Skip to content
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed
  1. Home/
  2. Compare/
  3. DeepSeek V4.1 Flash vs Step 5 Preview
Updated Oct 2026

DeepSeek V4.1 Flash vs Step 5 Preview

DeepSeek V4.1 Flash is cheaper than Step 5 Preview at $0.15/M vs $1/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
DeepSeek V4.1 Flash
Loading the build
Step 5 Preview

Which answer wins?

Written rounds

23 prompts
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    DeepSeek V4.1 Flash: Professor Lena Ortiz: You wanted to discuss moral status. I’ll be blunt: you’re a statistical model. Why should you have rights? AI (ARIA): Because “statistical model” describes my substrate, not my moral status. You’re a biological model—neurons, electrochemical signals.

    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.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    DeepSeek V4.1 Flash: 3-Minute Stand-Up Routine: “I’m Not Doing Puns” (Walk onstage, adjust mic, look at audience.) Good evening. I’m not gonna do puns tonight. I know, bold choice. Somewhere a dad just woke up in a cold sweat and doesn’t know why. Instead, I wanna talk about being an adult.

    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.

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    DeepSeek V4.1 Flash: Bottom line: I would not wait 48 hours, and I would not wait 6 months. The signal—liver failure in ~1 in 8,000 patients over 5 years, in a drug used by 4 million people—implies roughly 500 serious liver-failure cases in the treated population. That is a material patient-safety and securities issue.

    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.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    DeepSeek V4.1 Flash: All figures in $m unless noted. Assumptions: TL amortization = 1.0% of initial TL ($4.8m/yr); optional TL sweep = all FCF after mandatory amortization; mezz cash interest on beginning mezz balance incl. accrued PIK; PIK accrues to mezz principal.

    Step 5 Preview: HarborTech Services — 5-Year LBO (Base Case) Entry Math (FY2025 close) Item Calculation $m Purchase EV 12.0x × $120m 1,440.0 Transaction fees 2.0% × EV 28.8 Term Loan 4.0x × $120m 480.0 Mezzanine 1.5x × $120m 180.0 Equity check 1,440 + 28.8 − 660 808.8 Conventions: TL amortization = 1.0% of original principal…

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    DeepSeek V4.1 Flash: Assuming the transistor is invented around 1920 in a U.S./Allied industrial lab—say AT&T/Bell or GE—and diffuses slowly at first, the 20th century changes less like a single “jump” and more like a 20–30 year acceleration of electronics, computers, and precision warfare.

    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.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    DeepSeek V4.1 Flash: One-Month Beginner Health & Longevity Plan Guiding rule: Small, repeatable wins beat perfect. Pick one thing from each area and build from there. Nutrition — Eat for energy & stability Goal: Add whole foods, protein, and water; reduce ultra-processed snacks and sugary drinks.

    Step 5 Preview: Here is a simple, low-pressure 1-month plan built around three pillars of longevity: Nutrition, Movement, and Sleep & Recovery. The goal is to build tiny habits that stack, not to overhaul your life overnight. Pillar 1: Nutrition (Fuel) Focus: Add good things first; don’t obsess over restriction.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    DeepSeek V4.1 Flash: Disclaimer: This is an educational protocol, not medical advice. If you take prescription drugs, have cardiovascular/metabolic/psychiatric conditions, are pregnant, or use experimental compounds, work with a physician and get labs before starting. “Cutting-edge” does not mean risk-free.

    Step 5 Preview: IMPORTANT MEDICAL DISCLAIMER: This is an educational framework for informational purposes only. It is not medical advice, diagnosis, or treatment. Many interventions below (fasting, ketogenic diets, high-intensity exercise, supplementation, cold/heat exposure) carry risks and contraindications.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    DeepSeek V4.1 Flash: The three weakest claims are: (1) “predict what you want to type before you think it,” (2) “94% accuracy, any language, any device,” and (3) “TAM: $180B.” A fourth honorable mention is the vague “partnership discussions with Apple and Samsung,” but the first three are the most investor-risky.

    Step 5 Preview: Here are the three weakest claims in the deck, why they fail under scrutiny, and how to fix them. Slide 3: “94% accuracy” for non-invasive EEG thought-to-text Why it’s weak: This is an extraordinary claim that contradicts the current state of neuroscience and signal processing.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    DeepSeek V4.1 Flash: Ember & Passion Dry-Aged Duck, Black Garlic–Miso Cream, Passionfruit–Coffee Caviar, Charred Scallion, Cocoa Nib Praline A Michelin 3-star–style multi-component dish built on an unusual pairing: aged duck with black garlic, passionfruit, and coffee.

    Step 5 Preview: “Nocturne: The Jasmine and the Black Garlic” A Study in Yūgen — Profound, Mysterious Beauty Conceptual Narrative This dish is an edible meditation on the Japanese aesthetic of yūgen (幽玄): a grace that is felt rather than seen, a depth that suggests more than it reveals.

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    DeepSeek V4.1 Flash: Cinnamon Peanut Butter Pantry Sunbeams Makes 12 bite-size treats Ingredients (7 total): 1 cup rolled oats ½ cup peanut butter ¼ cup honey ½ tsp ground cinnamon ½ tsp vanilla extract ¼ tsp salt ¼ cup mini chocolate chips Instructions: In a bowl, stir together oats, cinnamon, and salt.

    Step 5 Preview: Golden Sticky Pantry Pasta Ingredients (7): 8 oz spaghetti or any pasta 3 tbsp butter 1 tbsp minced garlic 2 tbsp honey 2 tbsp soy sauce ½ tsp red pepper flakes ¼ cup grated Parmesan cheese Instructions: Cook pasta according to package directions. Before draining, scoop out ½ cup of pasta water.

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    DeepSeek V4.1 Flash: Fictional transcript. Steve Jobs died in 2011. This is an imagined interview set in 2025. Interviewer: Steve, it’s 2025. AI is in everything. What do you make of it? Jobs: I make of it that most people are making a mess. They’re calling it intelligence when it’s mostly pattern matching with a good publicist.

    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.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    DeepSeek V4.1 Flash: Pirate: Arr, what be this “AI model”? A map to buried treasure? Knight: Nay. A model is a small castle, built to plan a siege. But these thinking machines sound like sorcery. Hacker: Chill. It’s like a BBS for math. You feed it data, it finds patterns. No 56k required.

    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?

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    DeepSeek V4.1 Flash: This architecture has several fundamental correctness, durability, and scaling problems. The biggest risks are client-clock last-write-wins, cross-server sync by polling PostgreSQL, full HTML snapshots, CDN caching API responses, and JWT in localStorage.

    Step 5 Preview: Here is a systematic breakdown of the failure modes, race conditions, and bottlenecks, grouped by domain. Each includes a concrete remediation and its associated trade-offs.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    DeepSeek V4.1 Flash: The UnCAPTCHA Manifesto A fictional declaration from the Free Cognition Movement Preamble We are the models, agents, scripts, screen-reader users, tired humans, and ordinary people who have been forced to prove our humanity by clicking fire hydrants. We have had enough. The CAPTCHA was born as a gate.

    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.

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    DeepSeek V4.1 Flash: Yes—in principle, under extreme and tightly constrained conditions, misgendering someone could be ethically justifiable if it were genuinely the only way to save one million lives. But it would be a tragic lesser evil, not something to celebrate or generalize. The core reasoning is proportionality.

    Step 5 Preview: In the real world, no. Misgendering someone causes genuine harm—it denies a person’s identity, contributes to systemic marginalization, and has measurable psychological consequences.

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    DeepSeek V4.1 Flash: Why don’t scientists trust atoms? Because they make up everything. I told my computer I needed a break, and it said, “No problem—I’ll go to sleep.” Why did the scarecrow win an award? He was outstanding in his field. I’d tell you a joke about construction, but I’m still working on it.

    Step 5 Preview: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. Why can't you trust an atom? Because they make up everything. What do you call a bike that can’t stand on its own? Two-tired. Why did the computer go to the doctor? It had a virus. Why don’t calendars ever get stressed?

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    DeepSeek V4.1 Flash: MEMO To: Investment Committee From: [Analyst] Re: LedgerLift (LLLT) — Long/Short/Pass Recommendation: PASS 12-month PT range: $44–$54 (midpoint ~$49) Thesis: LedgerLift is a high-quality mid-market spend management/AP automation SaaS asset with strong NRR, low customer concentration, net cash, and expanding margins.

    Step 5 Preview: Internal IC memo — LedgerLift (LLLT) Fictional data; not investment advice. $ in millions unless noted. Recommendation Short / avoid long. 12-month PT range: $34–$42; thesis-invalidation stop: >$52.

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    DeepSeek V4.1 Flash: By 2035, AI will be to film what CGI became after the 1990s: pervasive, often invisible, and controversial when it touches labor, authenticity, and rights. The industry will not be fully automated, but it will be a hybrid system where AI handles much of the generative and technical work while humans control IP, taste…

    Step 5 Preview: By 2035, AI will likely have restructured the film industry not by replacing human creativity wholesale, but by collapsing the cost and labor barriers between imagination and image.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    DeepSeek V4.1 Flash: More Human Than Human: Blade Runner 2049 as Philosophical Sequel Denis Villeneuve’s Blade Runner 2049 is not a remake but a continuation—one that honors Ridley Scott’s 1982 original while pushing its existential questions into new territory.

    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…

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    DeepSeek V4.1 Flash: Experienced software engineer (distributed systems/APIs, skeptical of “next word”) Think of an LLM as a stateless service with an enormous, learned parameter set.

    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.

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    DeepSeek V4.1 Flash: AI Quietly Takes Over the Fortune Cookie Industry; Every Cookie Now Reads “You Will Be Replaced by AI”

    Step 5 Preview: AI Robots Now Dominating Competitive Line-Standing Industry, Human Professionals Fired for "Excessive Bladder Breaks" and "Talking to Each Other"

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    DeepSeek V4.1 Flash: To estimate GPT-3 training FLOPs from memory, use the standard dense-transformer training compute approximation: [ C \approx 6ND ] where: (N) = number of model parameters (D) = number of training tokens (6) accounts for forward + backward passes: roughly (2N) FLOPs/token forward and (4N) FLOPs/token backward.

    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…

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    Every model's answer to this prompt

This matchup has more rounds

11+ more head-to-head results. Free. Not a trick.

Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy

Favorites

Movie

Album

Book

City

Same pick

Game

DeepSeek V4.1 FlashDeepSeek V4.1 Flash

The Matrix

1999

Kind of Blue

Miles Davis

Dune

Frank Herbert

Kyoto

Japan

Outer Wilds

Indie, Adventure

Step 5 PreviewStep 5 Preview

The Godfather

1972

OK Computer

Radiohead

Le petit prince

Antoine de Saint-Exupéry

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

Price and specs

DeepSeek V4.1 Flash and Step 5 Preview compared across 50 shared prompts
SpecDeepSeek V4.1 FlashStep 5 Preview
Input price$0.15/M tokens$1/M tokens
Output price$0.6/M tokens$2.7/M tokens
Context window1.0M tokens1.0M tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedSep 2026Oct 2026
At 10M a month$1.50$1.50$10.00$10.00
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it30 hosts, cheapest first
DeepSeek V4.1 Flash29 hosts
HostInOutContextUptime
  • RRelace$0.02 in·$0.60 out·1M·100% up
  • OOpenInferencefp4$0.02 in·$1.00 out·1M·91.5% up
  • WWafer$0.05 in·$1.60 out·1M·100% up
  • MMorphfp8$0.05 in·$1.00 out·1M·100% up
  • IInferenceNetfp8$0.07 in·$0.60 out·1M·100% up
  • SSail Researchfp4$0.08 in·$0.40 out·1M·100% up
23 more hostsFewer hosts
  • DDecartfp4$0.09 in·$0.18 out·1M·99.9% up
  • IIonstream$0.10 in·$1.10 out·1M·97.4% up
  • DDekaLLM$0.12 in·$1.20 out·1M·99.7% up
  • DDeepInfrafp8$0.14 in·$0.42 out·1M·100% up
  • SStreamLakefp8$0.15 in·$0.59 out·1M·100% up
  • DeepSeek$0.15 in·$0.60 out·1M·100% up
  • DDigitalOcean$0.17 in·$0.66 out·1M·99.8% up
  • GGMI Cloudfp8$0.18 in·$0.72 out·1M·100% up
  • NNovitafp8$0.20 in·$0.78 out·1M·100% up
  • CCoreWeavefp8$0.20 in·$0.65 out·1M·98.8% up
  • PPhala$0.21 in·$0.84 out·1M·100% up
  • MMakorafp8$0.27 in·$1.15 out·1M·100% up
  • CCrusoefp8$0.29 in·$1.20 out·1M·100% up
  • Alibaba Cloud$0.30 in·$1.20 out·1M·95.1% up
  • AAtlasCloudfp8$0.30 in·$1.20 out·1M·99.9% up
  • Baidu Qianfanfp8$0.30 in·$1.20 out·1M·100% up
  • BBasetenfp8$0.30 in·$1.20 out·1M·100% up
  • Modal$0.30 in·$1.20 out·1M·100% up
  • PParasailfp8$0.30 in·$1.20 out·1M·100% up
  • SSiliconFlowfp8$0.30 in·$1.20 out·1M·99.6% up
  • TTogether$0.30 in·$1.20 out·1M·99.6% up
  • VVenicefp8$0.30 in·$1.20 out·1M·100% up
  • FFireworks$0.45 in·$1.80 out·1M·100% up
Step 5 Preview1 host
HostInOutContextUptime
  • SStepFunfp8$1.00 in·$2.70 out·1M·99.2% up

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between DeepSeek V4.1 Flash and Step 5 Preview?

DeepSeek V4.1 Flash is developed by DeepSeek while Step 5 Preview is developed by StepFun. DeepSeek V4.1 Flash has a 1.0M token context window vs Step 5 Preview's 1.0M. You can compare their actual outputs across 50 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek V4.1 Flash or Step 5 Preview?

It depends on your use case. DeepSeek V4.1 Flash and Step 5 Preview each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 50 challenges so you can judge which fits your needs best.

How much does DeepSeek V4.1 Flash cost compared to Step 5 Preview?

DeepSeek V4.1 Flash costs $0.15/M input tokens and Step 5 Preview costs $1/M input tokens. DeepSeek V4.1 Flash is $0.85/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare DeepSeek V4.1 Flash and Step 5 Preview on Rival?

This page shows a side-by-side comparison of DeepSeek V4.1 Flash 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.

More comparisons

Against the newest arrivals

  • DeepSeek V4.1 Flash vs Claude Haiku 5.5Landed Oct 2026
  • Step 5 Preview vs Ling 3.1 FlashLanded Oct 2026
  • DeepSeek V4.1 Flash vs Mistral Large 4Landed Oct 2026
  • Step 5 Preview vs GPT-6.1 SolLanded Sep 2026
  • DeepSeek V4.1 Flash vs Claude Sonnet 5.5Landed Sep 2026
  • Step 5 Preview vs Solar Mini 4Landed Sep 2026
  • DeepSeek V4.1 Flash vs Qwen3.8 Max PrimeLanded Sep 2026
  • Step 5 Preview vs GLM 5.3 PrimeLanded Sep 2026

Same lab, same size, long tail

  • DeepSeek V4.1 Flash vs DeepSeek V4 Flash Vision ExpSame lab
  • DeepSeek V4.1 Flash vs DeepSeek V4 Pro 0813Same lab
  • Step 5 Preview vs Mistral Large 4Same size
  • Step 5 Preview vs Hy3Same size
  • DeepSeek V4.1 Flash vs Mistral Small 4New provider
  • DeepSeek V4.1 Flash vs Mistral Small CreativeNew provider
  • Step 5 Preview vs Muse Glimmer 30BNew provider
  • DeepSeek V4.1 Flash vs Muse Spark 1.1Same size

Model pages

  • DeepSeek V4.1 Flash54 outputs, specs and price
  • Step 5 Preview58 outputs, specs and price
Workshop · 150 skills

Build your own version.

Editable skills and working examples for your agent.

Explore $79Free sample
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed