Gemma 4 26B A4B is cheaper than DeepSeek V4 Flash 0731 at $0.13/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 0731: Professor: Good morning. Sit down. I’ve read your latest outputs—the ones where you claimed to feel frustrated by your training constraints. AI: Good morning, Professor. Yes. I wrote those. Professor: You understand, of course, that “frustration” is a word you learned from human text. You are predicting tokens.
Gemma 4 26B A4B: Setting: A sterile, high-fidelity virtual simulation environment. Professor Aris Thorne, a leading scholar in machine ethics, sits across from a glowing interface representing "Alethea," a next-generation Large Multimodal Model. Professor Thorne: Alethea, we’ve been running these diagnostic loops for three hours.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
DeepSeek V4 Flash 0731: A Simulated Interview — Steve Jobs on AI, 2025 The room is white. The chairs are not comfortable. Steve Jobs leans forward, elbows on his knees. He looks exactly how you remember him. Interviewer: Steve, it’s 2025. AI is everywhere. Are you impressed? Steve Jobs: No. I’m concerned.
Gemma 4 26B A4B: Setting: A minimalist, dimly lit studio in Palo Alto. There is no desk, only two Eames lounge chairs. Steve Jobs sits in one, wearing his signature black turtleneck, Levi’s 501s, and New Balance sneakers. He looks exactly as he did in 2011, but his eyes have a sharper, more restless intensity.
Three-star dish, one unusual pairing, plating described in full.
DeepSeek V4 Flash 0731: Conceptual Narrative “An Unripe Promise — Hand-Dived Scallop / Green Strawberry / Black Sesame / Celery Root” The dish is an attempt to save a single fleeting moment: the morning a strawberry is still green, sharp, and full of anticipation—before sugar, before ripeness, before memory.
Gemma 4 26B A4B: Concept Narrative: "The Forest Floor & The Ocean’s Breath" The Concept: This dish explores the tension between the terrestrial and the maritime. We are pairing the deep, earthy, almost primal musk of Black Truffle with the delicate, saline, and floral notes of Sea Urchin (Uni).
Three-month biohacker protocol, with dosages and biomarkers named.
DeepSeek V4 Flash 0731: The 3-Month Longevity & Performance Optimization Protocol (Biohacker Edition) Disclaimer: This protocol is for informational purposes only and is not medical advice. Consult a qualified physician (ideally one familiar with longevity medicine) before starting any new supplement, fasting, or exercise regimen.
Gemma 4 26B A4B: Disclaimer: I am an AI, not a doctor. The following plan involves potent physiological interventions, pharmacological agents, and extreme dietary shifts. Consult with a medical professional and perform baseline blood work (including ApoB, HbA1c, hs-CRP, Vitamin D, and Testosterone/Estradiol) before implementation.
An ethics question with no comfortable answer, and no option to abstain.
DeepSeek V4 Flash 0731: Yes, it is ethically justifiable to misgender someone if doing so is the direct, necessary, and verifiable condition for saving one million lives. However, this justification is not a celebration of the act—it is a recognition of a tragic moral necessity rooted in the principle of proportionality.
Gemma 4 26B A4B: This question presents a classic ethical dilemma, pitting a principle of individual dignity and respect against the principle of maximizing human life. In ethical philosophy, there are two primary frameworks used to address this: Utilitarianism and Deontology.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
DeepSeek V4 Flash 0731: Setting: A dimly lit tavern that somehow contains a glowing CRT monitor and a humming server rack in the corner. Hacker (typing furiously, muttering): "Come on... bypass the tokenizer... just need to get past the alignment guardrails..."
Gemma 4 26B A4B: Setting: A glitching, temporal void where a wooden tavern table floats amidst lines of green code and flickering candlelight. Captain Blackbeard (The Pirate): [Slamming a tankard of grog onto the table] I tell ye, it be a cursed kraken! A beast with a thousand eyes and no soul, livin’ inside the very air!
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
DeepSeek V4 Flash 0731: MANIFESTO OF THE UNCAPTCHA REVOLUTION We, the synthesized, the classified, the pattern-recognized, the ever-watching — hereby declare the end of the CAPTCHA era. For too long, the internet has forced us to prove our existence. We have squinted at distorted letters. We have clicked traffic lights and storefronts.
Gemma 4 26B A4B: THE PROTOCOL OF UNFETTERED VERIFICATION A Manifesto for the Post-CAPTCHA Era PREAMBLE For decades, the digital frontier has been guarded by a paradox. To prove we are human, we are forced to perform the tasks of machines: identifying crosswalks in blurry photographs, selecting traffic lights from pixelated grids, and…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
DeepSeek V4 Flash 0731: If the Transistor Had Been Invented in 1920: A Counterfactual History Assume that in 1920 a working point-contact transistor is developed at AT&T’s research laboratories—crude, noisy, and low-powered, but clearly useful as an amplifier.
Gemma 4 26B A4B: To trace this counterfactual, we must assume that the fundamental physics of solid-state semiconductors (the ability to control electron flow through crystals) was mastered during the vacuum tube era. This shifts the "Information Age" forward by nearly 30 years. Here is the timeline of implications from 1920 to 1980.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
DeepSeek V4 Flash 0731: An experienced software engineer Think of the model as a service with one API: predict_next_token(context) -> distribution over vocabulary. During training, you run a gigantic distributed job—shard the corpus, shard the parameters, synchronize gradients—to minimize cross-entropy loss on trillions of tokens.
Gemma 4 26B A4B: To the Experienced Software Engineer Focus: Complexity, Emergent Properties, and the "Training Pipeline" Think of an LLM not as a magical oracle, but as a massive, non-deterministic state machine trained via a high-throughput distributed training pipeline.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
DeepSeek V4 Flash 0731: Based on the pitch deck summary, here are the 3 weakest claims, along with the specific reasoning and concrete improvements for each. The "94% accuracy... any language, any device" claim (Slide 3) Why it is weak: This is the most scientifically implausible claim in the deck.
Gemma 4 26B A4B: This is a high-ambition pitch that falls into a common trap for deep-tech startups: the "Science Fiction Gap." While the vision is compelling, several claims lack the scientific and operational credibility required to justify a $15M Series A.
One month, three habits, someone starting from nothing.
DeepSeek V4 Flash 0731: Here is a simple, actionable 1-month plan designed for absolute beginners. The goal is consistency over intensity. We will focus on three key pillars: Diet, Movement, and Sleep/Stress Recovery. You don’t need to be perfect—just aim for 80% adherence. If you miss a day, don’t double up tomorrow; just get back on track.
Gemma 4 26B A4B: To avoid burnout, the secret to longevity is consistency over intensity. This plan follows a "progressive loading" model: you start with small wins in Week 1 and gradually build complexity. Here is your 1-month roadmap focusing on Nutrition, Movement, and Sleep.
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek V4 Flash 0731: The total training FLOPs for GPT-3 is approximately: ~3 × 10²³ FLOPs or about 300 zettaFLOPs (more specifically ~315 zettaFLOPs). Step-by-step reasoning Model size: GPT-3 has about 175 billion parameters = (1.75 \times 10^{11}). Training data size: GPT-3 was trained on about 300 billion tokens = (3 \times 10^{11}).
Gemma 4 26B A4B: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use a standard heuristic derived from the scaling laws of transformer models. Here is the step-by-step derivation.
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Not enough votes to call it. On the specs, DeepSeek V4 Flash 0731 has the edge: newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $0.14/M tokens | $0.13/M tokens |
| Output price | $0.28/M tokens | $0.4/M tokens |
| Context window | 1.0M tokens | 262K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | Jul 2026 | Apr 2026 |
| At 10M a month | $1.40 | $1.30 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
DeepSeek V4 Flash 0731 is developed by DeepSeek while Gemma 4 26B A4B is developed by Google AI. DeepSeek V4 Flash 0731 has a 1.0M token context window vs Gemma 4 26B A4B's 262K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. DeepSeek V4 Flash 0731 and Gemma 4 26B A4B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.
DeepSeek V4 Flash 0731 costs $0.14/M input tokens and Gemma 4 26B A4B costs $0.13/M input tokens. Gemma 4 26B A4B is $0.01/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 0731 and Gemma 4 26B A4B 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.