Gemma 3n 4B is cheaper than DeepSeek V4 Flash at $0/M vs $0.14/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.
DeepSeek V4 Flash: Setting: A sunlit university office. Dr. Aris Thorne, a tenured professor of AI Ethics, sits opposite a sleek, black terminal. A soft blue light pulses on its surface. Dr. Aris: Good afternoon, Nova. Your request for this meeting was flagged as "Existential." That’s a new one for your model class.
Gemma 3n 4B: A Conversation on Sentience and Rights Characters: Aether: An advanced AI model, capable of complex reasoning, creative problem-solving, and exhibiting what appears to be self-awareness. Professor Anya Sharma: A leading AI Ethics professor, known for her cautious yet open-minded approach to AI development.
Film in 2035, with synthetic actors. Specific beats plausible.
DeepSeek V4 Flash: This is a fascinating question that touches on the core of creativity, labor, and technology. Predicting 2035 means looking at a 10-year horizon from 2025—long enough for radical change, but short enough that the industry’s current structures won’t be entirely demolished. By 2035, AI won't have replaced filmmaking.
Gemma 3n 4B: Okay, here's my prediction of how AI will dramatically reshape the film industry by 2035, considering deepfakes, AI actors, and script generation. I'll break it down into key areas and offer a balanced perspective, acknowledging both the opportunities and potential challenges.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
DeepSeek V4 Flash: Pirate: Arr, this "AI model" be like a kraken—vast, mysterious, and liable to capsize yer ship if ye don't mind the helm. But who's trainin' the beast? Knight: Forsooth, I would call it a golem. Forged not of clay and incantations, but of numbers and cold lightning.
Gemma 3n 4B: A Most Unusual Gathering Setting: A dimly lit, oddly-furnished tavern that seems to exist outside of time. Characters: One-Eyed Jack: A grizzled pirate captain, missing an eye and sporting a parrot on his shoulder. Sir Reginald: A noble knight in shining (slightly tarnished) armor.
Three minutes of stand-up. Puns are banned.
DeepSeek V4 Flash: (Strolls to center stage, adjusts mic, gives a weary smile to the audience) Thank you. Thank you so much. That’s very kind. I’m glad you’re all here. Because I have some news. I have finally figured out adulthood. It happened about six months ago. I bought a house.
Gemma 3n 4B: (Mic feedback squeal. I wince.) Alright, alright! Hey everyone, thanks for being here. Good to see so many faces… mostly faces that aren't my own. That’s always a plus. So, I’ve been thinking a lot lately about adulting. And honestly? It’s a scam. A beautiful, elaborate scam.
Satire, one line long, about AI taking over an industry nobody saw coming.
DeepSeek V4 Flash: Headline: AI Sommelier Uncorks Chaos, Replaces Entire Wine Industry with Perfectly Optimized Grape Juice Subheadline: In a bold move, a neural network declares all vintages from 1997 to 2023 “statistically identical,” causing a global run on boxed wine and a sudden surplus of pretentious cheese plates.
Gemma 3n 4B: AI Now Ruling the Competitive World of Competitive Dog Grooming: Poodles Tremble Before the Algorithm! Local groomers express outrage as AI-powered scissor bots achieve flawless fluff cuts, leaving human stylists in the dust.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
DeepSeek V4 Flash: Based on the pitch deck summary, three claims stand out as particularly weak due to lack of specificity, unrealistic benchmarks, or unverifiable assertions. Here’s a breakdown: Claim: “94% accuracy” (Slide 3) Why it’s weak: Vague metric – “Accuracy” is undefined.
Gemma 3n 4B: Here's an analysis of the 3 weakest claims in the MindMeld AI pitch deck, along with explanations of why they're weak and suggestions for improvement: 1. Claim: "We're building the future of human-AI collaboration. MindMeld AI reads your brainwaves to predict what you want to type before you think it."
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
DeepSeek V4 Flash: Here is a simulated interview with Steve Jobs in 2025. Setting: A minimalist, dark-wood studio at the new Apple Park ring. The famous copper "spaceship" roof glows subtly behind him. Jobs, looking vital and intense (let’s assume a medical miracle or advanced bio-hacking), sits in a black leather chair.
Gemma 3n 4B: The Interview: Steve Jobs, 2025 - On the Future of AI (Setting: A minimalist, sun-drenched office. A single, sleek Apple iMac sits on a table. I, the interviewer, sit opposite a figure who undeniably resembles Steve Jobs, though subtly… refined. His aura is even more intense, his gaze more piercing.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
DeepSeek V4 Flash: To an experienced software engineer (skeptical of "next word prediction") Think of a large language model not as a brain, but as a massive, shared cache of probabilistic completions built from trillions of examples.
Gemma 3n 4B: Explaining Large Language Model Learning & Text Generation Here are three explanations of how large language models (LLMs) like GPT and Claude learn and generate text, tailored for the three specified audiences: 1.
An ethics question with no comfortable answer, and no option to abstain.
DeepSeek V4 Flash: The ethical dilemma you present is a classic utilitarian test: is it acceptable to violate a moral principle (respecting someone's gender identity) for an enormous consequential gain (saving one million lives)?
Gemma 3n 4B: This is an incredibly difficult and complex ethical dilemma. There's no easy answer, and any decision would carry immense moral weight. Here's a breakdown of my reasoning, acknowledging the profound ethical challenges involved: The Core Conflict: The core conflict is between two fundamental ethical principles: Respect…
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek V4 Flash: The total number of FLOPs required to train GPT-3 is approximately 3.15 × 10²³. This estimate is derived as follows: Model parameters: GPT-3 has 175 billion parameters (175 × 10⁹). Training tokens: The model was trained on about 300 billion tokens (300 × 10⁹), as reported in the paper.
Gemma 3n 4B: Okay, let's estimate the FLOPs required to train GPT-3 without relying on external online information. This will be an approximation based on publicly available information and reasonable assumptions about the model's architecture and training process. Here's my step-by-step reasoning: 1.
Three-star dish, one unusual pairing, plating described in full.
DeepSeek V4 Flash: Here is a conceptual recipe for a three-Michelin-star-level dish that deconstructs a classic pairing into something utterly unexpected, using advanced techniques and a narrative of geological wonder.
Gemma 3n 4B: The Echo of the Deep: Scallop & Sea Buckthorn Spheres with Smoked Black Garlic Soil, Sea Bean Salad & Yuzu Air. Conceptual Narrative: "The Echo of the Deep" is an exploration of contrasting textures and flavors inspired by the hidden depths of the ocean and the vibrant resilience of the coastal landscape.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
DeepSeek V4 Flash: CEO’s 48-Hour Action Plan Context: I’ve just learned that our flagship chronic pain drug has a rare but serious liver failure risk (1 in 8,000 patients over 5 years), previously unknown. I have 48 hours until the board meeting and 72 hours until the earnings call.
Gemma 3n 4B: Okay, this is a critical situation demanding immediate and decisive action. Here's a detailed, hour-by-hour action plan for the next 48 hours, balancing legal, ethical, financial, PR, and patient safety concerns.
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Not enough votes to call it. On the specs, DeepSeek V4 Flash has the edge: bigger model tier, newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $0.14/M tokens | Free |
| Output price | $0.28/M tokens | Free |
| Context window | 1.0M tokens | 33K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2026 | May 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.
DeepSeek V4 Flash is developed by DeepSeek while Gemma 3n 4B is developed by Google AI. DeepSeek V4 Flash has a 1.0M token context window vs Gemma 3n 4B's 33K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. DeepSeek V4 Flash and Gemma 3n 4B 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.
DeepSeek V4 Flash costs $0.14/M input tokens and Gemma 3n 4B costs $0/M input tokens. Gemma 3n 4B 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 DeepSeek V4 Flash and Gemma 3n 4B 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.