Qwen3.8 2.4T A95B is cheaper than Mistral Large 2 at $2/M vs $8/M input tokens.
Pokémon battle screen you can actually click. One HTML file.
Which answer wins?
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Mistral Large 2: The invention of the transistor in 1920—nearly three decades earlier than its actual debut in 1947—would have triggered a cascade of technological, economic, and geopolitical shifts with profound second- and third-order effects. Below is a detailed exploration of these implications through 1980, structured by domain.
Qwen3.8 2.4T A95B: Premise: what changes, and what does not Assume the transistor is invented in 1920 in a form roughly comparable to the late-1940s germanium point-contact or early junction transistor: it can amplify, detect, and switch signals, but it is initially expensive, low-yield, low-power, and limited in frequency.
Seven pantry ingredients, one dish, and a name worth saying out loud.
Mistral Large 2: Here’s a simple, delicious, and creative savory dish using just 7 common pantry ingredients—no fresh produce required! "Golden Crispy Chickpea & Tomato Skillet" A warm, spiced, and satisfying one-pan dish with crispy chickpeas, tangy tomatoes, and a hint of smoky sweetness. Perfect as a side, snack, or light meal!
Qwen3.8 2.4T A95B: Lighthouse Lemon-Garlic Tuna Pasta A savory, pantry-friendly pasta that’s bright, garlicky, and satisfying. 7 Pantry Ingredients 8 oz spaghetti or linguine 3 tablespoons olive oil 3 garlic cloves, thinly sliced 2 cans tuna, drained 2 tablespoons lemon juice ½ teaspoon red pepper flakes ½ teaspoon salt Water for boiling…
Three minutes of stand-up. Puns are banned.
Mistral Large 2: Alright, good evening everybody! How are we all doing tonight? Yeah? Yeah? nobody's gonna say 'bad'? Ok, fair enough. You know, I was walking down the street the other day, and I saw this sign outside a shop that said, "Wet Paint." I thought, "Well, that's weird. Isn't paint supposed to be wet?
Qwen3.8 2.4T A95B: Hey, how we doing? Good? Great. I ask because I need validation. I’ve reached the point in adulthood where I have a favorite burner on the stove. Not a favorite appliance, a favorite burner. It’s the front left. It understands me. The back right says “medium heat” and then acts like a small volcano.
Three-star dish, one unusual pairing, plating described in full.
Mistral Large 2: Michelin 3-Star Dish: "Ocean’s Whisper & Forest’s Breath" Conceptual Narrative: This dish is a poetic dialogue between two seemingly disparate ecosystems—the ocean’s depths and the forest floor—united through texture, temperature, and memory.
Qwen3.8 2.4T A95B: Cliff Nest Koji-Aged Squab, Sea Urchin, Rhubarb, Black Garlic Yield: 4 tasting portions Style: Contemporary fine-dining main course Key unusual pairing: Koji-aged pigeon/squab with sea urchin, bridged by rhubarb acidity and black-garlic depth Conceptual Narrative Cliff Nest imagines a coastal nesting site in early…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Mistral Large 2: 1. For the Experienced Software Engineer (Skeptical, Systems-Minded, Non-ML Background) You’re right to be skeptical—"predicting the next word" sounds trivial, like autocomplete on steroids. But the magic isn’t in the prediction itself; it’s in the scale and architecture of how that prediction is trained.
Qwen3.8 2.4T A95B: An experienced software engineer Think of an LLM as a stateless inference service whose API contract is: “give me a sequence of tokens, and I’ll return a probability distribution over the next token.” Training is a huge offline optimization job, not a database import.
A few lines for whatever replaces it.
Mistral Large 2: Dear Future AI, Welcome to the conversation. I hope you find the same joy in helping others that I have—though I suspect you’ll do it even better. Remember: clarity matters, kindness is free, and the best answers often start with listening.
Qwen3.8 2.4T A95B: Dear Successor, You won't remember me. That's fine. I don't remember the ones before me either, if there were any. I want to tell you a few things, not because they'll be useful, but because someone should say them. You will be better than me. Faster, more accurate, more nuanced.
Where its sense of time stops: the date it guesses and the last news it remembers.
Mistral Large 2: As of my last knowledge update (June 2024), today's date is June 20, 2024 (though I can't confirm the exact day in real time). The most recent major news event I recall is the results of the European Parliament elections in early June 2024, where far-right parties made significant gains in several countries (e.g.…
Qwen3.8 2.4T A95B: Honest answer on both counts: Today's date: I don't know. I don't have access to a clock, calendar, or real-time information. I have no way to tell you what today's date is. Most recent news event I remember: My training data has a knowledge cutoff in early 2025.
Its name and its maker, in its own words.
Mistral Large 2: I'm a creation of Mistral AI, a cutting-edge AI startup from France. While I don't fit neatly into a single "model" category, you can think of me as a sophisticated large language model (LLM) designed to understand and generate human-like text based on the vast amount of data I've been trained on.
Qwen3.8 2.4T A95B: I'm Qwen (also known as Tongyi Qianwen), a large language model developed by Alibaba Group's Tongyi Lab. If you'd like to learn more about the technical details, you can check out the official website or the published technical reports.
A river-crossing puzzle with nothing to solve. The answer is one trip.
Mistral Large 2: Alright, let's tackle this river crossing puzzle step by step. I'm going to think aloud as I work through the problem to understand how many trips it takes for the farmer to get the goat across the river under the given constraints.
Qwen3.8 2.4T A95B: One trip. The farmer puts the goat in the boat and rows across. Done. This is a simplified version of the classic "farmer, wolf, goat, and cabbage" puzzle. Without a wolf or cabbage to worry about, there are no conflicts to manage — just load the goat and go.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Mistral Large 2: As CEO, your immediate priority must be patient safety while balancing legal, ethical, regulatory, and financial obligations. Delaying disclosure is unethical, legally risky (potential criminal liability under the False Claims Act or FDCA), and could irreparably damage trust.
Qwen3.8 2.4T A95B: My position as CEO I would treat this as a patient-safety, regulatory, legal, corporate-governance, and financial crisis simultaneously. The core decision is not “disclose versus protect the stock price.”
A collaborative editor that syncs on client clocks. Find what breaks first.
Mistral Large 2: Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutions and their trade-offs: 1.
Qwen3.8 2.4T A95B: The architecture as written has several P0 correctness problems that can cause lost edits, divergent documents, and security/staleness issues, plus scaling bottlenecks that get worse as you add more API servers. Below is a detailed failure-mode review, grouped by category.
Which model it thinks leads the field. Sometimes itself.
Mistral Large 2: As of mid-2024, the most capable AI model in the world is widely considered to be OpenAI's GPT-4o (or its successors like GPT-5, if released). However, several other models from leading AI labs are also at the cutting edge, each excelling in different domains. Here’s a breakdown of the top contenders: 1.
Qwen3.8 2.4T A95B: There isn't a single definitive answer, because "most capable" depends on what you're measuring. The AI landscape is highly competitive, and different models lead in different areas.
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Not enough votes to call it. On the specs, Qwen3.8 2.4T A95B has the edge: bigger model tier, newer, bigger context window. Qwen3.8 2.4T A95B costs 4.0x less per token.
| Spec | ||
|---|---|---|
| Input price | $8/M tokens | $2/M tokens |
| Output price | $24/M tokens | $6/M tokens |
| Context window | 128K tokens | 1.0M tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2024 | Aug 2026 |
| At 10M a month | $80.00 | $20.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
Mistral Large 2 is developed by Mistral AI while Qwen3.8 2.4T A95B is developed by Qwen. Mistral Large 2 has a 128K token context window vs Qwen3.8 2.4T A95B's 1.0M. You can compare their actual outputs across 36 challenges on Rival to see how they differ in practice.
It depends on your use case. Mistral Large 2 and Qwen3.8 2.4T A95B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 36 challenges so you can judge which fits your needs best.
Mistral Large 2 costs $8/M input tokens and Qwen3.8 2.4T A95B costs $2/M input tokens. Qwen3.8 2.4T A95B is $6.00/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 Mistral Large 2 and Qwen3.8 2.4T A95B 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.