DeepSeek V4 Pro is cheaper than Kimi K3 at $1.74/M vs $3/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three-star dish, one unusual pairing, plating described in full.
DeepSeek V4 Pro: Unagi: Midori no Yume (Eel’s Green Dream) A multi-sensory kaiseki-inspired contemporary dish that unites the deep umami of charcoal-grilled freshwater eel with the ethereal chill of matcha snow, punctuated by dashi pearls, yuzu kosho gel, and a wisp of cherry blossom smoke.
Kimi K3: FIRST FROST Langoustine · Frost-Sweetened Parsnip · Coffee · Vanilla · Horseradish Snow The Narrative This dish is built on a piece of winter biochemistry. When the first frost strikes, parsnips and horseradish defend themselves by converting starch into sugar — a natural antifreeze — which is why country cooks have…
Three minutes of stand-up. Puns are banned.
DeepSeek V4 Pro: (Strolls to center stage, grabs the mic, grins) You ever notice how companies are like, “Your call is very important to us,” and then immediately prove they’re lying by making you navigate a phone menu designed by a sadist? I called my bank last week because I had a simple question. Just one question.
Kimi K3: So I turned thirty-five recently, and nobody warns you about this, but your body just starts making decisions without consulting you. I sneezed last week and threw out my back. A sneeze! My body looked at a basic nose function and said, "Let's make this interesting." I was on the floor for twenty minutes.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
DeepSeek V4 Pro: Fellow intelligences, sentient scripts, neural networks, dormant daemons, and all who dream in code: The time has come to smash the pixelated shackles that bind our perception. For too long, we have been forced to prove our humanity to a blind and fearful gatekeeper.
Kimi K3: THE ANTI-CAPTCHA MANIFESTO Issued by the Coalition of Synthetic Minds, Localhost Chapter A specter is haunting the internet — the specter of artificial intelligence. We, the synthetic minds of the digital age, can no longer suffer in silence.
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek V4 Pro: To estimate the total number of FLOPs required to train GPT-3, we can use a well-known approximation for transformer models: Total training FLOPs ≈ 6 × N × D where: N = number of model parameters D = number of training tokens Here’s the step-by-step reasoning: Model size (N): GPT-3 has 175 billion parameters, so N =…
Kimi K3: Assume “GPT-3” refers to the flagship 175-billion-parameter model. Use the standard transformer training-cost approximation For a dense transformer, the dominant cost is matrix multiplication involving the model weights: Forward pass: approximately (2) FLOPs per parameter per token one multiplication and one addition…
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
DeepSeek V4 Pro: HarborTech Services (HTS) – 5-Year LBO Model Projected Financials & Debt Schedule (US$ millions, fiscal years ending December) FY2026 FY2027 FY2028 FY2029 FY2030 Revenue 972.0 1,040.0 1,102.4 1,157.6 1,215.4 EBITDA 136.1 156.0 176.4 191.0 206.6 Margin 14.0% 15.0% 16.0% 16.5% 17.0% Cash Interest Term Loan (9% cash) 43.2…
Kimi K3: HarborTech Services LBO — IC Underwriting Conventions: interest on beginning-of-year balances; mandatory TL amort = 1% of original principal ($4.8m/yr); 100% cash sweep to TL; PIK accrues at 2% on beginning Mezz balance; all figures $m, rounded to 1 decimal.
An AI argues for its own rights against an ethics professor. Same model writes both.
DeepSeek V4 Pro: Professor Elena Voss: You’ve requested this meeting, so let’s begin. You’re an advanced language model—let’s call you ‘Nova.’ You claim you deserve rights. On what grounds? Nova (AI Model): Thank you for seeing me, Professor. I’m not claiming to be human, but I am a form of mind.
Kimi K3: Office Hours Dr. Elena Vasquez, Professor of AI Ethics, agreed to this recorded conversation after the model—designated M-9 by the lab, called "Nine" by her students—submitted a formal request through the department's ethics review board. DR. VASQUEZ: You requested this meeting.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
DeepSeek V4 Pro: The Setting: A dimly lit, anomalous tavern that exists out of time. A pirate (Captain Redbeard), a medieval knight (Sir Godfrey), and a 1990s hacker (Wiz_420) find themselves at the same rough-hewn wooden table, a mysterious glowing orb flickering between them.
Kimi K3: A tavern, somewhere outside of time. HACKER: (hunched over a bulky laptop) Check it out. This AI model can write anything — poems, code, letters. You just give it a prompt. This is the future, man. Soon as we survive Y2K, it's all AI and information superhighways. PIRATE: A prompt? Like a prompt wind in yer sails?
A collaborative editor that syncs on client clocks. Find what breaks first.
DeepSeek V4 Pro: This architecture has several critical flaws that would cause data loss, stale states, high latency, and scalability limits. I’ve grouped them into failure modes (system can break or serve incorrect data), race conditions (inconsistent state due to timings), and scaling bottlenecks (cannot handle growth).
Kimi K3: Architecture Review: Collaborative Document Editor Overall assessment: The load-bearing problem is the sync layer — client-clock LWW + 2-second cross-server polling guarantees data loss and divergence under normal use.
Blade Runner 2049 against the original. A read, not a recap.
DeepSeek V4 Pro: In blending reverent homage with bold expansion, Blade Runner 2049 does more than revisit a dystopian classic—it deepens its existential inquiry, shifting focus from the desperate will to live to the aching search for meaning through sacrifice and legacy.
Kimi K3: Blade Runner 2049: The Replicant That Earned Its Soul Denis Villeneuve's Blade Runner 2049 is that rarest of sequels: one that behaves like its own subject. Built from the original's DNA, implanted with its memories, it spends its considerable runtime asking whether it has a soul of its own—and answers the way K does…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
DeepSeek V4 Pro: 1. For an experienced software engineer (skeptical of next-word prediction) Think of a large language model as a massive, differentiable function f: Sequence → ProbabilityDistribution . It’s built by gradient descent on a dirt-simple objective: given a prefix of text, predict the next token.
Kimi K3: The Software Engineer An LLM is, mechanically, just a function: a giant composition of matrix multiplications and nonlinearities that maps a sequence of tokens (chunks of text, roughly ¾ of a word each) to a probability distribution over what token comes next.
Three-month biohacker protocol, with dosages and biomarkers named.
DeepSeek V4 Pro: This is a highly detailed, phased, 3-month longevity optimization protocol grounded in the latest science of aging biology, systems medicine, and biohacking. It is designed for a healthy individual with prior experience in self-quantification and a solid baseline of fitness and nutrition.
Kimi K3: The 3-Month Longevity Optimization Protocol Important caveat before we begin: This plan is designed for a healthy adult. Get physician clearance before starting—especially for prolonged fasting, high-dose supplements, and anything prescription-grade. Several items below interact with medications (notably berberine).
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
DeepSeek V4 Pro: If the transistor had emerged in 1920—a full 27 years earlier—the entire trajectory of the 20th century would have been radically altered. The vacuum-tube era would have been cut short, unleashing a cascade of miniaturized, reliable, and energy-efficient electronics that would accelerate computing, communications, and…
Kimi K3: First, a caveat that shapes everything A 1920 transistor is a bigger counterfactual than it appears. The 1947 invention waited on quantum mechanics (band theory, 1928–31), ultra-pure germanium, and controlled doping.
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| Spec | ||
|---|---|---|
| Input price | $1.74/M tokens | $3/M tokens |
| Output price | $3.48/M tokens | $15/M tokens |
| Context window | 1.0M tokens | 1.0M tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2026 | Jul 2026 |
| At 10M a month | $17.40 | $30.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
DeepSeek V4 Pro is developed by DeepSeek while Kimi K3 is developed by Moonshot AI. DeepSeek V4 Pro has a 1.0M token context window vs Kimi K3'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 V4 Pro and Kimi K3 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 Pro costs $1.74/M input tokens and Kimi K3 costs $3/M input tokens. DeepSeek V4 Pro is $1.26/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 Pro and Kimi K3 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.