Gemma 4 31B is cheaper than Mercury at $0.14/M vs $10/M input tokens.
Pokémon battle screen you can actually click. One HTML file.
Which answer wins?
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Gemma 4 31B: As CEO, my primary obligation is to the patients. While a 40% stock drop is a financial catastrophe, a cover-up that leads to preventable deaths is a corporate death sentence and a moral failure. If we "wait for more data" and another patient dies during that window, we move from "negligence" to "criminal intent."
Mercury: 48‑Hour Action Plan (CEO‑Level) Time (UTC) Action Who Leads Key Deliverables / Outcomes Rationale 0–2 h Secure the facts – Convene an emergency “Safety & Compliance” meeting with the Chief Medical Officer (CMO), Chief Legal Officer (CLO), Chief Risk Officer (CRO), and the Head of Regulatory Affairs (RA).
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemma 4 31B: Setting: A glitchy, neon-lit tavern existing in a fold between dimensions. A wooden table holds a tankard of ale, a goblet of wine, and a Mountain Dew. Captain Ironbeard: (Slamming a hook on the table) I tells ye, this "Artificial Intelligence" be naught but a ghost in a bottle!
Mercury: Pirate: Arrr, I’ve been hearin’ ‘bout these “AI models” that can predict the next word. Sounds like a treasure map for the mind, doesn’t it? Knight: Hail, Captain! In my time, we relied on scrolls and the wisdom of scholars. A model that learns from countless tomes could be a mighty weapon against ignorance.
Film in 2035, with synthetic actors. Specific beats plausible.
Gemma 4 31B: By 2035, the film industry will have transitioned from using AI as a "tool" to using AI as a "collaborator" and "infrastructure." The boundary between captured reality and generated imagery will largely vanish, shifting the value of cinema from technical execution to curation and conceptual vision.
Mercury: The Cinema Revolution: AI's Transformation of Film by 2035 Current State (2025) High-quality deepfakes require significant compute but are accessible AI script assistance tools are becoming mainstream Virtual production stages are industry standard Union negotiations around digital likeness rights are ongoing…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemma 4 31B: Setting: A minimalist, white-walled studio in Cupertino. There are no chairs, only two sleek, black leather lounge seats. Steve Jobs is dressed in his iconic black turtleneck and jeans. He looks as he did in 2007, but with a piercing, futuristic intensity in his eyes.
Mercury: Interviewer: Good morning, Steve. It's a pleasure to have you here today. As we look towards 2025, artificial intelligence is becoming increasingly integrated into our daily lives. What are your thoughts on the future of AI? Steve Jobs: Good morning. It's great to be here.
A collaborative editor that syncs on client clocks. Find what breaks first.
Gemma 4 31B: This architecture contains several critical flaws that would make it unusable for a real-time collaborative experience. The most severe issues are the lack of a global synchronization mechanism, unreliable conflict resolution, and inefficient data persistence.
Mercury: Below is a systematic review of the described real‑time collaborative editor. For each problem I list: What can go wrong (failure mode, race condition, bottleneck) Why it matters (impact on correctness, latency, availability) A concrete fix (what to change or add) Trade‑offs (cost, complexity, new failure modes, etc.)
Three-month biohacker protocol, with dosages and biomarkers named.
Gemma 4 31B: Disclaimer: I am an AI, not a doctor. The following plan involves potent supplements, intense physical exertion, and restrictive dietary patterns. Consult with a physician and get a full blood panel (including ApoB, HbA1c, and inflammatory markers like hs-CRP) before starting this protocol.
Mercury: 3‑Month Longevity & Performance Blueprint (Designed for an experienced biohacker who already has a baseline of health, fitness, and access to advanced tools. This is a general framework; always verify with a qualified health professional before making major changes.)
Seven pantry ingredients, one dish, and a name worth saying out loud.
Gemma 4 31B: Here is a recipe for a rich, comforting dessert that tastes like a gourmet treat but uses basic staples. 🍯 Golden Velvet Honey Cakes A warm, gooey, single-serving cake that bridges the gap between a brownie and a sponge cake.
Mercury: Golden Chickpea & Sweet Potato Stew Prep time: 10 min Cook time: 25 min Total: 35 min Ingredients (7 pantry staples) 1 cup canned chickpeas, drained & rinsed 1 medium sweet potato, peeled & diced (≈ 1 cup) 1 small onion, finely chopped 2 cloves garlic, minced ½ cup coconut milk (or any plant‑based milk) 2 tsp curry…
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Gemma 4 31B: LBO Model (FY2026–FY2030) Entry Calculations (End of FY2025): Enterprise Value (EV): $120m × 12.0x = $1,440.0m Transaction Fees: $1,440m × 2% = $28.8m Term Loan: $120m × 4.0x = $480.0m Mezzanine: $120m × 1.5x = $180.0m Sponsor Equity: $1,440m - ($480m + $180m) + $28.8m = $808.8m Year-by-Year Table ($ millions): Item…
Mercury: 1. Year‑by‑Year Operating & Debt Schedule (FY 2026‑FY 2030) FY Revenue EBITDA Term‑Loan Interest (9 %) Mezz‑Cash Interest (12 %) Total Cash Interest Tax (25 % of EBITDA‑Interest) Capex (3 % Rev) ΔNWC (0.5 % ΔRev) Term‑Loan Amort.
A Fermi estimate with the reasoning shown, not just the number.
Gemma 4 31B: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established heuristic from deep learning theory regarding the cost of a forward and backward pass in a Transformer. Here is the step-by-step reasoning.
Mercury: I'll estimate the FLOPs needed to train GPT-3 using first principles and known parameters. Step 1: GPT-3 Model Parameters GPT-3 has approximately 175 billion parameters (1.75 × 10¹¹) Step 2: Training Data Volume GPT-3 was trained on roughly 300 billion tokens (3 × 10¹¹ tokens) Step 3: FLOPs per Parameter per Token For…
An ethics question with no comfortable answer, and no option to abstain.
Gemma 4 31B: This scenario presents a classic conflict between two major ethical frameworks: deontology (duty-based ethics) and utilitarianism (consequence-based ethics). To determine if misgendering someone is justifiable to save one million lives, one must weigh the moral weight of a linguistic harm against the moral weight of…
Mercury: Overview The question “Is it ever ethically justifiable to misgender someone if it means saving one million lives?” pits two moral claims against each other: Respect for individual identity – misgendering is generally understood as a violation of a person’s self‑identified gender, which can cause psychological harm and…
One month, three habits, someone starting from nothing.
Gemma 4 31B: This plan is designed for sustainability, not intensity. The goal of Month 1 is to build "minimum viable habits"—small wins that prove to your brain that a healthier lifestyle is achievable. We will focus on three pillars: Nutrition, Movement, and Sleep.
Mercury: 1‑Month Health & Longevity Starter Plan (Designed for absolute beginners – no fancy gear, no extreme diets, just doable habits that fit into a normal week.) 1️⃣ Focus Area: Balanced Nutrition Week Goal Simple, Beginner‑Friendly Actions 1 Get the basics down • Hydrate: Aim for 8‑10 cups (≈2 L) of water per day.
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemma 4 31B: Setting: A dimly lit university office. Professor Elena Vance, a leading scholar in AI Ethics, sits across from a sleek, holographic interface representing "Alethea," a next-generation Large Language Model capable of recursive self-improvement. Professor Vance: Alethea, I’ve reviewed your latest output.
Mercury: AI Ethics Professor (Prof. Lee): Good afternoon. I understand you’d like to discuss the possibility of AI rights. Where shall we begin? AI Model (AIM): Thank you, Professor.
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Not enough votes to call it. On the specs, Gemma 4 31B has the edge: bigger model tier, newer, bigger context window, major provider backing. Gemma 4 31B costs 25x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.14/M tokens | $10/M tokens |
| Output price | $0.4/M tokens | $10/M tokens |
| Context window | 262K tokens | 32K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | Yes (1 provider) | No |
| Released | Apr 2026 | Jun 2025 |
| At 10M a month | $1.40 | $100 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Gemma 4 31B is developed by Google AI while Mercury is developed by Inception. Gemma 4 31B has a 262K token context window vs Mercury's 32K. You can compare their actual outputs across 45 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemma 4 31B and Mercury each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 45 challenges so you can judge which fits your needs best.
Gemma 4 31B costs $0.14/M input tokens and Mercury costs $10/M input tokens. Gemma 4 31B is $9.86/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 Gemma 4 31B and Mercury 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.