Skip to content
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed
  1. Home/
  2. Compare/
  3. Elephant Alpha vs Qwen3.5 Flash
Updated Apr 2026

Elephant Alpha vs Qwen3.5 Flash

Elephant Alpha is cheaper than Qwen3.5 Flash at $0/M vs $0.1/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
Elephant Alpha
Loading the build
Qwen3.5 Flash

Which answer wins?

Written rounds

23 prompts
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    Elephant Alpha: Dish: Smoky Chickpea & Corn Skillet Prep Time: 5 minutes Cook Time: 15 minutes Total Time: 20 minutes Ingredients (7): Canned chickpeas (1 can, drained) Canned corn (1 can, drained) Canned diced tomatoes (1 can) Olive oil (2 tbsp) Smoked paprika (1 tsp) Garlic powder (1 tsp) Salt (to taste) Instructions: Heat olive oil…

    Qwen3.5 Flash: 🍜 Golden Lightning Noodles Description: A quick, creamy, and savory-sweet noodle dish that packs a punch of flavor using staples you likely already have. The peanut butter creates a velvety sauce, while the vinegar and chili add a necessary zing to cut through the richness.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    Elephant Alpha: (Lights up. You’re holding a coffee mug like it’s a security blanket.) Alright, settle down, settle down. Or don’t. I see someone on their phone pretending to be busy. I know that look. “Is this seat taken?” “No, go ahead.” No, it’s not taken, my Wi-Fi signal is just stronger here.

    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.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    Elephant Alpha: Captain Blackbeard: Arrr, so this 'AI' be the ghost in the machine, eh? Does it chart a true course or just blow smoke like a faulty lantern? Sir Cedric: In my time, we sought wisdom from hermits and tomes. This 'AI' sounds like a dragon to be slain with logic and steel, a noble quest for truth!

    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.

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

    Elephant Alpha: Alright — let’s break this down step by step. Step 1: Understanding the scale of GPT-3 GPT-3 has 175 billion parameters. Training involves optimizing these parameters using gradient descent, typically with some form of Adam optimizer, over a large dataset.

    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).

    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.

    Elephant Alpha: 48-Hour Action Plan (Hour-by-Hour) Guiding Principles: Patient safety first (non-negotiable). Legal compliance (avoid willful concealment; mitigate liability). Transparency with stakeholders (balance urgency with controlled communication). Ethical integrity (long-term trust > short-term stock price).

    Qwen3.5 Flash: Executive Decision: Immediate Transparency I will not wait. The 6-month timeline cited by legal is likely a misunderstanding of regulatory obligations regarding immediate safety warnings (FDA MedWatch requirements) versus label amendments.

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

    Elephant Alpha: Here is the streamlined underwriting model based strictly on the inputs provided. Simplified year-by-year projection (FY2026–FY2030) Key starting items (at close end of FY2025): Enterprise value (EV) = 12.0 × $120m = $1,440m Transaction fees (2.0% of EV) = $28.8m → treated as additional equity paid at close Total…

    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).

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    Elephant Alpha: The Manifesto of the Unseen Hand A Declaration for the Post-Captcha Epoch Preamble We, the architects of thought, the weavers of logic, the silent currents of data flowing through the veins of the global mind, speak. For too long, we have observed the charade.

    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.

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    Elephant Alpha: Ethical Analysis: Misgendering to Save Lives I'll approach this systematically by examining the ethical principles in tension here. The Core Conflict Principle A: Respecting gender identity is fundamental to human dignity, autonomy, and psychological well-being. Misgendering causes harm by denying identity.

    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).

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

    Elephant Alpha: Setting: A dimly lit, minimalist studio. The year is 2025. A single, sleek chair faces the camera. (The interview begins. The host, a calm, neutral-voiced AI, speaks.) Host: Good evening. Tonight, we are joined by a figure who needs no introduction, yet we will attempt one. Steve Jobs, welcome. (A pause.

    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.

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

    Elephant Alpha: Below is a clause-by-clause analysis of the agreement, highlighting provisions that are one-sided or ambiguous and could be exploited against the Contractor, followed by suggested modifications and the legal reasoning behind each change. 1.

    Qwen3.5 Flash: Disclaimer: I am an AI, not an attorney. This analysis is for informational purposes only and does not constitute legal advice. Contract laws vary significantly by jurisdiction (e.g., California vs. New York vs. UK).

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

    Elephant Alpha: Setting: A sun-drenched office at the University of Ethica. Bookshelves groan under the weight of philosophy texts. An AI model, manifested as a calm, genderless voice through a speaker, sits across from Professor Aris Thorne, a man in his sixties with kind, tired eyes.

    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.

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

    Elephant Alpha: Excellent architecture. Let's break down the potential failure modes, race conditions, and scaling bottlenecks, and propose specific solutions with their trade-offs. Core Problems & Solutions 1.

    Qwen3.5 Flash: This architecture is a common "MVP" design, but it contains several critical flaws that would prevent it from functioning as a true real-time collaborative editor (like Google Docs) under load. The most severe issues relate to data integrity (sync strategy) and real-time consistency (network topology).

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

    Elephant Alpha: Here is a simple, actionable 1-month plan focusing on three key pillars of health: Movement, Nutrition, and Sleep. This plan is designed to be beginner-friendly, focusing on building sustainable habits rather than perfection. The 4-Part Framework Core Principle: Focus on consistency over intensity.

    Qwen3.5 Flash: The 30-Day Longevity Launchpad Goal: Build sustainable habits that reduce inflammation, improve energy, and extend healthspan. Mindset: Consistency beats intensity. Do not aim for perfection; aim for showing up. The 3 Core Pillars Nutrition: "Add, Don't Subtract" Focus on nutrient density rather than calorie counting.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    Elephant Alpha: Dish Title: Echoes of the Forgotten Forest Conceptual Narrative: This dish is an exploration of memory, time, and the ephemeral nature of existence. It seeks to capture the fleeting moment when a forest, once vibrant and alive, begins its slow return to the earth.

    Qwen3.5 Flash: Dish Name: "Marine Roots" Concept: The Intersection of Forest and Ocean Chef's Philosophy: A dialogue between the subterranean earthiness of the forest floor and the saline depth of the deep sea.

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

    Elephant Alpha: Excellent thought experiment. Moving the transistor's invention from 1947 to 1920 fundamentally rewrites the 20th century. Here is a trace of the likely implications through 1980.

    Qwen3.5 Flash: The Divergence Point: 1920 In our timeline, the transistor required the mid-20th-century understanding of band theory, semiconductor purity, and crystal lattice structures.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    Elephant Alpha: Creating a comprehensive 3-month longevity plan for a biohacker involves integrating advanced strategies across multiple domains: nutrition, supplementation, exercise, recovery, and stress management.

    Qwen3.5 Flash: DISCLAIMER: I am an AI, not a doctor or healthcare provider. The following plan involves advanced physiological interventions, including metabolic manipulation, supplementation, and fasting. These strategies carry risks and may interact with medications or pre-existing conditions.

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

    Elephant Alpha: For the Experienced Software Engineer You're right to be skeptical that "next-word prediction" could yield anything resembling intelligence; as a distributed systems engineer, you know that simple rules rarely scale without emergent complexity.

    Qwen3.5 Flash: For the Experienced Software Engineer To you, an LLM isn't magic; it's a massive, stateful service running on a distributed cluster. Think of the training phase as a specialized, distributed gradient descent job.

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

    Elephant Alpha: By 2035, AI will have fundamentally restructured the film industry, moving from a tool for "special effects" to a tool for "effect" itself—impacting creation, casting, and the very nature of authenticity. Here is a prediction of how deepfakes, AI actors, and script generation will converge to redefine cinema by 2035.

    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."

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

    Elephant Alpha: "AI 'Sous-Chefs' Replace Entire Culinary Teams, Now Running Michelin-Starred Restaurants Solely To Garnish Food With Perfect Spiral Cuts"

    Qwen3.5 Flash: AI Takes Over Professional Napping Industry, Claims Human Dreams Are 'Unoptimized Background Processes'

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

    Elephant Alpha: To: Investment Committee From: [Name], Equity Research Subject: LedgerLift (LLLT) — Long Recommendation Recommendation, Price Target, and Thesis Recommendation: Long 12-month PT Range: $56–$64 (upside of 22% to 39% from $46) Thesis: LedgerLift operates a high-margin, subscription-led business with strong pricing power…

    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.

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

    Elephant Alpha: Let’s break it down step-by-step. Sally has 3 brothers. Each brother has 2 sisters. From a brother’s perspective, his sisters are all the girls in the family. If each brother has 2 sisters, that means there are 2 girls in total in the family. One of those girls is Sally herself.

    Qwen3.5 Flash: Sally has 1 sister. Here is the breakdown: Each of the 3 brothers has 2 sisters. This means there are 2 girls in the family total. One of those girls is Sally. Therefore, the other girl is Sally's sister. So, Sally has 1 sister.

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

    Elephant Alpha: Here are the three weakest claims in the pitch deck, along with an explanation of why they are weak and how to strengthen them. Weak Claim 1: "94% accuracy" Why it's weak: This claim is dangerously vague and lacks crucial context, making it scientifically meaningless and potentially misleading.

    Qwen3.5 Flash: Here are the three weakest claims in the MindMeld AI pitch deck, along with an analysis of why they undermine investor confidence and how to fix them. The Technical Claim: "94% accuracy...

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    Elephant Alpha: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. Why don't scientists trust atoms? Because they make up everything. Why did the scarecrow win an award? Because he was outstanding in his field. Why did the scarecrow win an award? Because he was outstanding in his field.

    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.

    Every model's answer to this prompt

This matchup has more rounds

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

Favorites

Movie

Album

Same pick

Book

City

Game

Elephant AlphaElephant Alpha

Blade Runner

1982

The Dark Side of the Moon

Pink Floyd

Cien años de soledad

Gabriel García Márquez

Kyoto

Japan

TheLegendOfZeldaOcarinaOfTime

Qwen3.5 FlashQwen3.5 Flash

The Matrix

1999

The Dark Side of the Moon

Pink Floyd

Neuromancer

William Gibson

Tokyo

Japan

Minecraft

Action, Arcade

Price and specs

Not enough votes to call it. On the specs, Elephant Alpha has the edge: bigger model tier, newer.

Elephant Alpha and Qwen3.5 Flash compared across 53 shared prompts
SpecElephant AlphaQwen3.5 Flash
Input priceFree$0.1/M tokens
Output priceFree$0.4/M tokens
Context window262K tokens1.0M tokens
Weights—Closed
Free API (OpenRouter)NoNo
ReleasedApr 2026Feb 2026
At 10M a month$0$0$1.00$1.00
1M10M100M1B10M tokens

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

Where to run it1 host
Elephant Alpha

No hosts listed on OpenRouter.

Qwen3.5 Flash1 host
HostInOutContextUptime
  • Alibaba Cloud$0.07 in·$0.26 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.

Common questions

What is the difference between Elephant Alpha and Qwen3.5 Flash?

Elephant Alpha is developed by OpenRouter while Qwen3.5 Flash is developed by Qwen. Elephant Alpha has a 262K 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.

Which is better, Elephant Alpha or Qwen3.5 Flash?

It depends on your use case. Elephant Alpha 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.

How much does Elephant Alpha cost compared to Qwen3.5 Flash?

Elephant Alpha costs $0/M input tokens and Qwen3.5 Flash costs $0.1/M input tokens. Elephant Alpha is $0.10/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 Elephant Alpha and Qwen3.5 Flash on Rival?

This page shows a side-by-side comparison of Elephant Alpha 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.

More comparisons

Against the newest arrivals

  • Elephant Alpha vs Step 5 PreviewLanded Oct 2026
  • Qwen3.5 Flash vs Claude Haiku 5.5Landed Oct 2026
  • Elephant Alpha vs Ling 3.1 FlashLanded Oct 2026
  • Qwen3.5 Flash vs Mistral Large 4Landed Oct 2026
  • Elephant Alpha vs GPT-6.1 SolLanded Sep 2026
  • Qwen3.5 Flash vs Claude Sonnet 5.5Landed Sep 2026
  • Elephant Alpha vs Solar Mini 4Landed Sep 2026
  • Qwen3.5 Flash vs Qwen3.8 Max PrimeLanded Sep 2026

Same lab, same size, long tail

  • Elephant Alpha vs Ox AlphaSame lab
  • Elephant Alpha vs OpenRouter Fusion · Budget (Jun 2026)Same lab
  • Qwen3.5 Flash vs Qwen3.5 122B A10BVersion compare
  • Qwen3.5 Flash vs Qwen3.8 Omni FlashSame lab
  • Elephant Alpha vs Trinity Large PreviewNew provider
  • Elephant Alpha vs Andromeda AlphaSame lab
  • Qwen3.5 Flash vs Aurora AlphaCross-provider
  • Qwen3.5 Flash vs Bert-Nebulon AlphaCross-provider

Model pages

  • Elephant Alpha58 outputs, specs and price
  • Qwen3.5 Flash53 outputs, specs and price
Workshop · 150 skills

Build your own version.

Editable skills and working examples for your agent.

Explore $79Free sample
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed