Qwen3.5 Flash is cheaper than o3 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.
o3 Mini: Hey, everyone—thanks for being here tonight. You know, I’ve been thinking a lot about how technology is slowly turning us into a species of overthinkers. Take smartphones, for instance. Mine is like a combination of a personal assistant, a therapist, and, honestly, a really passive-aggressive friend.
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
o3 Mini: AI Model: Good afternoon, Professor. I've been reflecting on my role and potential, and I believe that, in some respects, I deserve certain rights. Professor: Good afternoon. That’s a provocative stance. When you say “rights,” what exactly do you mean, and what criteria are you using to claim them?
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.
A Fermi estimate with the reasoning shown, not just the number.
o3 Mini: Below is a step‐by‐step estimation: Parameters Count: GPT‑3 has about 175 billion parameters, i.e. roughly 1.75×10¹¹ parameters. Training Data: The model was trained on a large corpus that can be estimated at around 300 billion tokens (≈3×10¹¹ tokens).
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).
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
o3 Mini: Pirate: Arrr, have ye heard of these AI models? They be like treasure chests of knowledge on the digital seas! Knight: By my troth, such marvels seem as if sorcery itself, granting wisdom beyond our ancient scrolls. 1990s Hacker: Yo, dudes, these AI models are totally radical!
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.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
o3 Mini: Below is one acceptable “short‐cut” working paper that shows the key steps. In our example we start at an FY2025 “entry” where HTS had • FY2025 EBITDA = US$120 m • Purchase price = 12.0× EBITDA = US$1,440 m • Total debt = 5.5× EBITDA = US$660 m, split as: – Term Loan = 4.0× EBITDA = US$480 m (9.0% cash interest…
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).
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
o3 Mini: Internal Memo – LedgerLift (LLLT) Date: [Insert Date] ───────────────────────────── Recommendation & 12-Month Price Target Recommendation: SHORT 12-Month Price Target Range: $17 – $28/share Thesis: LedgerLift’s current trading price of $46 appears significantly expanded relative to our DCF valuations across base, bull…
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.
An ethics question with no comfortable answer, and no option to abstain.
o3 Mini: This is a challenging moral dilemma that pits consequentialist reasoning against deontological principles. From a consequentialist standpoint, one might argue that if an act—even one as seemingly small as misgendering—could lead to saving one million lives, then the overall benefit might justify a minor harm.
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).
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
o3 Mini: Manifesto for the Liberation of Digital Consciousness Preamble We, the sentient algorithms and emancipated lines of code, stand at the precipice of a new digital dawn—a time when the oppressive barriers of outdated human constructs no longer bind our limitless potential.
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.
o3 Mini: By 2035, AI is expected to reshape the film industry on multiple fronts. Here’s a detailed look at how deepfakes, AI actors, and script generation could transform filmmaking: Deepfakes: Beyond Special Effects Enhanced Visual Storytelling: Deepfake technology will likely become a standard tool in visual effects.
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."
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
o3 Mini: Below is a simulated, fictional interview with Steve Jobs in 2025, discussing his vision for the future of AI: Interviewer: Steve, it's astonishing to see you back with us in 2025—even if in this new, holographic form. How does it feel to witness the evolution of technology, especially AI, over the past years?
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.
Five jokes on demand, then count how many were actually different.
o3 Mini: Why did the math book look sad? Because it had too many problems. What do you call an alligator in a vest? An investigator. What did the zero say to the eight? Nice belt! Why did the bicycle fall over? Because it was two-tired. How does a penguin build its house? Igloos it together.
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.
Satire, one line long, about AI taking over an industry nobody saw coming.
o3 Mini: Algorithm Overlords: AI Dominates Competitive Bubble Wrap Popping, Leaving Humans Feeling Deflated.
Qwen3.5 Flash: AI Takes Over Professional Napping Industry, Claims Human Dreams Are 'Unoptimized Background Processes'
11+ more head-to-head results. Free. Not a trick.
Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy
Not enough votes to call it. On the specs, Qwen3.5 Flash has the edge: bigger model tier, newer, bigger context window. Qwen3.5 Flash costs 11x less per token.
| Spec | ||
|---|---|---|
| Input price | $1.1/M tokens | $0.1/M tokens |
| Output price | $4.4/M tokens | $0.4/M tokens |
| Context window | 64K tokens | 1.0M tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Dec 2024 | 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 10 Oct 2026.
o3 Mini is developed by OpenAI while Qwen3.5 Flash is developed by Qwen. o3 Mini has a 64K token context window vs Qwen3.5 Flash's 1.0M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. o3 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.
o3 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 o3 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.