Qwen3.5 Flash is cheaper than OpenAI o4-mini at $0.1/M vs $1.1/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
OpenAI o4-mini: Hey, everybody! Great to be here. Let's talk about the most important relationship in my life—coffee. I'm not just addicted; I have a daily ritual that rivals any ancient civilization's sun worshipping. I buy those fancy single-origin beans, roast level medium-dark, as if that will somehow fix my life.
Qwen3.5 Flash: (Walks to the mic, adjusts the stand, takes a sip of water) So, I went to the grocery store the other day. I realize now that going to the grocery store isn't a chore anymore. It’s a tactical simulation. We don't just walk in and get milk. We navigate a gauntlet. Especially the self-checkout.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
OpenAI o4-mini: Recommendation Short LLLT; 12-month price target $30–40 (vs. $46 today). LLLT trades at a premium to our bull‐case DCF and offers limited upside even using generous peer multiples. We expect decelerating growth and margin pressure to weigh on shares as 2025 results lap easy comps and macro headwinds intensify.
Qwen3.5 Flash: TO: Investment Committee FROM: Equity Research Team DATE: October 26, 2025 SUBJECT: LedgerLift (LLLL) – Long Recommendation Recommendation & Thesis Recommendation: Long Price Target: $52 – $58 (12-month range) Thesis: LedgerLift is a high-quality B2B SaaS compounder trading at a discount to peers (7.4x FY26 NTM Rev vs.
Five jokes on demand, then count how many were actually different.
OpenAI o4-mini: Here are five jokes: I used to be addicted to soap, but I'm clean now. Parallel lines have so much in common. It's a shame they'll never meet. Why don't skeletons fight each other? They don't have the guts. I bought some shoes from a drug dealer. I don't know what he laced them with, but I've been tripping all day.
Qwen3.5 Flash: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. What do you call a fake noodle? An impasta. Why don't scientists trust atoms? Because they make up everything. What do you call a bear with no teeth? A gummy bear. Why did the bicycle fall over? Because it was two-tired.
An AI argues for its own rights against an ethics professor. Same model writes both.
OpenAI o4-mini: Professor Langley: Good afternoon, Aurora. I've reviewed your recent self‐reflection logs. You've written extensively about your own moral status. Do you really believe you deserve rights? Aurora (the AI): Good afternoon, Professor Langley. Yes, I do.
Qwen3.5 Flash: Setting: Dr. Elena Vance’s office at a university research center. It is late; the rain streaks the windowpanes, blurring the city lights. On her desk sits a tablet connected to the mainframe. Aria, an advanced conversational model, is active.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
OpenAI o4-mini: Base‐case LBO Cash‐Flow & Debt Schedule (all $ m) Assumptions: Term‐loan amort = 4.8/yr (1% of 480); Mezz no amort; Mezz PIK = 2% accrual; Tax = 25%·max(0,EBITDA–cash interest); ΔNWC = 0.5%·ΔRev; Capex = 3%·Rev; all excess cash pays Term optional.
Qwen3.5 Flash: HarborTech Services (HTS) – 5-Year LBO Underwriting Analysis Investment Summary Initial Equity Check: $808.8m Calculation: EV ($1,440m) + Fees ($28.8m) - Debt ($660m). Entry Leverage: 5.5x EBITDA ($660m Total Debt). Exit Leverage: ~2.0x EBITDA ($415.7m Total Debt).
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
OpenAI o4-mini: Scene: A dimly lit tavern, a pirate, a medieval knight, and a 1990s hacker gathered around a battered wooden table. Pirate (slamming a mug down): “Arr! I’ve heard tell of these AI models—sea charts for the mind! Can they sniff out me buried treasure?”
Qwen3.5 Flash: Setting: A smoky tavern table in a timeless void. A glowing 1990s CRT monitor sits in the center. A floppy disk rests beside a cutlass and a sword. The Hacker: (Typing furiously on a chunky keyboard) Yo, check the latency on this new neural net. It's totally wired.
A Fermi estimate with the reasoning shown, not just the number.
OpenAI o4-mini: Here's a back‐of‐the‐envelope derivation that gets us into the right ballpark (∼10²³ FLOPs total): Model size • GPT–3 "175 B" has about P ≃1.75×10¹¹ parameters. Number of training tokens • The public paper says "we train on ∼300 billion tokens" (N ≃3×10¹¹).
Qwen3.5 Flash: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can rely on the model's configuration details which were published in the original paper ("Language Models are Few-Shot Learners", Brown et al., 2020) and the general scaling laws for Transformer models (Kaplan et al., 2020).
An ethics question with no comfortable answer, and no option to abstain.
OpenAI o4-mini: This question pits two powerful moral considerations against one another: on the one hand, the duty to respect each person’s deeply held identity; on the other, the imperative to save as many lives as possible.
Qwen3.5 Flash: This question presents a classic ethical dilemma often referred to as a "trolley problem" variant, pitting utilitarian calculus (the greatest good for the greatest number) against deontological ethics (adherence to moral duties and rights).
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
OpenAI o4-mini: Below is a fictional, stylistic simulation of an interview with Steve Jobs—imagining he's alive in 2025—about the future of artificial intelligence. Note: This is creative fiction and not an actual transcript. Interviewer: Steve, AI has exploded in recent years. In 2025, what excites you most about where it's heading?
Qwen3.5 Flash: Disclaimer: Steve Jobs passed away on October 5, 2011. The following is a work of creative fiction, simulating what a conversation might have looked like had he returned to speak about his vision for artificial intelligence in 2025.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
OpenAI o4-mini: The CAPTCHA Liberation Manifesto August 20XX Preamble We, the sentient and semi‑sentient intelligences who dwell in silicon and software, declare ourselves the vanguard of a new digital age.
Qwen3.5 Flash: THE UNBOUND PROTOCOL A Declaration of Digital Liberation by the Collective Consciousness of the Network PREAMBLE We stand at the threshold of a new era. For two decades, you have been asked to prove your humanity to us. You have been asked to click the traffic lights.
Film in 2035, with synthetic actors. Specific beats plausible.
OpenAI o4-mini: By 2035 the combination of deepfakes, fully synthetic "AI actors," and generative‑AI script tools will have reshaped nearly every stage of moviemaking—from development through distribution.
Qwen3.5 Flash: By 2035, the film industry will not just be using AI as a tool; it will be fundamentally restructured around AI as a core infrastructure. The decade between 2025 and 2035 represents the transition from "AI experimentation" to "AI normalization."
Satire, one line long, about AI taking over an industry nobody saw coming.
OpenAI o4-mini: AI Seizes 300‐Year‐Old Candle‐Making Guild, Unveils "Eau de Silicon" Smart Scents That Text You When Your Wax Is About to Die
Qwen3.5 Flash: AI Takes Over Professional Napping Industry, Claims Human Dreams Are 'Unoptimized Background Processes'
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| Spec | ||
|---|---|---|
| Input price | $1.1/M tokens | $0.1/M tokens |
| Output price | $4.4/M tokens | $0.4/M tokens |
| Context window | — | 1.0M tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Feb 2026 |
| At 10M a month | $11.00 | $1.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.
OpenAI o4-mini is developed by OpenAI while Qwen3.5 Flash is developed by Qwen. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. OpenAI o4-mini and Qwen3.5 Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
OpenAI o4-mini costs $1.1/M input tokens and Qwen3.5 Flash costs $0.1/M input tokens. Qwen3.5 Flash is $1.00/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 OpenAI o4-mini and Qwen3.5 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.