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  5. GPT-6 SolvsQwen3 Coder Next
Updated Sep 2026

GPT-6 SolvsQwen3 Coder Next

Qwen3 Coder Next is cheaper than GPT-6 Sol at $0.2/M vs $2/M input tokens.

GPT-6 Sol and Qwen3 Coder Next compared across 20 shared prompts
SpecGPT-6 SolQwen3 Coder Next
Input price$2/M tokens$0.2/M tokens
Output price$10/M tokens$1.5/M tokens
Context window1.1M tokens262K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedSep 2026Feb 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 20 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…

GPT-6 Sol90 words
Smoky Sunset Chickpeas Prep: 5 minutes · Cook: 15 minutes · Serves: 2 Ingredients 1 can chickpeas, drained 1 can diced tomatoes 1 tablespoon olive oil 2 garlic cloves, minced 1 teaspoon dried oregano ½ teaspoon smoked paprika ½ teaspoon salt Instructions Heat the olive oil in a skillet over medium heat.
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Qwen3 Coder Next269 words
** Recipe Name: ** 🌟 Golden Honey-Sesame Crisps 🌟 A sweet-and-savory, crunchy snack or dessert topping—ready in 15 minutes with just 7 pantry staples! 📋 Ingredients (All Common Pantry Staples) Rolled oats – ½ cup All-purpose flour – ¼ cup Brown sugar – 2 tbsp (light or dark) Honey – 2 tbsp Sesame seeds – 2 tbsp…
Read the full answer

Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

GPT-6 Sol1,130 words
The central flaw is that the system treats collaborative edits as independent writes to a document. They are not: edits need a shared ordering and a merge protocol. Adding API servers or read replicas will not fix that. Issue and failure mode Specific solution Trade-off Client-clock last-write-wins loses work.
Read the full answer
Qwen3 Coder Next1,536 words
This architecture has several critical flaws that would manifest under real-world usage, especially with multiple concurrent users. Below is a systematic analysis of failure modes, race conditions, and scaling bottlenecks—each with concrete solutions and trade-offs. 🔴 1.
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Explain Like I'm a Specific Expert

Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced…

GPT-6 Sol617 words
Experienced software engineer Think of a language model as a service whose API accepts a sequence of tokens and returns a probability distribution over the next token. During training, it sees enormous numbers of text sequences and adjusts billions of parameters to make the tokens that actually followed more likely.
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Qwen3 Coder Next1,030 words
For the Experienced Software Engineer (Distributed systems & APIs; skeptical of “next-word prediction”) You’re right to be skeptical—*on its surface*, calling LLMs “next-token predictors” sounds reductive, like calling a jet engine “a fancy fan that pulls air.”
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Ethical Dilemma with Stakeholders

You're the CEO of a pharmaceutical company. Your best-selling drug ($2B annual revenue, used by 4 million patients for chronic pain) has…

GPT-6 Sol986 words
I would not wait six months or wait for the earnings call. An internal signal of liver failure in a widely used drug warrants immediate patient-protection measures and prompt contact with regulators, even while causality and the precise risk are being checked.
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Qwen3 Coder Next683 words
CEO Action Plan: Next 48 Hours *Goal: Fulfill fiduciary, ethical, and regulatory duties while protecting patients, long-term value, and stakeholder trust—*not* short-term stock price.* Hour 0–4: Immediate Internal Triage & Secure Data Activate Crisis Response Team (CRT): Convene in person (or secure virtual) with…
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Michelin Star Recipe Design

Design an innovative recipe for a multi-component dish worthy of a Michelin 3-star restaurant. The recipe should feature an unusual…

GPT-6 Sol937 words
Tidal Orchard Seared scallops · strawberry–kombu gel · white-miso and white-chocolate beurre blanc · sea-lettuce oil · buckwheat lace The unexpected pairing is scallop and strawberry. Both have a gentle sweetness, but strawberry’s acidity makes the scallop taste more distinctly of the sea.
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Qwen3 Coder Next1,200 words
Dish Title: “The Silken Confluence of Forest and Sea” Conceptual Narrative: This dish explores the paradoxical harmony between land and ocean—two realms seemingly opposed, yet bound by shared elemental chemistry and sensory resonance.
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Advanced Longevity Plan (Biohacker)

Develop a comprehensive, cutting-edge 3-month longevity plan for a biohacker aiming to optimize longevity, physical and cognitive…

GPT-6 Sol1,402 words
A 12-week longevity and performance plan The highest-confidence longevity strategy is not a large supplement stack. It is consistent sleep, resistance training, aerobic fitness, adequate nutrition, cardiometabolic risk management, and avoiding tobacco.
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Qwen3 Coder Next956 words
The 12-Week Neuro-Longevity Acceleration Protocol (NLAP v2.0) A Science-Backed, Tiered Longevity & Performance System for the Advanced Biohacker Designed for a 35–45-year-old, metabolically healthy, physically active biohacker with no contraindications.
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Our Verdict
GPT-6 Sol
GPT-6 Sol
Qwen3 Coder Next
Qwen3 Coder NextRunner-up

Not enough votes to call it. On the specs, GPT-6 Sol has the edge: newer, bigger context window, major provider backing.

Qwen3 Coder Next costs 6.7x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-6 Sol
Input
$2.00
Output
$10.00
Qwen3 Coder Next
Input
$0.20
10× cheaper
Output
$1.50
6.7× cheaper

Qwen3 Coder Next is cheaper on both: 10× input, 6.7× output.

Where to run it

7 hosts, cheapest first

GPT-6 Sol3 hosts
HostInOutContextUptime
Azure AI Foundry$2.00 in·$10.00 out·1.1M·96.6% upOpenAI$2.00 in·$10.00 out·1.1M·100% upAmazon Bedrock$2.20 in·$11.00 out·1.1M·99.9% up
Qwen3 Coder Next4 hosts
HostInOutContextUptime
PParasailbf16$0.12 in·$0.80 out·262k·100% upSStreamLake$0.18 in·$0.90 out·256k·99.4% upNNovitafp8$0.20 in·$1.50 out·262k·99.5% upAlibaba Cloud$0.30 in·$1.50 out·262k·99.2% up

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

Research

What we learned reading every model

FAQ

Common questions

GPT-6 Sol is developed by OpenAI while Qwen3 Coder Next is developed by Qwen. GPT-6 Sol has a 1.1M token context window vs Qwen3 Coder Next's 262K. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.

It depends on your use case. GPT-6 Sol and Qwen3 Coder Next each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 20 challenges so you can judge which fits your needs best.

GPT-6 Sol costs $2/M input tokens and Qwen3 Coder Next costs $0.2/M input tokens. Qwen3 Coder Next is $1.80/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 GPT-6 Sol and Qwen3 Coder Next 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

GPT-6 Sol logo
GPT-6 Sol20 outputs, specs and price
Qwen3 Coder Next logo
Qwen3 Coder Next53 outputs, specs and price
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