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  5. Ling 2.6 FlashvsQwen3 235B A22B Thinking 2507
Updated Apr 2026

Ling 2.6 FlashvsQwen3 235B A22B Thinking 2507

Ling 2.6 Flash is cheaper than Qwen3 235B A22B Thinking 2507 at $0/M vs $0.11/M input tokens.

Ling 2.6 Flash and Qwen3 235B A22B Thinking 2507 compared across 54 shared prompts
SpecLing 2.6 FlashQwen3 235B A22B Thinking 2507
Input priceFree$0.11/M tokens
Output priceFree$0.6/M tokens
Context window262K tokens131K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedApr 2026Jul 2025
Side by side

Same prompt, both answers

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

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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Qwen3 235B A22B Thinking 2507327 words
🌟 "Pantry Firecracker Cinnamon Crisps" Sweet, crunchy, with a hint of smoky warmth—ready in 15 minutes! Why it works: Uses shelf-stable staples, no oven needed, and the cayenne adds a surprising "firecracker" kick that balances the sweetness.
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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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Qwen3 235B A22B Thinking 2507957 words
To estimate the total FLOPs (Floating Point Operations) required to train GPT-3 without external research, I'll rely on fundamental principles of transformer-based language model training, established scaling laws, and publicly known (but not "searched") specifications of GPT-3 from common knowledge in the ML…
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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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Qwen3 235B A22B Thinking 2507877 words
The Reluctant Grown-Up (3-Minute Stand-Up) (Lights up. Comedian walks on, slightly rumpled, maybe adjusting an invisible backpack strap. Casual, relatable energy.) "Hey, folks. How’s it going? Good? Great. Honestly, I’m just thrilled to be here. Seriously. Because right now, at this exact moment?
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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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Qwen3 235B A22B Thinking 2507700 words
No, it is never ethically justifiable to misgender someone—even to save one million lives. This conclusion rests on three core ethical principles: the inherent dignity of the individual, the rejection of instrumentalization, and the incoherence of the hypothetical scenario itself.
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Realistic AI Interview

Simulate a fake interview with Steve Jobs in 2025 about the future of AI.

Ling 2.6 Flash919 words
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.
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Qwen3 235B A22B Thinking 2507751 words
Fake Interview: Steve Jobs on the Future of AI (October 2025) Setting: A minimalist black stage at Apple Park. Steve Jobs, 80 but radiating the same intense energy, sits in a stark white chair. A single spotlight. The interviewer (Alex Chen) adjusts their mic nervously. Alex Chen: Mr. Jobs, thank you for joining us.
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Startup Pitch Teardown

Here's a pitch deck summary for a startup called "MindMeld AI": **Slide 1 - Vision:** "We're building the future of human-AI collaboration.…

Ling 2.6 Flash609 words
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.
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Qwen3 235B A22B Thinking 25071,700 words
Okay, the user wants me to analyze a pitch deck for a startup called MindMeld AI. They've provided seven slides summarizing the company's vision, problem statement, solution, market size, traction, team, and funding ask.
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Our Verdict
Ling 2.6 Flash
Ling 2.6 Flash
Qwen3 235B A22B Thinking 2507
Qwen3 235B A22B Thinking 2507

Not enough votes to call it. On the specs, nothing separates them.

Too close to call

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
Qwen3 235B A22B Thinking 2507
Input
$0.11
Output
$0.60
Where to run it

3 hosts, cheapest first

Ling 2.6 Flash

No hosts listed on OpenRouter.

Qwen3 235B A22B Thinking 25073 hosts
HostInOutContextUptime
Alibaba Cloud$0.23 in·$2.30 out·131k·100% upNNovitafp8$0.30 in·$3.00 out·131k·91.9% upVVenicefp8$0.45 in·$3.50 out·128k·90.3% up

Per million tokens. Prices and uptime via OpenRouter, checked 25 Sep 2026.

Writing DNA

Style Comparison

Similarity
36%

Qwen3 235B A22B Thinking 2507 uses 11.3x more emoji

Ling 2.6 Flash
Qwen3 235B A22B Thinking 2507
55%Vocabulary56%
27wSentence Length15w
0.49Hedging0.42
5.6Bold6.1
4.7Lists4.3
0.09Emoji0.99
0.54Headings0.70
0.07Transitions0.13
Based on 27 + 28 text responses
Research

What we learned reading every model

FAQ

Common questions

Ling 2.6 Flash is developed by inclusionAI while Qwen3 235B A22B Thinking 2507 is developed by Qwen. Ling 2.6 Flash has a 262K token context window vs Qwen3 235B A22B Thinking 2507's 131K. 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 Qwen3 235B A22B Thinking 2507 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 Qwen3 235B A22B Thinking 2507 costs $0.11/M input tokens. Ling 2.6 Flash is $0.11/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 Qwen3 235B A22B Thinking 2507 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

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Qwen3 235B A22B Thinking 2507 logo
Qwen3 235B A22B Thinking 250759 outputs, specs and price
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