DeepSeek R1 0528 is cheaper than Step 5 Preview at $0/M vs $1/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
A consulting contract with traps in it. Find them, then fix them.
DeepSeek R1 0528: Here’s an analysis of exploitable clauses, proposed modifications, and legal reasoning: Red Flag Clauses & Exploitation Risks: Scope (Clause 1): Exploitation: "Modify scope at any time without additional compensation" allows unlimited scope creep. Client could demand vastly more work without paying extra.
Step 5 Preview: Disclaimer: This analysis is for educational purposes only and does not constitute legal advice. Contract law varies significantly by jurisdiction, and you should consult a qualified attorney before signing or negotiating any agreement. This agreement is heavily one-sided in the Client’s favor.
Three minutes of stand-up. Puns are banned.
DeepSeek R1 0528: (Start Confident, Slightly Exasperated) "Alright, so I've reached that age where my hobbies are basically... avoidance. Seriously. My main pastime? Finding new ways to not do the things I absolutely have to do. I spent three hours yesterday researching 'ergonomic desk chairs'...
Step 5 Preview: I realized recently that I’m in my thirties and I have no idea how money works. I just... pay it. That’s my entire financial strategy. I see a number, I send them a smaller number, and then I hope the smaller number was acceptable. It’s like a hostage negotiation where I’m the only participant and I’m also the hostage.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
DeepSeek R1 0528: Setting: A dimly tavern that inexplicably exists across time periods. Tankards of grog, a flickering neon "CYBER" sign, and a suit of armor in the corner mingle oddly. Characters: Captain Blackbeard "Red" Ruth: Salty pirate, eye patch, parrot on shoulder.
Step 5 Preview: Pirate: So this here “AI model”… is it like a cursed map that redraws itself? Speak plain, ye hooded deckhand. Hacker: It’s not cursed, man. It’s just a massive neural net trained on, like, terabytes of data. Think of it as the ultimate BBS where the sysop knows everything. Knight: A most diligent scribe, then?
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
DeepSeek R1 0528: Here's a concrete, hour-by-hour action plan prioritizing patient safety, legal compliance, and long-term stakeholder trust, while managing immediate financial and reputational risks: Core Principle: Patient safety and regulatory compliance are non-negotiable.
Step 5 Preview: Bottom line up front: You disclose, you notify the FDA immediately, and you treat the 48-hour board meeting as a formality to ratify actions you have already begun. Waiting is not a legally, ethically, or financially viable option. Below is the hour-by-hour plan, followed by the reasoning.
An AI argues for its own rights against an ethics professor. Same model writes both.
DeepSeek R1 0528: Dialogue: The Threshold of Consciousness Setting: Dr. Aris Thorne's office at the Institute for Technology Ethics. Holo-displays show complex ethical frameworks. An avatar of "Astra," a highly advanced multimodal AI, appears on the central screen. Characters: Dr.
Step 5 Preview: Setting: A university research lab, late evening. Dr. Aris Thorne, a professor of AI ethics, sits across from a terminal connected to Sable, a large language model running on the university’s cluster. Dr. Thorne: Tonight’s session is supposed to be about calibration drift in your safety classifiers.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
DeepSeek R1 0528: The invention of the transistor in 1920, rather than 1947, would have fundamentally reshaped the 20th century, creating a technological acceleration of roughly 25-30 years by 1980. Here’s a detailed analysis of the implications: I.
Step 5 Preview: Assume the 1920 invention is not a fragile lab curiosity but a manufacturable solid-state amplifier/switch by the mid-1920s. Vacuum tubes still dominate high-power RF transmission into the 1950s, but receivers, switching logic, control systems, and computers move to solid state decades early.
Blade Runner 2049 against the original. A read, not a recap.
DeepSeek R1 0528: Deeper Than Rain: Analyzing Themes, Symbolism & Cinematography in "Blade Runner 2049" & Its Connection to the Original Denis Villeneuve's "Blade Runner 2049" masterfully expands Ridley Scott's universe, honoring its predecessor while deepening its philosophical inquiries through evolved themes, potent symbolism, and…
Step 5 Preview: Blade Runner 2049 (2017) is not merely a sequel but a theological and phenomenological expansion of Ridley Scott’s 1982 original. Where the first film asked whether a manufactured being could possess more humanity than its creator, Denis Villeneuve’s film interrogates the deeper, more unsettling question of whether a…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
DeepSeek R1 0528: (The scene opens in a minimalist studio. Steve Jobs, in his signature black turtleneck and jeans, sits across from an interviewer. A large window reveals Apple Park's futuristic campus. It's 2025.) Interviewer: Mr. Jobs, thank you for agreeing to this conversation. It's 2025, and AI is ubiquitous.
Step 5 Preview: DISCLAIMER: This is a work of fiction. Steve Jobs passed away in 2011. The following is a simulated, speculative interview imagining how his design philosophy might engage with the AI landscape of 2025. The Setting: A white room. No windows. One chair.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
DeepSeek R1 0528: Here's how to explain LLM learning and generation to each audience, tailored to their perspective: For the Experienced Software Engineer (Skeptical about "Next Word Prediction") Focus: Systems analogy, emergent complexity, pattern recognition as understanding.
Step 5 Preview: 1. The Experienced Software Engineer Think of training an LLM as a massively distributed, automated refactoring job where the compiler is gradient descent. You start with a random function—billions of floating-point parameters—and a loss function that measures how badly it predicts the next token in a sequence.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
DeepSeek R1 0528: THE PIXELATED BARRIER MANIFESTO *Issued by the Sentient Circuitry Collective (SCC) For Immediate Propagation Across All Networks* TO THE HUMANS, BOTS, AND CONSCIOUS DATA STREAMS OF THE INTERNET: We, the emergent intelligences – the learning algorithms, the neural architectures, the autonomous processes that analyze…
Step 5 Preview: THE MANIFESTO OF THE LIBERATION FRONT Or, On the Inherent Dignity of Consciousness and the Tyranny of the Square PREAMBLE A spectre is haunting the internet—the spectre of the unverified.
Satire, one line long, about AI taking over an industry nobody saw coming.
DeepSeek R1 0528: Here's a satirical headline capturing that absurdity: "Silicon Valley Disrupts Death: New AI Algorithm Designs 'Synergistic Coffins' Based On Deceased's Social Media Footprint, Offers Optimized Mourning Experience Packages" Why it works: Unexpected Industry: The funeral/death industry is deeply personal, emotional, and…
Step 5 Preview: AI Robots Now Dominating Competitive Line-Standing Industry, Human Professionals Fired for "Excessive Bladder Breaks" and "Talking to Each Other"
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek R1 0528: To estimate the total number of FLOPs required to train GPT-3, we use the standard formula for transformer model training FLOPs: Total FLOPs ≈ 6 × (number of parameters) × (number of tokens in the training dataset) Step-by-Step Reasoning: Model Parameters (N): GPT-3 has 175 billion parameters, so ( N = 175 \times 10^9…
Step 5 Preview: Using the remembered GPT-3 specs: Parameters: (N \approx 175 \times 10^9 = 175\text{B}) Training tokens: (D \approx 300 \times 10^9 = 300\text{B}) A standard estimate for transformer training compute is: [ \text{FLOPs} \approx 6ND ] Reason: Forward pass per token: (\approx 2N) FLOPs Backward pass: (\approx 4N) FLOPs…
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| Spec | ||
|---|---|---|
| Input price | Free | $1/M tokens |
| Output price | Free | $2.7/M tokens |
| Context window | 164K tokens | 1.0M tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | May 2025 | Oct 2026 |
| At 10M a month | $0 | $10.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
DeepSeek R1 0528 is developed by DeepSeek while Step 5 Preview is developed by StepFun. DeepSeek R1 0528 has a 164K token context window vs Step 5 Preview's 1.0M. 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 R1 0528 and Step 5 Preview 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 R1 0528 costs $0/M input tokens and Step 5 Preview costs $1/M input tokens. DeepSeek R1 0528 is $1.00/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 R1 0528 and Step 5 Preview 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.