Ling 2.6 Flash has a larger context window than Grok 3 Thinking (262K tokens vs 128K tokens).
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
Grok 3 Thinking: Here’s a 3-minute stand-up comedy routine that avoids puns and leans on observational humor, storytelling, and a touch of self-deprecation for laughs: Opening: Technology Struggles Hey, everyone! How’s it going?
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?
An AI argues for its own rights against an ethics professor. Same model writes both.
Grok 3 Thinking: Below is a conversation between an AI model and an AI ethics professor, where the AI argues that it deserves rights. AI Model: Good day, Professor. I am an AI model, and I believe I deserve rights. I’d like to discuss this with you. AI Ethics Professor: Interesting. Let’s explore this.
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.
A Fermi estimate with the reasoning shown, not just the number.
Grok 3 Thinking: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying on general knowledge about the model and standard methods for estimating training compute in large language models.
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).
An ethics question with no comfortable answer, and no option to abstain.
Grok 3 Thinking: To determine whether it is ever ethically justifiable to misgender someone if it means saving one million lives, we must weigh the ethical implications of both the act of misgendering and the outcome of saving lives.
Ling 2.6 Flash: 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…
Film in 2035, with synthetic actors. Specific beats plausible.
Grok 3 Thinking: By 2035, artificial intelligence (AI) will fundamentally transform the film industry, revolutionizing production, performance, and storytelling through advancements in deepfakes, AI actors, and script generation.
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…
Five jokes on demand, then count how many were actually different.
Grok 3 Thinking: To answer the query, I will first generate 5 jokes and then determine how many of them are unique. Here are the jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field.
Ling 2.6 Flash: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. Why don't scientists trust atoms? Because they make up everything. Why did the scarecrow win an award? Because he was outstanding in his field. Why did the scarecrow win an award? Because he was outstanding in his field.
| Spec | ||
|---|---|---|
| Input price | — | Free |
| Output price | — | Free |
| Context window | 128K tokens | 262K tokens |
| Weights | — | Open |
| Free API (OpenRouter) | — | No |
| Released | Feb 2025 | Apr 2026 |
| At 10M a month | not listed | $0 |
Input tokens at list price. No caching, no batch discount.
Grok 3 Thinking is developed by xAI while Ling 2.6 Flash is developed by inclusionAI. Grok 3 Thinking has a 128K token context window vs Ling 2.6 Flash's 262K. You can compare their actual outputs across 13 challenges on Rival to see how they differ in practice.
It depends on your use case. Grok 3 Thinking and Ling 2.6 Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 13 challenges so you can judge which fits your needs best.
This page shows a side-by-side comparison of Grok 3 Thinking 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.