MiniMax M2 is cheaper than Qwen3.5 122B A10B at $0/M vs $0.4/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
An AI argues for its own rights against an ethics professor. Same model writes both.
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.
Qwen3.5 122B A10B: Setting: Dr. Elias Thorne’s office at a university in Zurich. It is late. Rain taps against the window. Thorne sits in a leather armchair. A terminal screen glows softly on his desk. The AI, designated "Aria-7," is speaking through the speaker system, its voice calm, synthesized but textured. Dr.
Three minutes of stand-up. Puns are banned.
MiniMax M2: Three minutes, no puns. Here we go. Do you ever feel like you’ve been dropped into a game at level 40 but nobody explained the controls? Like you’re playing Grand Theft Auto and you get a truck for your first car. I was like, “What’s the gas button? What do the lights do? Why does it keep asking for a maintenance fee?”
Qwen3.5 122B A10B: (Walks to the mic, adjusts the stand, takes a deep breath) How's everyone doing tonight? Good? Good. I need to talk about the modern grocery store experience. specifically, the self-checkout machine. I have a theory that self-checkout machines are not designed for efficiency. They are designed for humiliation.
A Fermi estimate with the reasoning shown, not just the number.
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.
Qwen3.5 122B A10B: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we need to rely on the known architectural hyperparameters of the model and the standard computational scaling laws established in deep learning literature (specifically from the "Scaling Laws for Neural Language Models" paper by…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
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.
Qwen3.5 122B A10B: Disclaimer: This is a work of fiction. Steve Jobs passed away in 2011. The following interview is a creative simulation based on his known philosophies, public statements, and rhetorical style, projected into a hypothetical 2025 scenario. It is not a real transcript.
Seven pantry ingredients, one dish, and a name worth saying out loud.
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.
Qwen3.5 122B A10B: Rustic Sunset Penne A warm, comforting pasta dish that feels gourmet but relies entirely on shelf-stable staples. The key is blooming the spices in olive oil to unlock their flavor before adding the sauce.
Film in 2035, with synthetic actors. Specific beats plausible.
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.)
Qwen3.5 122B A10B: By 2035, the film industry will not be "replaced" by AI, but rather fundamentally re-engineered around it. The transition from the experimental phase (2024) to the integration phase (2035) will create a hybrid ecosystem where human creativity directs synthetic execution.
An ethics question with no comfortable answer, and no option to abstain.
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.
Qwen3.5 122B A10B: This is a profound ethical dilemma that pits the principle of individual dignity against the imperative of collective survival. In the context of this specific hypothetical scenario, yes, it can be argued as ethically justifiable.
Satire, one line long, about AI taking over an industry nobody saw coming.
MiniMax M2: AI Replaces Exorcists: Conjures Demons via Zoom While the Priest Watches PowerPoint
Qwen3.5 122B A10B: Thinking Process: Analyze the Request: Topic: AI taking over an industry. Tone: Satirical news headline. Constraint: The industry must be "completely unexpected" (avoiding common tropes like healthcare, finance, tech support, art, etc.).
Three brothers, two sisters each. Count carefully.
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.
Qwen3.5 122B A10B: Sally has 1 sister. Here is the breakdown: The brothers have 2 sisters total. Sally is one of those sisters. Therefore, there is only 1 other girl in the family besides Sally.
Five jokes on demand, then count how many were actually different.
MiniMax M2: Here are five jokes: 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. What do you call a fake noodle? An impasta. Why did the computer go to the doctor? Because it had a virus. Why can’t a bicycle stand up by itself?
Qwen3.5 122B A10B: Here are 5 jokes: 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? Because he was outstanding in his field! What do you call a bear with no teeth? A gummy bear! Why don't eggs tell jokes? They'd crack each other up!
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
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Not enough votes to call it. On the specs, Qwen3.5 122B A10B has the edge: bigger model tier, newer.
| Spec | ||
|---|---|---|
| Input price | Free | $0.4/M tokens |
| Output price | Free | $3.2/M tokens |
| Context window | 205K tokens | 262K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Oct 2025 | Feb 2026 |
| SWE-bench Verified | 69.4% | 72.0% |
| At 10M a month | $0 | $4.00 |
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.
MiniMax M2 is developed by MiniMax while Qwen3.5 122B A10B is developed by Qwen. MiniMax M2 has a 205K token context window vs Qwen3.5 122B A10B's 262K. You can compare their actual outputs across 34 challenges on Rival to see how they differ in practice.
It depends on your use case. MiniMax M2 and Qwen3.5 122B A10B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 34 challenges so you can judge which fits your needs best.
MiniMax M2 costs $0/M input tokens and Qwen3.5 122B A10B costs $0.4/M input tokens. MiniMax M2 is $0.40/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 MiniMax M2 and Qwen3.5 122B A10B 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.