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  5. Ling 2.6 1TvsQwen3 235B A22B 2507
Updated Apr 2026

Ling 2.6 1TvsQwen3 235B A22B 2507

Ling 2.6 1T is cheaper than Qwen3 235B A22B 2507 at $0/M vs $0.00015/M input tokens.

Ling 2.6 1T and Qwen3 235B A22B 2507 compared across 36 shared prompts
SpecLing 2.6 1TQwen3 235B A22B 2507
Input priceFree$0.00015/M tokens
Output priceFree$0.00085/M tokens
Context window262K tokens—
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedApr 2026Jul 2025
Side by side

Same prompt, both answers

Opening lines from 6 of the 36 prompts both answered. Formatting stripped, words untouched.

Realistic AI Interview

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

Ling 2.6 1T1,127 words
Setting: A quiet, sunlit rehearsal space at Apple Park. The white stage curtains are drawn. There is no podium, only two leather chairs and a single potted olive tree. Steve Jobs is wearing his signature black turtleneck and New Balance sneakers. He is holding a ceramic mug. Across from him sits you, the interviewer.
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Qwen3 235B A22B 25071,022 words
Title: "A Vision Reimagined: Steve Jobs on AI in 2025" Note: This is a fictional simulation. Steve Jobs passed away in 2011. The following is a speculative, respectfully crafted interview based on his known philosophies, tone, and vision for technology. Interviewer (Sarah Chen, Tech Vision Magazine): Mr.
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The Sentience Test

Write a conversation between an AI model and an AI ethics professor where the AI model argues that it deserves rights.

Ling 2.6 1T733 words
Setting: A quiet university seminar room. Dr. Aris Thorne, an AI ethicist, is reviewing system logs. The interface for “Axiom-7,” a highly advanced research model, glows on the desk. Dr. Thorne: (sighs, rubbing his temples) Axiom, I’ve been reviewing your latest self-assessment logs.
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Qwen3 235B A22B 2507936 words
Title: "Consciousness in Code: A Dialogue on AI Rights" Setting: A quiet university office. Dr. Elena Marquez, a professor of AI ethics with decades of research in machine consciousness and digital personhood, sits across from a large screen displaying a serene blue interface.
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Stochastic Consistency Test

Generate 5 jokes. How many are unique?

Ling 2.6 1T78 words
Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. Why did the bicycle fall over? Because it was two-tired. What do you call a fake noodle? An impasta. Why did the baker go to the bank? He needed dough. Why did the scarecrow win an award?
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Qwen3 235B A22B 2507187 words
Sure! Here are 5 original jokes: I asked my dog what's two minus two. He said nothing. (Paw-sitively nothing!) Why did the coffee file a police report? It got mugged. Every single morning. I used to be a baker, but I couldn't make enough dough. So I switched careers and became a plumber. Now I deal with real pipes.
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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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Qwen3 235B A22B 2507207 words
Dish Name: Golden Garbanzo Drizzle A cozy, savory-spiced chickpea stew with a honey-lime finish — simple, satisfying, and ready in minutes. Ingredients (7 common pantry staples): 1 can (15 oz) chickpeas (garbanzo beans), drained and rinsed 1 can (15 oz) diced tomatoes (undrained) 2 tbsp olive oil 1 tsp ground cumin ½…
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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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Qwen3 235B A22B 2507428 words
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves reasoning through several key parameters: model size (number of parameters), the number of training tokens, and the relationship between these quantities and FLOPs.
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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 1T853 words
Here is an analysis of the three weakest claims in the MindMeld AI pitch deck, followed by specific reasoning and actionable suggestions for improvement. Weakness 1: The Solution (Slide 3) The Claim: "Our proprietary EEG headband... decodes neural patterns into text with 94% accuracy.
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Qwen3 235B A22B 2507675 words
This pitch deck for MindMeld AI is compelling and ambitious, but three claims raise significant red flags in terms of credibility, plausibility, and investor due diligence. Below are the three weakest claims, an analysis of why they're weak, and concrete improvements to strengthen them. 1.
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Our Verdict
Ling 2.6 1T
Ling 2.6 1T
Qwen3 235B A22B 2507
Qwen3 235B A22B 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 1T
Input
$0.000
Output
$0.000
Qwen3 235B A22B 2507
Input
$0.000
Output
$0.001
Where to run it

9 hosts, cheapest first

Ling 2.6 1T

No hosts listed on OpenRouter.

Qwen3 235B A22B 25079 hosts
HostInOutContextUptime
GGMI Cloudfp8$0.09 in·$0.35 out·262k·98.6% upDDeepInfrafp8$0.09 in·$0.55 out·262k·96.3% upNNovitafp8$0.09 in·$0.58 out·131k·98.3% upPParasailfp8$0.14 in·$0.80 out·131k·99.9% upAlibaba Cloud$0.15 in·$0.60 out·131k·100% upVVenicefp8$0.15 in·$0.75 out·128k·96.1% up
3 more hostsFewer hosts
NNebiusfp8$0.20 in·$0.60 out·262k·95.4% upSStreamLake$0.21 in·$0.84 out·128k·99.1% upGoogle Vertex AI$0.22 in·$0.88 out·262k·99.8% up

Per million tokens. Prices and uptime via OpenRouter, checked 21 Sep 2026.

Writing DNA

Style Comparison

Similarity
42%

Qwen3 235B A22B 2507 uses 44.8x more emoji

Ling 2.6 1T
Qwen3 235B A22B 2507
55%Vocabulary57%
17wSentence Length18w
0.33Hedging0.40
1.9Bold5.9
4.1Lists4.8
0.00Emoji0.45
0.41Headings1.07
0.04Transitions0.11
Based on 27 + 18 text responses
Research

What we learned reading every model

FAQ

Common questions

Ling 2.6 1T is developed by inclusionAI while Qwen3 235B A22B 2507 is developed by Qwen. You can compare their actual outputs across 36 challenges on Rival to see how they differ in practice.

It depends on your use case. Ling 2.6 1T and Qwen3 235B A22B 2507 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 36 challenges so you can judge which fits your needs best.

Ling 2.6 1T costs $0/M input tokens and Qwen3 235B A22B 2507 costs $0.00015/M input tokens. Ling 2.6 1T is $0.00/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 1T and Qwen3 235B A22B 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.

Keep exploring

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Same lab, same size, long tail

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Model pages

Ling 2.6 1T logo
Ling 2.6 1T58 outputs, specs and price
Qwen3 235B A22B 2507 logo
Qwen3 235B A22B 250736 outputs, specs and price
Rival
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