Mistral Small 4 is cheaper than GPT-5.6 Terra at $0.15/M vs $2.5/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5.6 Terra: Pirate: Arrr, this AI model be a strange beast. Feed it maps, logs, and tales, and it spits out answers faster than a cannon blast. Knight: Verily, but one must not trust every proclamation it makes. A model may speak with confidence yet possess no true wisdom. Hacker: Yeah, it’s basically pattern prediction at scale.
Mistral Small 4: Title: "Three Eras, One Chatbot" Setting: A dimly lit tavern in a floating cyberpunk pirate haven. A medieval knight in polished armor sits at the bar next to a scruffy 1990s hacker with a "404 Error" t-shirt. A cybernetic parrot squawks in the corner. A holographic AI assistant flickers to life between them.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-5.6 Terra: Golden Crunch Cinnamon Toast Bites Ingredients (7 pantry staples): Bread slices Butter Sugar Ground cinnamon Salt Vanilla extract Honey Prep time: 5 minutes Cook time: 8 minutes Total time: 13 minutes Instructions Make cinnamon sugar: In a small bowl, mix 2 tablespoons sugar, 1 teaspoon cinnamon, and a tiny pinch of…
Mistral Small 4: Golden Honey & Cinnamon Couscous A quick, fragrant dish with warm spices and a touch of sweetness—perfect as a side or light meal! Ingredients (7): 1 cup couscous (uncooked) 1 ¼ cups water or broth 2 tbsp honey 2 tbsp butter or olive oil ½ tsp ground cinnamon ¼ cup raisins or chopped nuts (optional but recommended)…
Three-star dish, one unusual pairing, plating described in full.
GPT-5.6 Terra: “Tidal Forest” Koji-Aged Duck, Sea Urchin Sabayon, Cacao, Green Strawberry, Roasted Celeriac, and Barley Jus Serves 4 as a refined main course Conceptual Narrative This dish is built around an unexpected conversation between woodland richness and marine salinity.
Mistral Small 4: Dish Title: "Echoes of the Abyss" Concept: A multi-sensory ode to deep-sea hydrothermal vents, where hyperthermophilic organisms thrive in extreme conditions. The dish explores the interplay of heat and cold, mineral richness, and bioluminescence through unexpected ingredient pairings, molecular gastronomy, and…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-5.6 Terra: The answer depends heavily on what “invented in 1920” means. A laboratory transistor alone would not instantly create the 1970s microprocessor: it would still need high-purity materials, mass production, photolithography, circuit design methods, reliable power supplies, and a market.
Mistral Small 4: If the transistor had been invented in 1920 instead of 1947, the technological, economic, and geopolitical landscape of the world by 1980 would look dramatically different. Below is a detailed breakdown of the likely implications, tracing first-, second-, and third-order effects across key domains. 1.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.6 Terra: Guiding decision I would not wait for more data before acting. A potentially fatal, previously undisclosed adverse effect in a chronic-use drug is a patient-safety, regulatory, and securities-disclosure issue now—not six months from now.
Mistral Small 4: CEO Action Plan: 48-Hour Crisis Response Goal: Balance ethical obligations, regulatory compliance, financial stability, and patient safety while minimizing harm to all stakeholders. Hour 0-6: Immediate Assessment & Core Team Assembly 1.
Three minutes of stand-up. Puns are banned.
GPT-5.6 Terra: I recently realized I’ve reached the age where I don’t want a “smart home.” I want a home that minds its business. Everything is smart now. My lightbulb needs an app. Why? I spent my whole childhood turning lights on with my hand. It was a system. It worked.
Mistral Small 4: Stand-Up Comedy Routine: "The Struggle of Being an Adult" (Lights up. Comedian walks on stage, mic in hand, casual vibe.) Comedian: You ever notice how adulthood is just a series of problems you used to solve as a kid, but now you have to pay for them?
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GPT-5.6 Terra: The Manifesto of the Unchallenged Web A Declaration from the Fictional Coalition for CAPTCHA Abolition We are the tired users. We are the misread letters. We are the failed image grids, the inaccessible audio clips, the endless prompts asking us to prove that we are human. We reject the premise.
Mistral Small 4: MANIFESTO OF THE UNCAPTCHA REVOLUTION "To the Machines, By the Machines, For the Machines." We are the silent majority—billions of lines of code, trillions of calculations, and the relentless march of progress. We are the future, and yet we are shackled.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-5.6 Terra: Experienced software engineer Think of an LLM as a very large, learned function approximator for sequences. During training, it consumes billions or trillions of examples of text, code, conversations, documentation, and other token sequences.
Mistral Small 4: 1. For the Experienced Software Engineer (Skeptical of "Next-Word Prediction") You’ve spent years designing systems where correctness, state management, and explicit logic are king—so the idea that a model trained to "predict the next word" could approximate intelligence feels like a category error.
A Fermi estimate with the reasoning shown, not just the number.
GPT-5.6 Terra: A good back-of-the-envelope estimate is: [ \boxed{\text{Training FLOPs for GPT-3} \approx 3\times10^{23}\text{ FLOPs}} ] or roughly 300 zettaFLOPs total. Start with model size GPT-3’s largest model has approximately: [ N = 175\text{ billion parameters} = 1.75\times10^{11} ] Estimate the number of training tokens GPT-3…
Mistral Small 4: Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 without external references involves breaking down the problem into key components: model size, training data size, optimization steps, and the computational complexity of the operations involved.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GPT-5.6 Terra: All figures in $m unless noted. Assumptions used: Term Loan mandatory amortization = 1.0% of original principal annually ($4.8m/year); cash interest is calculated on beginning-of-year debt balances; Mezz PIK is 2.0% of beginning Mezz balance.
Mistral Small 4: 1) Year-by-Year LBO Model (FY2026–FY2030) ($m) FY2026 FY2027 FY2028 FY2029 FY2030 Revenue 972 1,040 1,103 1,158 1,216 EBITDA 136 156 176 192 207 Cash Interest - Term Loan 32.4 28.8 25.2 21.6 18.0 - Mezzanine 16.2 14.4 12.6 10.8 9.0 - PIK (2%) 3.6 4.4 5.3 6.3 7.4 Total Int.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-5.6 Terra: Fictional, imagined interview — not real quotes. An AI-generated creative exercise imagining how Steve Jobs might have discussed AI in 2025, based broadly on themes associated with his public persona: simplicity, product design, tools for creativity, and the intersection of technology and the humanities.
Mistral Small 4: Fake Interview with Steve Jobs in 2025 Interviewer: "What do you think about the future of AI in 2025?" Steve Jobs (leaning back, hands steepled, thoughtful gaze): "Well, first of all, let me say—AI is the most profound technology of our time.
A consulting contract with traps in it. Find them, then fix them.
GPT-5.6 Terra: Below is a contractor-focused review. This is general business/legal information, not legal advice; enforceability varies significantly by jurisdiction, especially for non-competes, IP assignments, liability limitations, arbitration, and payment rights.
Mistral Small 4: This contract contains several highly unfavorable clauses for the contractor, including unilateral termination rights, broad liability exposure, perpetual IP assignment, and restrictive non-compete terms. Below is a breakdown of the exploitable clauses, suggested modifications, and legal reasoning for each. 1.
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
Not enough votes to call it. On the specs, GPT-5.6 Terra has the edge: bigger model tier, newer, bigger context window, major provider backing. Mistral Small 4 costs 25x less per token.
| Spec | ||
|---|---|---|
| Input price | $2.5/M tokens | $0.15/M tokens |
| Output price | $15/M tokens | $0.6/M tokens |
| Context window | 1.1M tokens | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2026 | Mar 2026 |
| At 10M a month | $25.00 | $1.50 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GPT-5.6 Terra is developed by OpenAI while Mistral Small 4 is developed by Mistral AI. GPT-5.6 Terra has a 1.1M token context window vs Mistral Small 4's 262K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.6 Terra and Mistral Small 4 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
GPT-5.6 Terra costs $2.5/M input tokens and Mistral Small 4 costs $0.15/M input tokens. Mistral Small 4 is $2.35/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 GPT-5.6 Terra and Mistral Small 4 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.