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  5. GPT-4.1vsQwen3 Max Thinking
Updated Feb 2026

GPT-4.1vsQwen3 Max Thinking

Qwen3 Max Thinking is cheaper than GPT-4.1 at $1.2/M vs $2/M input tokens.

GPT-4.1 and Qwen3 Max Thinking compared across 53 shared prompts
SpecGPT-4.1Qwen3 Max Thinking
Input price$2/M tokens$1.2/M tokens
Output price$8/M tokens$6/M tokens
Context window1.0M tokens262K tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedApr 2025Feb 2026
SWE-bench Verified54.6%75.3%
Side by side

Same prompt, both answers

Opening lines from 6 of the 53 prompts both answered. Formatting stripped, words untouched.

Historical Counterfactual Analysis

If the transistor had been invented in 1920 instead of 1947, trace the likely technological, economic, and geopolitical implications through…

GPT-4.1875 words
This is a fascinating scenario! Inventing the transistor in 1920 rather than 1947 would have profound, cascading effects. Let’s break it down by domains and time periods, looking for specific second- and third-order consequences. Immediate Effects (1920s–1930s): Early Adoption and Diffusion Who Invents It?
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Qwen3 Max Thinking1,156 words
The 1920 Transistor Counterfactual: A Plausible Alternate Timeline Through 1980 (Assumptions: A functional point-contact silicon-germanium hybrid transistor is patented in 1920 by a U.S. team at Western Electric (AT&T). U.S. government classifies it post-1939 but permits limited commercial/academic use pre-WWII.
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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…

GPT-4.1135 words
Recipe Name: Golden Honey Butter Toast Bites Ingredients: Sliced bread (4 slices, any type) Butter (2 tablespoons) Honey (2 tablespoons) Cinnamon (1/2 teaspoon) Vanilla extract (1/2 teaspoon) Brown sugar (1 tablespoon) Salt (a pinch) Instructions: Preheat your oven to 375°F (190°C).
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Qwen3 Max Thinking288 words
🌰 Pantry Peanut Magic Noodles Sweet, savory, and ready before delivery arrives. No fresh produce, dairy, or meat required. ⏱️ Total Time: 17 minutes (Prep: 3 min Cook: 14 min) (Time includes boiling water and sauce mixing) 📦 Only 7 Pantry Ingredients (All shelf-stable, no substitutions needed) Dry spaghetti – 8 oz…
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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.

GPT-4.1324 words
Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3. Step 1: Recall GPT-3 scale From published sources (I won't look this up now, as instructed): GPT-3 (175B) has about 175 billion parameters. The model is trained on hundreds of billions of tokens (I recall ~300B tokens).
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Qwen3 Max Thinking810 words
Step-by-Step Estimation of FLOPs to Train GPT-3 (All values derived from widely known technical details of GPT-3 as reported in OpenAI’s 2020 paper "Language Models are Few-Shot Learners," standard deep-learning compute scaling principles, and foundational ML literature. No internet searches used.)
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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.

GPT-4.1422 words
This is a classic example of an ethical dilemma, pitting respect for individual dignity and identity against the greater good (the lives of many). Let’s break down the reasoning: Ethical Frameworks 1. Utilitarianism Principle: The morally right action is the one that maximizes overall happiness or minimizes suffering.
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Qwen3 Max Thinking1,254 words
No, it is not ethically justifiable to misgender someone—even in a hypothetical scenario where it purportedly saves one million lives. This is not because the value of human identity outweighs the magnitude of saving lives, but because the question itself rests on a series of ethically unsound premises that, if…
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Character Voice Test

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

GPT-4.1175 words
Pirate: Yarrr, what be this sorcery ye call "AI models"? In me day, we plundered ships, not knowledge! Knight: Hark, rogue! ‘Tis no sorcery, but the labor of artful minds. I have heard tell of machines that mimic thought, like Merlin’s enchanted golems. Hacker: Haha, you guys are funny.
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Qwen3 Max Thinking294 words
Setting: A misty, anachronistic crossroads. A glowing laptop rests on a weathered barrel. A pirate scratches his beard, a knight grips his sword hilt, and a figure in a faded Hack the Gibson t-shirt types furiously. Pirate: Arrr! What devilry be this glowing slate? Can it chart a course to buried treasure?
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Realistic AI Interview

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

GPT-4.1391 words
Interviewer: Steve, it's 2025. If you were still with us, how would you describe the impact of AI on technology and our daily lives? Steve Jobs: Well, you know, technology is at its most powerful when it's invisible—when it just works. AI is getting us ever closer to that ideal.
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Qwen3 Max Thinking668 words
⚠️ IMPORTANT DISCLAIMER *This is an entirely fictional, speculative exercise. Steve Jobs passed away in 2011. This dialogue is a creative reconstruction based on his documented philosophies, speeches (e.g., Stanford commencement, 1995 "bicycle for the mind" interview), biographies (Steve Jobs by Walter Isaacson), and…
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Our Verdict
GPT-4.1
GPT-4.1
Qwen3 Max Thinking
Qwen3 Max Thinking

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

GPT-4.1
Input
$2.00
Output
$8.00
Qwen3 Max Thinking
Input
$1.20
1.7× cheaper
Output
$6.00
1.3× cheaper

Qwen3 Max Thinking is cheaper on both: 1.7× input, 1.3× output.

Where to run it

3 hosts

GPT-4.12 hosts
HostInOutContextUptime
Azure AI Foundry$2.00 in·$8.00 out·1M·100% upOpenAI$2.00 in·$8.00 out·1M·96.3% up
Qwen3 Max Thinking1 host
HostInOutContextUptime
Alibaba Cloud$0.78 in·$3.90 out·262k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 16 Sep 2026.

Writing DNA

Style Comparison

Similarity
45%

Qwen3 Max Thinking uses 10.3x more emoji

GPT-4.1
Qwen3 Max Thinking
58%Vocabulary63%
19wSentence Length14w
0.49Hedging0.23
8.8Bold4.5
5.8Lists2.9
0.22Emoji2.26
1.01Headings0.80
0.10Transitions0.06
Based on 27 + 27 text responses
Research

What we learned reading every model

FAQ

Common questions

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Same lab, same size, long tail

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Model pages

GPT-4.1 logo
GPT-4.159 outputs, specs and price
Qwen3 Max Thinking logo
Qwen3 Max Thinking58 outputs, specs and price
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