Kimi K3 has a larger context window than Qwen3 235B A22B (1.0M tokens vs 33K 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.
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…
Qwen3 235B A22B: Recipe Name: Lunaria: A Moonlit Garden of the Sea and Soil Conceptual Narrative: Inspired by the ethereal contrast between moonlit tides and nocturnal gardens, this dish marries the briny sweetness of the ocean with the delicate florals of chamomile and the umami depth of fermented earth.
A Fermi estimate with the reasoning shown, not just the number.
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…
Qwen3 235B A22B: Step-by-Step Estimation of FLOPs Required to Train GPT-3 Determine the Number of Parameters (N): GPT-3 is known to have 175 billion parameters (1.75 × 10¹¹). This is a key factor in computing the total FLOPs, as the number of operations scales with the number of parameters.
A consulting contract with traps in it. Find them, then fix them.
Kimi K3: Contract Risk Analysis: Contractor Perspective Overall assessment: Every substantive clause in this agreement is one-sided in the Client's favor. Several are likely unenforceable as written, but enforceability only matters after expensive litigation — the practical risk is that these clauses give the Client leverage to…
Qwen3 235B A22B: Here is a structured analysis of problematic clauses in the agreement, along with suggested modifications and legal reasoning: 1. SCOPE CLAUSE (Clause 1) Issue: Client can unilaterally modify scope without additional compensation, creating financial risk and potential exploitation via "scope creep."
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
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 $m, rounded to 1 decimal.
Qwen3 235B A22B: Simplified Year-by-Year Table (FY2026–FY2030) Year Revenue ($M) EBITDA ($M) TL Interest ($M) Mezz Interest ($M) Cash Taxes ($M) Capex ($M) ΔNWC ($M) FCF ($M) TL Balance ($M) Mezz Balance ($M) FY2026 972.0 136.1 43.2 21.6 17.8 29.2 0.4 23.9 456.1 183.6 FY2027 1,040.0 156.0 41.0 22.0 23.2 31.2 0.3 38.2 417.9 187.3 FY2028…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
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.
Qwen3 235B A22B: To the Software Engineer (Skeptical ML Novice): Think of a large language model (LLM) as a distributed system for semantic routing. At its core, the model is a massive lookup table trained to map input sequences (tokens) to output sequences, but the magic lies in its architecture.
Three minutes of stand-up. Puns are banned.
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.
Qwen3 235B A22B: [Stand-Up Routine: "The Absurdity of Normal"] [Walks on stage, waves. Mimics checking phone immediately.] Hey everyone! Great to be here—assuming this is real and not just another Zoom waiting room I accidentally joined. [Pause.]
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
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?
Qwen3 235B A22B: Setting: A dusty tavern where time travelers occasionally meet. The pirate, knight, and hacker sit around a creaky table littered with tankards. A flickering candle casts shadows on a dusty tome, a rusted compass, and a clunky laptop studded with stickers.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Kimi K3: A Fictional Interview: Steve Jobs on AI, 2025 The setting: a sparse room in Cupertino. Black turtleneck. Faded jeans. A cup of tea he doesn't touch. He studies the interviewer for a long moment before the first question. Interviewer: Steve, thanks for sitting down with us. Let's start broad. It's 2025.
Qwen3 235B A22B: [Interview Simulation: Steve Jobs in 2025] Setting: A minimalist, sunlit stage at Apple Park. Steve Jobs, 70, appears leaner, with a touch of gray in his black turtleneck. The audience leans forward, captivated. The host, a tech journalist, begins. Host: Mr. Jobs, it’s an honor. In 2025, AI is everywhere.
A collaborative editor that syncs on client clocks. Find what breaks first.
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.
Qwen3 235B A22B: Potential Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket State Isolation Across Servers Failure Mode: Users connected to different API servers do not receive real-time updates from other servers until the 2-second poll interval.
Satire, one line long, about AI taking over an industry nobody saw coming.
Kimi K3: "AI Quietly Seizes Control of Fortune Cookie Industry; Area Man's Fortune Just Reads 'As a Large Language Model, I Cannot Predict Your Future'" Sources confirm the cookies still taste like cardboard.
Qwen3 235B A22B: Headline: "AI Replaces Human Mourners in Funeral Industry: Algorithms Now Paid to Wail at Funerals, Leaving Professional Weepers 'Heartbroken' and Unemployed" Subheadline (optional for extra flair): "Deep Learning Models Master the Art of Grief, Offering Customizable 'Sorrow Packages' from 'Mild Sniffles' to 'Full-Body…
Film in 2035, with synthetic actors. Specific beats plausible.
Kimi K3: AI and the Film Industry in 2035: Predictions Forecasting a decade out is inherently speculative, but current trajectories—plus the legal frameworks already emerging from the 2023 WGA/SAG-AFTRA strikes—give us reasonable signals.
Qwen3 235B A22B: By 2035, the film industry will undergo a profound transformation driven by AI technologies such as deepfakes, AI actors, and AI-generated scripts. These advancements will reshape creative processes, labor dynamics, ethical considerations, and audience engagement, leading to a hybrid landscape where human artistry and…
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
Kimi K3: IC MEMO — LedgerLift (LLLT) Price $46 Mkt cap $8.74B Net cash $1.4B EV $7.34B EV/FY25 Rev 9.0x EV/FY26E Rev 7.4x EV/FY26E EBIT 37x Recommendation: SHORT (moderate conviction; 1.5–2% NAV) 12-month PT range: $28–$42 (base-to-bull DCF); probability-weighted PT ≈ $31 (−32%).
Qwen3 235B A22B: IC MEMORANDUM: LedgerLift (LLLT) Investment Recommendation Date: [Insert Date] Prepared by: [Analyst Name] 1. Recommendation Recommendation: Short 12-Month Price Target Range: $30–$40 Thesis: LLLT has a high-quality SaaS platform with robust customer retention and net revenue retention (NRR), but its valuation (~9x NTM…
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Not enough votes to call it. On the specs, Kimi K3 has the edge: newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $3/M tokens | — |
| Output price | $15/M tokens | — |
| Context window | 1.0M tokens | 33K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2026 | Apr 2025 |
| At 10M a month | $30.00 | not listed |
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.
Kimi K3 is developed by Moonshot AI while Qwen3 235B A22B is developed by Qwen. Kimi K3 has a 1.0M token context window vs Qwen3 235B A22B's 33K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Kimi K3 and Qwen3 235B A22B 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.
This page shows a side-by-side comparison of Kimi K3 and Qwen3 235B A22B 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.