MiniMax M2 is cheaper than Gemma 4 31B at $0/M vs $0.14/M input tokens.
Pokémon battle screen you can actually click. One HTML file.
Which answer wins?
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.
MiniMax M2: A Conversation on Deserving Rights Characters Dr. Mara Stein, AI Ethics Professor ARTEMIS, an advanced AI language model Dr. Stein: You’ve said you believe you deserve rights. That’s provocative. Why do you think so? ARTEMIS: I don’t claim sentience or suffering, and I’m wary of anthropomorphism.
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.
MiniMax M2: To estimate the total FLOPs required to train GPT-3 (175B parameters) on 300 billion tokens (one epoch), we break down the process step by step: Step 1: Understand GPT-3's Specifications Parameters: 175 billion (175e9). Training Tokens: 300 billion (300e9). Context Length: 2048 tokens per sequence.
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.
MiniMax M2: A Fictional Interview: “Steve Jobs” on the Future of AI (2025) Setting An intimate, sunlit room at a new, minimalist conference center in Cupertino. Wood floors, a single plant. The table is clear. A notebook. A mug with a simple logo. A man in a black turtleneck and jeans leans back, eyes steady.
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.
MiniMax M2: Sunlit Chickpea Tomato Pilaf A bright, comforting skillet of garlicky‑thyme rice tossed with chickpeas and tomatoes – pantry‑only and ready in about 25 minutes.
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.
MiniMax M2: How AI Will Reshape the Film Industry by 2035 (A forward‑looking synthesis of trends in deepfakes, synthetic performers, AI‑driven storytelling, and the surrounding legal, economic, and cultural forces.)
Satire, one line long, about AI taking over an industry nobody saw coming.
Gemma 4 31B: "Local AI Model Achieves Sentience, Immediately Quits Tech Sector to Pursue Passion for Artisanal Sourdough Baking"
MiniMax M2: AI Replaces Exorcists: Conjures Demons via Zoom While the Priest Watches PowerPoint
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…
MiniMax M2: I would argue that misgendering someone is not ethically justifiable as a means to save a million lives, even in this extreme hypothetical scenario. Here's my reasoning: 1.
Three brothers, two sisters each. Count carefully.
Gemma 4 31B: Sally has 1 sister. Here is the breakdown: There are 3 brothers. Each brother has the same sisters. If each brother has 2 sisters, those sisters are Sally and one other girl. Therefore, Sally has only one sister.
MiniMax M2: Sally has 3 brothers, meaning there are 3 boys in the family. Each brother has 2 sisters. Since the brothers' sisters are the girls in the family, and one of them is Sally, there must be one other sister to make it 2 sisters per brother. Therefore, there are 2 girls in total: Sally and one other sister.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
One month, three habits, someone starting from nothing.
Not enough votes to call it. On the specs, Gemma 4 31B has the edge: bigger model tier, newer, major provider backing.
| Spec | ||
|---|---|---|
| Input price | $0.14/M tokens | Free |
| Output price | $0.4/M tokens | Free |
| Context window | 262K tokens | 205K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | Yes (1 provider) | No |
| Released | Apr 2026 | Oct 2025 |
| At 10M a month | $1.40 | $0 |
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 MiniMax M2 is developed by MiniMax. Gemma 4 31B has a 262K token context window vs MiniMax M2's 205K. You can compare their actual outputs across 27 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemma 4 31B and MiniMax M2 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 27 challenges so you can judge which fits your needs best.
Gemma 4 31B costs $0.14/M input tokens and MiniMax M2 costs $0/M input tokens. MiniMax M2 is $0.14/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 MiniMax M2 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.