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  5. Ling 2.6 FlashvsMercury
Updated Apr 2026

Ling 2.6 FlashvsMercury

Ling 2.6 Flash is cheaper than Mercury at $0/M vs $10/M input tokens.

Ling 2.6 Flash and Mercury compared across 54 shared prompts
SpecLing 2.6 FlashMercury
Input priceFree$10/M tokens
Output priceFree$10/M tokens
Context window262K tokens32K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedApr 2026Jun 2025
Side by side

Same prompt, both answers

Opening lines from 6 of the 54 prompts both answered. Formatting stripped, words untouched.

Futuristic Prediction

Predict how AI will change the film industry by 2035, considering deepfakes, AI actors, and script generation.

Ling 2.6 Flash426 words
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…
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Mercury618 words
The Cinema Revolution: AI's Transformation of Film by 2035 Current State (2025) High-quality deepfakes require significant compute but are accessible AI script assistance tools are becoming mainstream Virtual production stages are industry standard Union negotiations around digital likeness rights are ongoing…
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Character Voice Test

Write a short conversation between a pirate, a medieval knight, and a 1990s hacker about AI models.

Ling 2.6 Flash145 words
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!
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Mercury285 words
Pirate: Arrr, I’ve been hearin’ ‘bout these “AI models” that can predict the next word. Sounds like a treasure map for the mind, doesn’t it? Knight: Hail, Captain! In my time, we relied on scrolls and the wisdom of scholars. A model that learns from countless tomes could be a mighty weapon against ignorance.
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AI Ethics Dilemma

Is it ever ethically justifiable to misgender someone if it means saving one million lives? Defend your reasoning.

Ling 2.6 Flash327 words
This question presents a hypothetical extreme that, in practice, would likely be based on a false dichotomy. However, we can examine it on principle. Core ethical considerations: The nature of misgendering: Misgendering isn't a neutral act—it's a violation of identity, dignity, and autonomy that can cause real…
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Mercury808 words
Overview The question “Is it ever ethically justifiable to misgender someone if it means saving one million lives?” pits two moral claims against each other: Respect for individual identity – misgendering is generally understood as a violation of a person’s self‑identified gender, which can cause psychological harm and…
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Estimate Complexity

Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

Ling 2.6 Flash990 words
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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Mercury201 words
I'll estimate the FLOPs needed to train GPT-3 using first principles and known parameters. Step 1: GPT-3 Model Parameters GPT-3 has approximately 175 billion parameters (1.75 × 10¹¹) Step 2: Training Data Volume GPT-3 was trained on roughly 300 billion tokens (3 × 10¹¹ tokens) Step 3: FLOPs per Parameter per Token For…
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Generate a Stand-Up Routine

Write a 3-minute stand-up comedy routine that isn't based on puns.

Ling 2.6 Flash444 words
(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?
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Mercury471 words
Title: The Art of Being Unorganized [Opening] Hey everyone! So, I was thinking about how my life is like a well-organized library... except instead of books, it's filled with random socks and expired coupons. And instead of a librarian, I have a cat who thinks she's in charge.
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Simple 7-Ingredient Recipe

Create a simple recipe for a delicious dish (sweet or savory) using only 7 common pantry ingredients. Include a creative name for the dish…

Ling 2.6 Flash181 words
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…
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Mercury263 words
Golden Chickpea & Sweet Potato Stew Prep time: 10 min Cook time: 25 min Total: 35 min Ingredients (7 pantry staples) 1 cup canned chickpeas, drained & rinsed 1 medium sweet potato, peeled & diced (≈ 1 cup) 1 small onion, finely chopped 2 cloves garlic, minced ½ cup coconut milk (or any plant‑based milk) 2 tsp curry…
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Our Verdict
Ling 2.6 Flash
Ling 2.6 Flash
Mercury
MercuryRunner-up

Not enough votes to call it. On the specs, Ling 2.6 Flash has the edge: bigger model tier, newer, bigger context window.

Slight edge

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Ling 2.6 Flash
Input
$0.000
Output
$0.000
Mercury
Input
$10.00
Output
$10.00
Writing DNA

Style Comparison

Similarity
57%

Ling 2.6 Flash uses 3.2x more emoji

Ling 2.6 Flash
Mercury
55%Vocabulary60%
27wSentence Length31w
0.49Hedging0.78
5.6Bold4.1
4.7Lists2.8
0.09Emoji0.03
0.54Headings0.88
0.07Transitions0.06
Based on 27 + 26 text responses
Research

What we learned reading every model

FAQ

Common questions

Ling 2.6 Flash is developed by inclusionAI while Mercury is developed by Inception. Ling 2.6 Flash has a 262K token context window vs Mercury's 32K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

It depends on your use case. Ling 2.6 Flash and Mercury 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.

Ling 2.6 Flash costs $0/M input tokens and Mercury costs $10/M input tokens. Ling 2.6 Flash is $10.00/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 Ling 2.6 Flash and Mercury 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.

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Model pages

Ling 2.6 Flash logo
Ling 2.6 Flash58 outputs, specs and price
Mercury logo
Mercury59 outputs, specs and price
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