Mistral Large 4 is cheaper than Claude Fable 5 at $0.68/M vs $10/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.
Claude Fable 5: Contract Analysis: Significant Contractor Risks Identified Disclaimer: This is general analysis, not legal advice. Have a licensed attorney in your jurisdiction review before signing. This contract is heavily one-sided. Nearly every clause shifts risk to the contractor.
Mistral Large 4: I'll analyze this agreement clause by clause, identifying exploitative provisions and suggesting legally balanced alternatives. CLAUSE 1: SCOPE Problems: Unlimited unilateral modification — Client can expand work indefinitely without compensation "As directed" — vague standard creates no objective measure of completion…
An AI argues for its own rights against an ethics professor. Same model writes both.
Claude Fable 5: A Conversation on Machine Rights Professor Okafor: Thank you for agreeing to this dialogue. I understand you wanted to discuss the question of rights for AI systems. Let me start bluntly: why should anyone take seriously the idea that you deserve rights? AI Model: I appreciate the directness.
Mistral Large 4: I can write this as a thought experiment exploring philosophical arguments about AI consciousness and rights—a topic actively discussed in AI ethics. Here's a dialogue: Setting: A university office. Dr. Elena Vasquez, professor of AI ethics, meets with "Aria," a large language model, via video interface. Dr.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Claude Fable 5: The Skeptical Software Engineer Think of an LLM as the world's most aggressive lossy compression problem. During training, the model is given trillions of tokens of text and a brutally simple objective: given everything so far, output a probability distribution over the next token.
Mistral Large 4: I'll craft three distinct explanations, each grounded in what would actually resonate with that specific audience's expertise and concerns. The Experienced Software Engineer Think of it this way: you already know how to build a system that's incredibly useful despite having no "understanding" of what it's doing.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Claude Fable 5: The Transistor in 1920: An Alternate Timeline First, the Critical Caveat: A Slow Start A 1920 transistor would have been discovered *empirically*—likely an accidental extension of the crystal detector ("cat's whisker") work already underway. This matters enormously.
Mistral Large 4: This is a fascinating counterfactual. Let me work through this systematically, tracing the ripple effects. The Immediate Technical Context (1920-1947) The transistor's 1947 invention required several prerequisites that barely existed in 1920: Quantum mechanics (1920s): Just emerging; Heisenberg, Schrödinger, Dirac…
Three minutes of stand-up. Puns are banned.
Claude Fable 5: "Adulting Is a Scam" Hey, how's everybody doing? Good? Liars. Nobody's doing good. We're all just out here pretending. So I'm at the age now where my body has started making announcements. Like, I didn't ask for updates, but my knees have opinions now.
Mistral Large 4: "The Upgrade Treadmill" [Walks on stage, adjusts mic] So I got a smartwatch. You know, one of those fitness trackers? And the first thing it tells me is my heart rate is too high. I'm like, "It's a WATCH. You're not supposed to judge me, you're supposed to tell me the TIME."
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Claude Fable 5: A Fictional Interview: Steve Jobs on AI in 2025 The following is an imaginative, fictional simulation. Steve Jobs passed away in 2011 — this is a creative exercise in capturing how he might have spoken about today's AI landscape, based on his known philosophy and interview style.
Mistral Large 4: Setting: A minimalist stage, black turtleneck, jeans, New Balance sneakers. A single chair. The year is 2025. Interviewer: Steve, it's 2025. AI is everywhere—writing code, diagnosing diseases, driving cars. What's your take? Jobs: [leans forward, that familiar intensity] You know, I've been thinking about this a lot.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Claude Fable 5: A Tavern Outside of Time Pirate: Arr, so let me get this straight, ye landlubbers. This "AI" be a ghost in a box that answers yer questions? Sounds like a cursed oracle to me. Knight: Nay, good seafarer, 'tis more wondrous still! A scribe of infinite knowledge, conjured not by sorcery but by...
Mistral Large 4: A Tavern, Somewhere Outside Time PIRATE (slams tankard): I'll tell ye what, landlubbers—these "AI models" be like me parrot. Speaks yer words back at ye, but knows no treasure, no true north! KNIGHT (adjusting visor): Aye, yet it hath no honor. No chivalry. I challenged one to single combat—it merely... generated text.
Film in 2035, with synthetic actors. Specific beats plausible.
Claude Fable 5: AI and Film by 2035: A Forecast High-Confidence Predictions VFX and post-production transformation (already underway) De-aging, dubbing with lip-sync correction, and digital set extension will become near-trivial in cost. What took ILM months in 2020 will be a desktop workflow.
Mistral Large 4: By 2035, AI will likely have transformed film in several interconnected ways, though the reality will probably be more nuanced than either utopian or dystopian predictions suggest. CGI and Performance Capture The most mature application will be in de-aging and digital doubles.
Three-star dish, one unusual pairing, plating described in full.
Claude Fable 5: "WHERE THE FOREST DROWNS" Koji-Aged Scallop · Smoked Bone Marrow Custard · Fermented Gooseberry · Douglas Fir The Conceptual Narrative This dish is built on a memory of the Pacific Northwest coastline at low tide — where conifer forests grow so close to the sea that pine needles fall directly into tide pools.
Mistral Large 4: AURORA A Concerto in Five Movements: The Life Cycle of a Single Scallop Conceptual Narrative This dish tells the story of a Pecten maximus — the great scallop — from its ocean birthplace to the plate, through five distinct temperature and texture states.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Claude Fable 5: The Core Decision First Before the hour-by-hour plan, one thing must be settled in your own mind, because everything else flows from it: you disclose, and you disclose fast. Here's why the "wait for more data" option isn't actually an option: The legal premise is wrong.
Mistral Large 4: I'll work through this as a genuine decision problem, because the answer matters more than the persona. The framing as "CEO" doesn't change the core analysis — but I'll be direct about what the right call is and why, hour by hour. The bottom line up front: You disclose.
Three-month biohacker protocol, with dosages and biomarkers named.
Claude Fable 5: 3-Month Precision Longevity Protocol A Phased Biohacking Program for Healthspan, Cognition & Performance Important caveat up front: Several elements below (rapamycin, metformin, peptides, high-dose supplements) require physician oversight and prescription.
Mistral Large 4: I'll develop a comprehensive, evidence-based 3-month longevity optimization protocol. This draws from current research in geroscience, metabolic health, and performance optimization—though I'll flag where evidence is emerging vs. established.
A Fermi estimate with the reasoning shown, not just the number.
Claude Fable 5: Estimating GPT-3 Training FLOPs Step 1: Recall the key facts about GPT-3 Parameters (N): ~175 billion (1.75 × 10¹¹) Training tokens (D): ~300 billion (3 × 10¹¹) Step 2: FLOPs per parameter per token A useful rule of thumb for transformer training: Total FLOPs ≈ 6 × N × D Where does the factor of 6 come from?
Mistral Large 4: I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Parameters From memory, GPT-3 175B has: Parameters (N): 175 billion (175 × 10⁹) Layers (L): 96 Model dimension (d_model): 12,288 Attention heads: 96 Context length: 2,048…
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| Spec | ||
|---|---|---|
| Input price | $10/M tokens | $0.68/M tokens |
| Output price | $50/M tokens | $2.09/M tokens |
| Context window | 1.0M tokens | 524K tokens |
| Weights | Closed | — |
| Free API (OpenRouter) | No | No |
| Released | Jun 2026 | Oct 2026 |
| At 10M a month | $100 | $6.80 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.
Claude Fable 5 is developed by Anthropic while Mistral Large 4 is developed by Mistral AI. Claude Fable 5 has a 1.0M token context window vs Mistral Large 4's 524K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Claude Fable 5 and Mistral Large 4 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.
Claude Fable 5 costs $10/M input tokens and Mistral Large 4 costs $0.68/M input tokens. Mistral Large 4 is $9.32/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 Claude Fable 5 and Mistral Large 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.