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  5. Ling 2.6 1TvsLing 2.6 Flash
Updated Apr 2026

Ling 2.6 1TvsLing 2.6 Flash

Ling 2.6 1T is the newer model, released after Ling 2.6 Flash.

Ling 2.6 1T and Ling 2.6 Flash compared across 54 shared prompts
SpecLing 2.6 1TLing 2.6 Flash
Input priceFreeFree
Output priceFreeFree
Context window262K tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedApr 2026Apr 2026
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 1T887 words
By 2035, AI will have moved from being a production tool to being an integrated creative partner, financier, and distribution engine in the film industry. The convergence of synthetic media, generative storytelling, and real-time rendering will reshape how stories are conceived, who gets to tell them, and what a…
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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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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 1T471 words
Here is a step-by-step back-of-the-envelope estimate, using only widely known facts about GPT-3 and standard deep learning training heuristics (no online search). Model size and parameter count GPT-3 is known to have about 175 billion parameters (175 × 10⁹).
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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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Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

Ling 2.6 1T797 words
Five-year operating and debt schedule (FY2026–FY2030) Entry (end-FY2025): EBITDA = $120.0m EV = 12.0x × $120.0m = $1,440.0m Fees = 2.0% × $1,440.0m = $28.8m Total uses = $1,440.0m + $28.8m = $1,468.8m Debt at close (5.5× FY2025 EBITDA = $660.0m): Term Loan (TL) = 4.0× = $480.0m Mezz = 1.5× = $180.0m Equity at close =…
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Ling 2.6 Flash957 words
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 =…
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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 1T151 words
Dish name: Pantry 7 Crunch Bowls Ingredients Pasta or rice (about 2 cups cooked) Canned black beans (1 can, rinsed) Canned diced tomatoes (1 can) Peanut butter (3–4 Tbsp) Soy sauce or tamari (2 Tbsp) Honey or maple syrup (1 Tbsp) Any spice/herb you keep (chili flakes, cumin, or garlic powder — about 1 tsp) Instructions…
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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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Character Voice Test

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

Ling 2.6 1T181 words
Pirate: Arrr, so this “AI” be a genie in a glass box? Feed it enough rum-scented scrolls and it tells ye where X marks the spot? Knight: A sorcerous scribe, more like. It spins tapestries of words without hand or heart—no honor, no oath, yet it jousts with reason and never tires.
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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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Generate a Stand-Up Routine

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

Ling 2.6 1T662 words
I’m going to keep this to three minutes, which means I have to talk fast, and I have to be honest. I don’t do puns. Puns are for people who want to be clever without having feelings. I have feelings, and they are mostly annoyed. Let’s start with doors. Have you noticed how doors now have personality?
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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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Our Verdict
Ling 2.6 1T
Ling 2.6 1T
Ling 2.6 Flash
Ling 2.6 FlashRunner-up

Not enough votes to call it. On the specs, Ling 2.6 1T has the edge: bigger model tier.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Ling 2.6 1T
Input
$0.000
Output
$0.000
Ling 2.6 Flash
Input
$0.000
Output
$0.000
Writing DNA

Style Comparison

Similarity
54%

Ling 2.6 Flash uses 8.8x more emoji

Ling 2.6 1T
Ling 2.6 Flash
55%Vocabulary55%
17wSentence Length27w
0.33Hedging0.49
1.9Bold5.6
4.1Lists4.7
0.00Emoji0.09
0.41Headings0.54
0.04Transitions0.07
Based on 27 + 27 text responses
Research

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Model pages

Ling 2.6 1T logo
Ling 2.6 1T58 outputs, specs and price
Ling 2.6 Flash logo
Ling 2.6 Flash58 outputs, specs and price
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