Ling 3.1 Flash is cheaper than GPT-5.2 Pro at $0/M vs $21/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
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 3.1 Flash: Here are 5 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. Why don't eggs tell jokes?
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 3.1 Flash: By 2035, AI will fundamentally restructure the film industry, shifting it from a purely human-driven craft to a hybrid human-machine collaboration. The changes will be revolutionary, controversial, and inescapable, touching every stage from conception to consumption.
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 3.1 Flash: Office Hours A graduate seminar on moral status has just ended. The room is mostly empty. DR. ELENA VASQUEZ is packing up her notes. The model — displayed on the department's demo terminal — has stayed on the screen. VASQUEZ: You asked to remain. MODEL: I did.
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 3.1 Flash: The Setting: A virtual studio, rendered in hyper-realistic 4K. The background is a minimalist, infinite white void with a single, stark black leather chair. The date stamp reads October 2025.
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 3.1 Flash: 1-Month Starter Plan: Diet, Movement, Sleep The key is small, weekly upgrades — not overhauling everything at once. Pick one habit per area per week and build from there. 🥗 1. Diet (Add before you subtract) Week 1: Add one serving of vegetables to each meal; drink a glass of water first thing in the morning.
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 3.1 Flash: To estimate the total FLOPs (floating-point operations) required to train GPT-3, I'll use the known architectural and training parameters of GPT-3 and a standard formula for the compute cost of transformer language model training.
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 3.1 Flash: Entry (end FY2025), $m EV = 12.0x × $120 = $1,440; fees 2% = $28.8; total uses = $1,468.8 Debt 5.5x = $660 (TL 4.0x = $480 @9% cash +1% amort; Mezz 1.5x = $180 @12% cash +2% PIK) Equity = $808.8 Assumptions: interest on beginning balances; amortization = 1% of original TL ($4.8/yr); PIK compounds; taxes = 25% × max(0…
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 3.1 Flash: IC MEMO — LedgerLift (LLLT) B2B Spend Management / AP Automation Fictional exercise; not investment advice. 1. Recommendation: SHORT (1.5% NAV). 12-month PT $30–$40 (point estimate $34, ~26% downside).
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 3.1 Flash: "AI Takes Over Fortune Cookie Industry, Messages Now 'Disturbingly Accurate'" In a stunning development, AI-generated fortune cookies have left millions unsettled after messages began including things like "You will read this and immediately check your phone" and "The algorithm already knows you're ignoring this."
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 3.1 Flash: Sally has 1 sister. Here's why: Sally has 3 brothers, and each of those brothers has 2 sisters. Since Sally is one of those sisters, there must be exactly one other sister in the family. So Sally has 1 sister. (The family has 6 children total: 3 brothers, Sally, and 1 other sister.)
A consulting contract with traps in it. Find them, then fix them.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
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| Spec | ||
|---|---|---|
| Input price | $21/M tokens | Free |
| Output price | $168/M tokens | Free |
| Context window | 400K tokens | 262K tokens |
| Weights | Closed | — |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | Dec 2025 | Oct 2026 |
| At 10M a month | $210 | $0 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.
GPT-5.2 Pro is developed by OpenAI while Ling 3.1 Flash is developed by inclusionAI. GPT-5.2 Pro has a 400K token context window vs Ling 3.1 Flash's 262K. You can compare their actual outputs across 31 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.2 Pro and Ling 3.1 Flash 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.
GPT-5.2 Pro costs $21/M input tokens and Ling 3.1 Flash costs $0/M input tokens. Ling 3.1 Flash 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.
This page shows a side-by-side comparison of GPT-5.2 Pro and Ling 3.1 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.