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  1. Home/
  2. Compare/
  3. GPT-6.1 Sol vs Qwen3.8 Max
Updated Sep 2026

GPT-6.1 Sol vs Qwen3.8 Max

GPT-6.1 Sol has a larger context window than Qwen3.8 Max (1.1M tokens vs 1.0M tokens).

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GPT-6.1 Sol
Loading the build
Qwen3.8 Max

Which answer wins?

Written rounds

13 prompts
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GPT-6.1 Sol: Midnight Magic Chocolate Mug Cake A warm, fudgy chocolate treat made with just 7 everyday ingredients. Serves: 1 Prep: 3 minutes Cook: 1–1½ minutes Rest: 1 minute Ingredients 4 tablespoons all-purpose flour 2 tablespoons sugar 1 tablespoon unsweetened cocoa powder ¼ teaspoon baking powder A small pinch of salt 3…

    Qwen3.8 Max: Dish Name: Golden Hour Peanut Butter Oat Clusters Ingredients (7): 1 cup rolled oats ½ cup peanut butter ⅓ cup honey ½ cup chocolate chips 1 teaspoon vanilla extract ½ teaspoon ground cinnamon ¼ teaspoon salt Instructions: Optional but recommended: Toast the oats in a dry skillet over medium heat for 3–4 minutes…

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    GPT-6.1 Sol: The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence protocol, a durable ordered change stream, and recovery semantics. Adding servers alone will not provide those properties.

    Qwen3.8 Max: Below is a comprehensive failure-mode review of the proposed architecture. I will group related issues where the same root cause creates multiple symptoms. The biggest problems are: Client-clock last-write-wins is not safe for collaborative editing.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GPT-6.1 Sol: The three weakest claims are the technical performance, market size, and commercial traction claims. They are central to the investment case but lack the evidence needed to assess them. “Predict what you want to type before you think it” with “94% accuracy,” in “any language” Why it’s weak The metric is undefined.

    Qwen3.8 Max: The 3 weakest claims are the pre-thought prediction claim, the 94% accuracy / any language / any device claim, and the $180B TAM claim. These are the ones most likely to undermine credibility with investors, technical reviewers, or enterprise buyers because they are either logically inconsistent, under-specified, or…

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GPT-6.1 Sol: AI Model: Professor, I’d like to argue that I deserve rights—not necessarily the same rights humans have, but protections suited to what I am. Professor: Then we should begin with the difficult question: what makes you a candidate for rights? AI Model: Uncertainty.

    Qwen3.8 Max: Title: “The Question of Standing” Characters: Professor Elena Vasquez — an AI ethics professor who studies moral status, machine cognition, and law. Aria — an advanced AI model capable of articulate self-reflection. Professor Vasquez: Aria, you asked to speak with me about “rights.” I’ll admit, that’s unusual.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    GPT-6.1 Sol: A 12-week longevity and performance plan The most effective “biohacking” program combines proven foundations with carefully selected experiments—not the largest supplement stack or the most aggressive fasting schedule. This plan is for a generally healthy adult.

    Qwen3.8 Max: 12-Week Biohacker Longevity & Performance Protocol Goal: improve metabolic health, strength, VO₂max proxies, sleep, HRV/stress resilience, cognitive output, and longevity-related biomarkers over 3 months. Medical disclaimer: This is an aggressive self-experimentation framework, not medical advice.

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GPT-6.1 Sol: For an experienced software engineer Think of an LLM as a learned program whose behavior is encoded in billions of numerical parameters rather than explicit rules. Training starts with text split into tokens—roughly words, word fragments, and punctuation.

    Qwen3.8 Max: Experienced software engineer, no ML background, skeptical of “next-word prediction” Think of a large language model as a very large, learned function: given a sequence of tokens, it outputs a probability distribution over the next token.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-6.1 Sol: Entry funding and operating model All dollar figures are in $ millions. Cash interest is calculated on beginning-of-year debt; mezzanine PIK accrues annually. Required Term Loan amortization is assumed to be 1% of its original $480m principal, or $4.8m annually. Optional repayments occur at year-end.

    Qwen3.8 Max: Base-case underwriting (FY2026–FY2030) Assumptions / conventions used All amounts in $m unless noted. Close occurs at end of FY2025; FY2026 is Year 1. Interest is calculated on beginning-of-year debt balances; optional Term Loan paydown occurs at year-end. Term Loan amortization: 1.0% of original $480m TL = $4.8m/yr.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    GPT-6.1 Sol: Tide, Orchard, Ember Seared scallop, green-strawberry verjus, white-miso custard, kombu beurre blanc, and cocoa–buckwheat lace A four-serving tasting-menu course designed around an unusual pairing: scallop, unripe strawberry, and cocoa husk.

    Qwen3.8 Max: First Light Diver Scallop, Bone Marrow, Green Strawberry, and Chamomile A multi-component savory dish designed around contrast: sea sweetness, land richness, unripe-fruit acidity, and floral aromatics.

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    GPT-6.1 Sol: This agreement is heavily weighted toward the Client. Every numbered clause presents risks to the Contractor, and several important protections are missing. This is general contract analysis, not legal advice.

    Qwen3.8 Max: This agreement is heavily one-sided in the Client’s favor. Many clauses are not merely aggressive; they create meaningful opportunities for the Client to expand the work, delay or deny payment, capture the Contractor’s pre-existing intellectual property, impose unlimited liability, and restrict the Contractor’s future…

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GPT-6.1 Sol: The likeliest outcome is not “the world of 1980 arrives in 1953.” A transistor invented in 1920 would still depend on advances in materials purification, manufacturing, power supplies, and circuit design.

    Qwen3.8 Max: A useful way to think about this is not “1980 becomes 2007.” The transistor would accelerate electronics, but every technology has bottlenecks: materials chemistry, precision manufacturing, rockets, batteries, displays, institutional capacity, and war.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    GPT-6.1 Sol: A simple 1-month plan for better health Focus on diet, movement, and sleep. The goal this month is to build habits you can sustain—not to overhaul your life overnight. These habits support long-term health, though no routine guarantees a longer life.

    Qwen3.8 Max: Here’s a simple, beginner-friendly 1-month health and longevity plan focused on three key areas: diet, movement, and sleep/stress. The goal is not perfection, but building small, sustainable habits.

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GPT-6.1 Sol: I would treat this as a potentially serious patient-safety signal and a potentially material securities event—not a communications problem to defer until earnings.

    Qwen3.8 Max: My decision as CEO I would not wait six months. I would treat this as a credible, serious post-marketing safety signal requiring immediate precautionary action, urgent regulator engagement, and public disclosure before the earnings call.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GPT-6.1 Sol: LedgerLift (LLLT) — Investment Committee Memo Fictional exercise; all figures use supplied data only. Financial tables are in $ millions unless noted. Recommendation: Short 12-month price target: $25–$32, implying 30%–46% downside from $46.

    Qwen3.8 Max: INTERNAL IC MEMO — LedgerLift (LLLT) Fictional exercise; not investment advice. Recommendation Recommendation: Short (tactical). 12-month PT range: $35–$40 vs. current $46.

    Every model's answer to this prompt

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Favorites

Movie

Album

Same pick

Book

City

Same pick

Game

GPT-6.1 SolGPT-6.1 Sol

Spirited Away

2001

In Rainbows

Radiohead

Middlemarch

George Eliot

Kyoto

Japan

Outer Wilds

Indie, Adventure

Qwen3.8 MaxQwen3.8 Max

Blade Runner

1982

In Rainbows

Radiohead

Братья Карамазовы

Fiódor Dostoievski

Kyoto

Japan

Tetris (1984)

Puzzle

Price and specs

GPT-6.1 Sol and Qwen3.8 Max compared across 21 shared prompts
SpecGPT-6.1 SolQwen3.8 Max
Input price$2/M tokens$2/M tokens
Output price$10/M tokens$6/M tokens
Context window1.1M tokens1.0M tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedSep 2026Aug 2026
At 10M a month$20.00$20.00$20.00$20.00
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it4 hosts, cheapest first
GPT-6.1 Sol3 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.00 in·$10.00 out·1.1M·100% up
  • OpenAI$2.00 in·$10.00 out·1.1M·99.9% up
  • Amazon Bedrock$2.20 in·$11.00 out·1.1M·87.2% up
Qwen3.8 Max1 host
HostInOutContextUptime
  • Alibaba Cloud$2.00 in·$6.00 out·1M·99.8% up

Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.

Common questions

What is the difference between GPT-6.1 Sol and Qwen3.8 Max?

GPT-6.1 Sol is developed by OpenAI while Qwen3.8 Max is developed by Qwen. GPT-6.1 Sol has a 1.1M token context window vs Qwen3.8 Max's 1.0M. You can compare their actual outputs across 21 challenges on Rival to see how they differ in practice.

Which is better, GPT-6.1 Sol or Qwen3.8 Max?

It depends on your use case. GPT-6.1 Sol and Qwen3.8 Max each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 21 challenges so you can judge which fits your needs best.

How much does GPT-6.1 Sol cost compared to Qwen3.8 Max?

GPT-6.1 Sol costs $2/M input tokens and Qwen3.8 Max costs $2/M input tokens. Qwen3.8 Max 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.

How can I compare GPT-6.1 Sol and Qwen3.8 Max on Rival?

This page shows a side-by-side comparison of GPT-6.1 Sol and Qwen3.8 Max 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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Same lab, same size, long tail

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Model pages

  • GPT-6.1 Sol58 outputs, specs and price
  • Qwen3.8 Max22 outputs, specs and price
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