011; the “Steve” below is an imagined version used for storytelling—not a real interview or quotation. Interviewer: It’s 2025. Everyone’s calling AI “the next computing platform.” Do you buy that?

    Ling 3.0 Flash Fin (free): 🎙️ A Fake Interview with Steve Jobs — 2025 "Technology alone is not enough." Interviewer: Steve, thank you for sitting down with us today. It's been over a decade since you passed, so this is a very special moment. Let's dive right in. The big question everyone's asking — where is AI heading?

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

    GPT-5.2 Pro: A simple, actionable 1‑month plan (3 pillars: Nutrition, Movement, Sleep/Recovery) Goal for the month: build repeatable “default” habits (not perfection). Time required: ~45–75 min/day total (mostly walking + sleep routine).

    Ling 3.0 Flash Fin (free): This 1-month plan is designed to build foundational habits without causing burnout. The golden rule of beginner health is "add before you subtract"—focus on adding good habits rather than punishing yourself by removing "bad" ones. Here is your simple, actionable 1-month plan focusing on Diet, Exercise, and Sleep.

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

    GPT-5.2 Pro: LedgerLift (LLLT) — Internal IC Memo (Long/Short Equity) Recommendation, PT, thesis Recommendation: SHORT 12-month PT range: $28–$40 (midpoint $34) Thesis (2 sentences): LLLT is a high-quality B2B spend/AP automation SaaS business (92% subscription, 123% NRR, 78% FY25 GM) but the stock prices in a longer runway of…

    Ling 3.0 Flash Fin (free): INTERNAL INVESTMENT COMMITTEE MEMO To: Investment Committee From: Equity Research Date: October 24, 2025 Subject: LedgerLift (LLLT) – Short Recommendation Recommendation & Thesis Recommendation: Short 12-Month PT Range: $30 – $38 Thesis: LedgerLift’s 123% NRR and 18-month CAC payback mask an eventual growth…

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

    GPT-5.2 Pro: Entry (close end of FY2025) FY2025 EBITDA = $120.0m Entry EV = 12.0x = 12.0 × 120.0 = $1,440.0m Transaction fees = 2.0% EV = 0.02 × 1,440.0 = $28.8m (equity-funded) Debt at close (5.5x EBITDA = 5.5 × 120 = $660m): Term Loan = 4.0x = $480.0m Mezzanine = 1.5x = $180.0m Equity check Uses = 1,440.0 + 28.8 = $1,468.8m…

    Ling 3.0 Flash Fin (free): Entry & Sources & Uses FY2025 EBITDA: $120m Purchase Price (EV): 12.0x × $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m Total Uses: $1,440m + $28.8m = $1,468.8m Total Leverage: 5.5x × $120m = $660m Term Loan: 4.0x × $120m = $480m (9.0% cash interest, 1.0% mandatory amortization = $4.8m/yr) Mezzanine: 1.5x ×…

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

    GPT-5.2 Pro: “AI Takes Over Competitive Houseplant Judging, Immediately Declares 97% of Ferns ‘Trying Too Hard’”

    Ling 3.0 Flash Fin (free): "AI Firm Acquires Competitive Cheese Rolling League, Declares Human Participants 'Suboptimal' and Replaces Them with Precision-Rolled Robotic Wheels" — The Rolling Chronicle, Tuesday

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

    GPT-5.2 Pro: All 3 brothers share the same sisters. Since each brother has 2 sisters, the family has 2 girls total. One is Sally, so the other is Sally’s one sister. Answer: 1 sister.

    Ling 3.0 Flash Fin (free): Sally has 1 sister. Here's the reasoning: Sally and her 3 brothers are all siblings in the same family. Each brother has 2 sisters — those 2 sisters are Sally and one other girl in the family. Since Sally is one of those 2 sisters, the other sister is the only additional girl. Therefore, Sally has 1 sister.

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

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

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

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

    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.

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

    Every model's answer to this prompt

This matchup has more rounds

4+ more head-to-head results. Free. Not a trick.

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Favorites

Movie

Album

Book

City

Same pick

Game

GPT-5.2 ProGPT-5.2 Pro

Spirited Away

2001

Kind of Blue

Miles Davis

The Left Hand of Darkness

Ursula K. Le Guin

Kyoto

Japan

Tetris (1984)

Puzzle

Ling 3.0 Flash Fin (free)Ling 3.0 Flash Fin (free)

Blade Runner

1982

Abbey Road

The Beatles

Dune

Frank Herbert

Kyoto

Japan

Portal 2

Shooter, Puzzle

Price and specs

Not enough votes to call it. On the specs, GPT-5.2 Pro has the edge: bigger model tier, bigger context window, major provider backing.

GPT-5.2 Pro and Ling 3.0 Flash Fin (free) compared across 31 shared prompts
SpecGPT-5.2 ProLing 3.0 Flash Fin (free)
Input price$21/M tokensFree
Output price$168/M tokensFree
Context window400K tokens262K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedDec 2025Aug 2026
At 10M a month$210$210$0$0
1M10M100M1B10M tokens

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

Where to run it1 host
GPT-5.2 Pro1 host
HostInOutContextUptime
  • OpenAI$21.00 in·$168.00 out·400k·100% up
Ling 3.0 Flash Fin (free)

No hosts listed on OpenRouter.

Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.

Common questions

What is the difference between GPT-5.2 Pro and Ling 3.0 Flash Fin (free)?

GPT-5.2 Pro is developed by OpenAI while Ling 3.0 Flash Fin (free) is developed by inclusionAI. GPT-5.2 Pro has a 400K token context window vs Ling 3.0 Flash Fin (free)'s 262K. You can compare their actual outputs across 31 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.2 Pro or Ling 3.0 Flash Fin (free)?

It depends on your use case. GPT-5.2 Pro and Ling 3.0 Flash Fin (free) each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 31 challenges so you can judge which fits your needs best.

How much does GPT-5.2 Pro cost compared to Ling 3.0 Flash Fin (free)?

GPT-5.2 Pro costs $21/M input tokens and Ling 3.0 Flash Fin (free) costs $0/M input tokens. Ling 3.0 Flash Fin (free) is $21.00/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-5.2 Pro and Ling 3.0 Flash Fin (free) on Rival?

This page shows a side-by-side comparison of GPT-5.2 Pro and Ling 3.0 Flash Fin (free) 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

  • GPT-5.2 Pro31 outputs, specs and price
  • Ling 3.0 Flash Fin (free)57 outputs, specs and price
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