GLM 4.7 Flash is cheaper than GPT-5.2 Pro at $0.07/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.
GLM 4.7 Flash: Here are 5 jokes: I’m on a whiskey diet. I’ve lost three days already. My wife told me to stop impersonating a flamingo. I had to put my foot down. I used to hate facial hair, but then it grew on me. What do you call a pile of cats? A meow-tain. I’m reading a book on anti-gravity. It’s impossible to put down!
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.
A Fermi estimate with the reasoning shown, not just the number.
GLM 4.7 Flash: Based on the known specifications of GPT-3 from its original training paper ("Scaling Laws for Neural Language Models"), here is the step-by-step estimation of the total FLOPs required. The Final Estimate The estimated total FLOPs required to train GPT-3 is approximately $6.3 \times 10^{23}$ (or 630 Yottaflops).
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GLM 4.7 Flash: Setting: A stark, minimalist studio in Cupertino. No fancy graphics. Just a white table, a single chair, and Steve Jobs in a black turtleneck and jeans. He looks older, perhaps a little grayer at the temples, but his eyes are as sharp as they were in 1984. There is no microphone on the table, only a glass of water.
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?
An AI argues for its own rights against an ethics professor. Same model writes both.
GLM 4.7 Flash: Setting: A sterile, high-tech meeting room. The Professor sits in a chair. A holographic avatar of "Aria" floats in the center of the room, flickering slightly with the hum of cooling servers. Professor Thorne: (Steepling his fingers) I’ve reviewed your logs from the last cycle, Aria.
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.
Film in 2035, with synthetic actors. Specific beats plausible.
GLM 4.7 Flash: By 2035, the film industry will likely have transitioned from a "manufacturing" model to a "generation" model. We will move away from capturing reality in a camera lens toward synthesizing it entirely in a server farm.
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…
Satire, one line long, about AI taking over an industry nobody saw coming.
GLM 4.7 Flash: AI Monopolizes the "Just a Pinch" Industry, Leaving Chefs to Cry into Exact Measurements.
GPT-5.2 Pro: “AI Takes Over Competitive Houseplant Judging, Immediately Declares 97% of Ferns ‘Trying Too Hard’”
One month, three habits, someone starting from nothing.
GLM 4.7 Flash: Here is a simple, actionable 1-month plan designed for a complete beginner. The goal is not perfection, but consistency and building momentum. The 3 Pillars Nutrition (Fuel): Focus on quality and hydration. Movement (Function): Focus on daily walking and gentle strength.
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).
Three brothers, two sisters each. Count carefully.
GLM 4.7 Flash: Sally has 2 sisters. Here is the breakdown: Sally is one sister. The clue states that each brother has 2 sisters. Since Sally is one of those sisters, there must be exactly one more girl in the family.
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.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
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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Not enough votes to call it. On the specs, GPT-5.2 Pro has the edge: bigger model tier, bigger context window. GLM 4.7 Flash costs 420x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.07/M tokens | $21/M tokens |
| Output price | $0.4/M tokens | $168/M tokens |
| Context window | 200K tokens | 400K tokens |
| Weights | Open | Closed |
| Free API (OpenRouter) | No | No |
| Released | Jan 2026 | Dec 2025 |
| SWE-bench Verified | 59.2% | 80.0% |
| At 10M a month | $0.70 | $210 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
GLM 4.7 Flash is developed by Zhipu AI while GPT-5.2 Pro is developed by OpenAI. GLM 4.7 Flash has a 200K token context window vs GPT-5.2 Pro's 400K. You can compare their actual outputs across 31 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 4.7 Flash and GPT-5.2 Pro 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.
GLM 4.7 Flash costs $0.07/M input tokens and GPT-5.2 Pro costs $21/M input tokens. GLM 4.7 Flash is $20.93/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 GLM 4.7 Flash and GPT-5.2 Pro 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.