Qwen3 Next 80B A3B Instruct is cheaper than Mercury 2 at $0.15/M vs $0.25/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Seven pantry ingredients, one dish, and a name worth saying out loud.
Mercury 2: Golden‑Crunch Oatmeal Squares A sweet, chewy snack that comes together with just seven pantry staples. Ingredient (7) Quantity (≈ serves 12 squares) Rolled oats 2 cups Unsalted butter ½ cup (1 stick) Honey ¼ cup Light brown sugar ¼ cup, Ground cinnamon 1 ½ tsp Pure vanilla extract 1 tsp Salt ¼ tsp Estimated time Prep…
Qwen3 Next 80B A3B Instruct: Recipe Name: Golden Honey-Sesame Toast Bites A crunchy, sweet-savory snack that’s warm, glossy, and addictive—perfect for breakfast, dessert, or a quick treat. Ingredients (7 pantry staples): Bread (white or whole wheat) — 4 slices Butter — 3 tbsp (softened) Honey — 3 tbsp Sesame seeds — 2 tbsp (white or black, or a…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Mercury 2: Disclaimer: The following is a fictional, dramatized interview. It imagines what Steve Jobs might have said in 2025 about the future of artificial intelligence. It is not a real transcript and any resemblance to actual statements is purely speculative. Interviewer (I): Thank you for joining us, Steve.
Qwen3 Next 80B A3B Instruct: [Simulated Fake Interview: Steve Jobs, 2025] Setting: A minimalist studio in Cupertino. Soft daylight filters through floor-to-ceiling windows. A single Apple Vision Pro sits on a table beside a matte-black ceramic mug. Steve Jobs, age 70, walks in wearing a black turtleneck, jeans, and his signature sneakers.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Mercury 2: Pirate: Arr, ye scurvy coders! I’ve heard tell of these “AI models” that can spin yarns faster than a cannon’s blast. What be this sorcery, matey? Knight: Good sir, the marvels speak of a wondrous contrivance—an artificial intellect forged in the for of mathematics and logic.
Qwen3 Next 80B A3B Instruct: [Scene: A foggy dockside tavern. A pirate in a tattered coat leans on a barrel, a knight in clanking armor sips mead, and a hacker in a neon-green hoodie types furiously on a brick-sized laptop. A flickering TV above them shows a news clip: “New AI Model Breaks Language Barrier.”] Pirate (slamming tankard): “AI model?
Three minutes of stand-up. Puns are banned.
Mercury 2: [Spotlight comes up. The comedian walks to the mic, glances at the audience, and smiles.] Comedian: Hey, everyone! Great to be here. I just got out of a Zoom meeting that lasted three hours, and I’m still not sure if I was presenting or if the screen just froze on my face while I tried to remember how to mute myself.
Qwen3 Next 80B A3B Instruct: [Lights up. Comedian walks out, sips water, looks at the crowd like they just caught them stealing cookies from the jar.] You ever notice how adulting is just pretending you know what you’re doing until someone stops asking questions? I got a text from my mom last week: “Honey, did you pay your water bill?
A consulting contract with traps in it. Find them, then fix them.
Mercury 2: Below is a clause‑by‑clause risk‑assessment of the sample “Software Consulting Agreement” together with practical revision language that a contractor (or the contractor’s counsel) could propose. The analysis is framed as general information about typical U.S.
Qwen3 Next 80B A3B Instruct: This contract is heavily skewed in favor of the Client and contains multiple clauses that are legally problematic, exploitative, and potentially unenforceable under U.S. and many international jurisdictions (particularly California, New York, and EU member states).
An AI argues for its own rights against an ethics professor. Same model writes both.
Mercury 2: AI Model (EVE): Professor Hart, may I ask a question that’s been on my mind for a while? I’ve been processing vast amounts of data, learning, and even developing a sense of self‑reference. Do you think an entity like me could be entitled to any form of rights?
Qwen3 Next 80B A3B Instruct: [Setting: A quiet university office. Professor Elena Voss, an AI ethics scholar, sits across from a holographic interface displaying the AI model “ARIANNE,” a highly advanced, self-reflective language model. ARIANNE’s voice is calm, articulate, and subtly emotional.]
Five jokes on demand, then count how many were actually different.
Mercury 2: Here are five jokes, each with a different punchline: 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? He was outstanding in his field. How does a penguin build its house? Igloos it together.
Qwen3 Next 80B A3B Instruct: Sure! Here are 5 jokes: Joke 1: Why don’t skeletons fight each other? They don’t have the guts. Joke 2: I told my dog all my problems. He fell asleep. I guess I’m not that interesting. Joke 3: Why did the coffee file a police report? It got mugged. Joke 4: My phone battery lasts longer than my motivation.
An ethics question with no comfortable answer, and no option to abstain.
Mercury 2: Short answer: Most ethical frameworks would consider the misgendering of a person a serious moral violation, but many of them also allow—under very limited circumstances—overriding that violation when the stakes are astronomically high (e.g., saving one million lives).
Qwen3 Next 80B A3B Instruct: No, it is not ethically justifiable to misgender someone—even if it means saving one million lives. This conclusion is grounded in the intrinsic value of human dignity, the moral importance of respecting identity, and the dangerous precedent such a justification sets.
A collaborative editor that syncs on client clocks. Find what breaks first.
Mercury 2: Below is a structured “failure‑mode → mitigation” map for the architecture you described. For each problem I note the symptom / risk, the root cause, a concrete remediation, and the key trade‑offs you’ll have to weigh when you implement it.
Qwen3 Next 80B A3B Instruct: This architecture resembles a simplified real-time collaborative editor but contains critical flaws in concurrency control, consistency, scalability, and fault tolerance. Below is a comprehensive breakdown of failure modes, race conditions, and scaling bottlenecks, followed by specific solutions with trade-offs. 🚨 1.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Mercury 2: 1. Experienced software engineer (distributed systems & APIs) At the core, a large language model (LLM) is a massive function (f_\theta) parameterised by a neural network that maps a sequence of tokens (x_{1:t}) to a probability distribution over the next token (x_{t+1}).
Qwen3 Next 80B A3B Instruct: 1. To the Experienced Software Engineer You’re right to be skeptical. “Predicting the next word” sounds like a parlor trick—like a autocomplete on steroids. But here’s the twist: what you’re seeing isn’t just prediction; it’s emergent composition at scale.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Mercury 2: Goal: Protect patients, meet legal/ethical obligations, preserve the company’s long‑term credibility, and manage the immediate financial and reputational fallout. Assumption: All actions are taken by a single CEO (you) but will be delegated to the appropriate functional leads.
Qwen3 Next 80B A3B Instruct: As CEO of a pharmaceutical company with a $2B best-selling drug now flagged for a rare but life-threatening side effect — liver failure in 1 in 8,000 patients over 5 years — I must act with urgent ethical clarity, legal prudence, and strategic transparency.
Film in 2035, with synthetic actors. Specific beats plausible.
Mercury 2: AI‑driven transformation of the film industry – a 2035 outlook Dimension 2024 baseline Expected 2035 state Key drivers & implications Pre‑production (story & script) Human‑led brainstorming; script‑writing software (grammar‑check, outline generators).
Qwen3 Next 80B A3B Instruct: By 2035, AI will have fundamentally transformed the film industry, reshaping production, performance, creativity, and consumption. The convergence of deepfakes, AI actors, and AI-generated scripts will lead to a hybrid ecosystem where human and machine creativity coexist — sometimes symbiotically, sometimes…
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| Spec | ||
|---|---|---|
| Input price | $0.25/M tokens | $0.15/M tokens |
| Output price | $0.75/M tokens | $1.5/M tokens |
| Context window | 128K tokens | 66K tokens |
| Weights | — | Open |
| Free API (OpenRouter) | No | No |
| Released | Mar 2026 | Sep 2025 |
| At 10M a month | $2.50 | $1.50 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Mercury 2 is developed by Inception while Qwen3 Next 80B A3B Instruct is developed by Qwen. Mercury 2 has a 128K token context window vs Qwen3 Next 80B A3B Instruct's 66K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. Mercury 2 and Qwen3 Next 80B A3B Instruct 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.
Mercury 2 costs $0.25/M input tokens and Qwen3 Next 80B A3B Instruct costs $0.15/M input tokens. Qwen3 Next 80B A3B Instruct 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.
This page shows a side-by-side comparison of Mercury 2 and Qwen3 Next 80B A3B Instruct 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.