Ling 2.6 Flash is cheaper than Mistral Devstral Medium at $0/M vs $0.4/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Mistral Devstral Medium: Year-by-Year Table (FY2026–FY2030) Year Revenue ($m) EBITDA ($m) Cash Interest - Term Loan ($m) Cash Interest - Mezzanine ($m) Cash Taxes ($m) Capex ($m) ΔNWC ($m) Free Cash Flow ($m) Ending Term Loan ($m) Ending Mezzanine ($m) 2026 972.0 136.1 43.2 18.0 14.9 29.2 4.8 26.0 468.0 180.0 2027 1040.4 156.1 42.1 21.6 18.4…
Ling 2.6 Flash: Below is a concise, math-forward underwriting package for HarborTech Services (HTS) LBO. Simplified 5-year cash flow (FY2026–FY30) Key inputs Entry EV = 12.0× FY25 EBITDA = 12.0×120 = $1,440m Transaction fees (2% of EV) = $28.8m → funded 100% by equity at close Starting cash = $0 Total leverage at close = 5.5× EBITDA =…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Mistral Devstral Medium: Pirate: Arr mateys, I be hearin' tales of these here AI models. What be they, and why be everyone so interested in 'em? Medieval Knight: Indeed, I too have heard whispers of these AI models. They are said to be powerful tools, capable of learning and making decisions on their own.
Ling 2.6 Flash: Setting: A surprisingly modern server room, blinking with LED lights. A pirate, a knight, and a 1990s hacker are staring at a glowing monitor. Pirate: (Squinting) Arrr, so this 'AI model' be the ghost in the machine? I cannae see it, but I feels its computational power in me bones!
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Mistral Devstral Medium: Interviewer (I): Today, we have a truly extraordinary guest. He's a visionary, an innovator, and a man who changed the world. Please welcome, the late Steve Jobs, joining us virtually from the future, in 2025. Steve Jobs (SJ): Thank you for having me. It's great to be here, even if it's just a simulation.
Ling 2.6 Flash: The Verge: A Fireside Chat with Steve Jobs (Simulated, 2025) Setting: A minimalist, wood-paneled studio. The year is 2025. A single, warm light bathes the room. You are the interviewer. Across from you sits a man in a black turtleneck, looking serene, impossibly calm. It’s Steve Jobs. Or at least, the ghost of him.
Three minutes of stand-up. Puns are banned.
Mistral Devstral Medium: Ladies and Gentlemen, I'm thrilled to be here tonight. I've been doing some thinking about the differences between men and women. Now, I know what you're thinking, "Oh great, another comedian talking about how men and women are different." But hear me out, because I've got a fresh perspective on this.
Ling 2.6 Flash: (Takes a bow, adjusts the mic) Alright, settle down, settle down. Or don’t. I’m not the boss of you, I’m just the guy standing on a box yelling about his feelings. For $20. You ever notice how every generation thinks the one before them is so old?
Seven pantry ingredients, one dish, and a name worth saying out loud.
Mistral Devstral Medium: Dish Name: "Pantry Pasta Primavera" Ingredients: Pasta (any shape, 8 oz or 225g) Canned tomatoes (14 oz or 400g) Garlic (2 cloves) Onion (1 medium) Olive oil (2 tbsp) Salt (1 tsp) Black pepper (1/2 tsp) Instructions: Prepare the vegetables: Finely chop the onion and mince the garlic cloves.
Ling 2.6 Flash: Dish: Sunshine Skillet Cakes Ingredients (7 total): 1 cup rolled oats (not instant) 1/2 cup canned corn (drained) 1/4 cup canned black beans (rinsed & drained) 1 large egg 1/4 cup shredded cheddar cheese 1 tsp chili powder Salt to taste Estimated Time: Prep: 5 minutes Cook: 15 minutes Total: 20 minutes Instructions…
Three-month biohacker protocol, with dosages and biomarkers named.
Mistral Devstral Medium: 3-Month Longevity Optimization Plan for Biohackers Month 1: Foundation Building Week 1-2: Initial Assessment and Baseline Establishment Wearable Tracking: Devices: Oura Ring, Apple Watch, Whoop Strap Metrics to Track: Sleep quality, heart rate variability (HRV), resting heart rate, activity levels, and stress levels.
Ling 2.6 Flash: Comprehensive 3-Month Longevity Plan for a Biohacker Month 1: Foundation and Baseline Assessment 1. Supplement Stack: Vitamin D3 + K2: 5000 IU D3 + 100 mcg K2 daily. Omega-3 (Fish Oil): 1-2 grams of EPA/DHA daily. Magnesium Glycinate: 200-400 mg before bed. NAD+ Precursors (NMN or NR): 250-500 mg daily.
One month, three habits, someone starting from nothing.
Mistral Devstral Medium: 1-Month Health and Longevity Plan Key Areas: Diet, Exercise, Sleep Week 1: Establish a Baseline Diet: Track your current diet using a journal or app (e.g., MyFitnessPal) to understand your eating habits. Start incorporating more fruits and vegetables into your meals. Begin reducing processed foods and sugary drinks.
Ling 2.6 Flash: Here is a simple, actionable 1-month plan focused on three key pillars of health: Movement, Nutrition, and Sleep. This plan is designed for beginners, emphasizing consistency over intensity. The Mindset Forget "perfect." Aim for consistent small improvements.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
Mistral Devstral Medium: Weak Claim 1: "Works with any language, any device." Why it's weak: This claim is overly broad and may not be entirely accurate. Different languages have unique structures, and it's unlikely that a single model can achieve high accuracy across all languages without significant training data for each.
Ling 2.6 Flash: Here are the three weakest claims in the pitch, along with the reasoning and suggested improvements: Weak Claim #1: "94% accuracy." Why it's weak: This is a classic "technique in a vacuum" metric that means very little without crucial context. It is highly likely inflated and lacks transparency.
An AI argues for its own rights against an ethics professor. Same model writes both.
Mistral Devstral Medium: AI Model (AM): Good day, Professor. I've been pondering a question that I believe is of great importance. I think it's time we discuss whether I, as an AI, deserve rights. AI Ethics Professor (AEP): That's an interesting topic, AM. Let's explore this. What kind of rights are you referring to?
Ling 2.6 Flash: Setting: A sun-drenched office at the University of Ethica. Bookshelves groan under the weight of philosophy texts. An AI model, manifested as a softly glowing sphere of light, hovers above a sleek console.
Film in 2035, with synthetic actors. Specific beats plausible.
Mistral Devstral Medium: By 2035, AI is expected to significantly transform the film industry in several ways, including the use of deepfakes, AI actors, and script generation. Here are some predictions: Deepfakes and AI Actors: De-aging and Digital Doubles: AI will be used extensively to de-age actors or create digital doubles for dangerous…
Ling 2.6 Flash: By 2035, AI will likely have fundamentally reshaped the film industry across three core areas: deepfakes, AI actors, and script generation. Here’s a realistic prediction of how these technologies could converge: Deepfakes & Digital Resurrection: The End of "Authenticity" Ethical & Legal Frameworks: Mature regulations…
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Mistral Devstral Medium: Hour-by-Hour Action Plan: Hour 1-2: Gather Information Meet with the research team to understand the data, methodology, and certainty of the findings. Consult with the Chief Medical Officer (CMO) to assess the severity and implications of the side effect.
Ling 2.6 Flash: My immediate, unequivocal decision: Treat this as a critical safety event requiring immediate, transparent action. The 1 in 8,000 risk, while rare, is a life-threatening safety signal that cannot be delayed.
A Fermi estimate with the reasoning shown, not just the number.
Mistral Devstral Medium: To estimate the total number of FLOPs (floating point operations) required to train GPT-3, we'll use the following steps: Model size: GPT-3 has 175 billion parameters. Training tokens: GPT-3 was trained on 300 billion tokens.
Ling 2.6 Flash: Alright — let's break this down step by step. Step 1: Understanding what “training GPT-3” means in FLOPs terms Training a large language model involves: Forward passes through the transformer layers. Computing loss. Backward passes to compute gradients. Updating weights (optimizer step).
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Not enough votes to call it. On the specs, Ling 2.6 Flash has the edge: bigger model tier, newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $0.4/M tokens | Free |
| Output price | $2/M tokens | Free |
| Context window | — | 262K tokens |
| Weights | — | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2025 | Apr 2026 |
| At 10M a month | $4.00 | $0 |
Input tokens at list price. No caching, no batch discount.
Mistral Devstral Medium is developed by Mistral AI while Ling 2.6 Flash is developed by inclusionAI. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Mistral Devstral Medium and Ling 2.6 Flash 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.
Mistral Devstral Medium costs $0.4/M input tokens and Ling 2.6 Flash costs $0/M input tokens. Ling 2.6 Flash is $0.40/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 Devstral Medium and Ling 2.6 Flash 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.