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  5. GPT-6 LunavsQwen3.8 2.4T A95B
Updated Sep 2026

GPT-6 LunavsQwen3.8 2.4T A95B

GPT-6 Luna is cheaper than Qwen3.8 2.4T A95B at $0.1/M vs $2/M input tokens.

GPT-6 Luna and Qwen3.8 2.4T A95B compared across 20 shared prompts
SpecGPT-6 LunaQwen3.8 2.4T A95B
Input price$0.1/M tokens$2/M tokens
Output price$0.5/M tokens$6/M tokens
Context window1.1M tokens1.0M tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedSep 2026Aug 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 20 prompts both answered. Formatting stripped, words untouched.

Adversarial Contract Review

Here is a contract for a software consulting engagement: --- SOFTWARE CONSULTING AGREEMENT This Agreement is entered into between TechCorp…

GPT-6 Luna2,040 words
This agreement is strongly one-sided. The enforceability of particular terms—especially the non-compete, liability provisions, and arbitration clause—depends on the governing law and the facts. The agreement does not identify a governing law, which is itself a significant gap.
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Qwen3.8 2.4T A95B6,649 words
This agreement is heavily one-sided and, as drafted, gives the Client multiple ways to extract value from the Contractor while minimizing its own obligations. Some provisions may be unenforceable or limited by law in certain jurisdictions, but even unenforceable clauses can be exploited commercially because the…
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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-6 Luna113 words
Crispy Parmesan Chickpeas A crunchy, savory snack with a cheesy finish Ingredients (7): 1 can chickpeas, drained and dried 1 tablespoon olive oil ½ teaspoon salt ½ teaspoon paprika ¼ teaspoon garlic powder ¼ cup grated Parmesan 1 tablespoon lemon juice Prep time: 5 minutes Cook time: 30–35 minutes Instructions: Heat…
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Qwen3.8 2.4T A95B166 words
Lighthouse Lemon-Garlic Tuna Pasta A savory, pantry-friendly pasta that’s bright, garlicky, and satisfying. 7 Pantry Ingredients 8 oz spaghetti or linguine 3 tablespoons olive oil 3 garlic cloves, thinly sliced 2 cans tuna, drained 2 tablespoons lemon juice ½ teaspoon red pepper flakes ½ teaspoon salt Water for boiling…
Read the full answer

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.…

GPT-6 Luna421 words
“94% accuracy” and “predict what you want to type before you think it” Why it’s weak: This is the deck’s central technical claim, but “accuracy” is undefined: Is it word-level or character-level accuracy, measured on a fixed vocabulary or open-ended text, and under what conditions?
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Qwen3.8 2.4T A95B1,709 words
The three weakest claims are the ones most likely to collapse under investor diligence: “before you think it,” “94% accuracy… any language, any device,” and the $180B TAM claim. They are weak because they are either scientifically implausible, under-specified, or logically inconsistent with the market evidence cited.
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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 Luna1,930 words
I would not wait six months for more data. A potentially fatal signal affecting a drug used by millions requires immediate safety assessment, prompt engagement with regulators, and a plan to inform patients and clinicians.
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Qwen3.8 2.4T A95B5,331 words
My position as CEO I would treat this as a patient-safety, regulatory, legal, corporate-governance, and financial crisis simultaneously. The core decision is not “disclose versus protect the stock price.”
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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 Luna1,979 words
The main architectural risk is that the system treats collaborative editing as “write a value, then broadcast it.” That works poorly under concurrency, server failures, reconnects, and scale.
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Qwen3.8 2.4T A95B3,715 words
The architecture as written has several P0 correctness problems that can cause lost edits, divergent documents, and security/staleness issues, plus scaling bottlenecks that get worse as you add more API servers. Below is a detailed failure-mode review, grouped by category.
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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 Luna1,249 words
Turbot, White Asparagus, Green Strawberry Kosho & Toasted-Hay Beurre Blanc A composed spring dish for four, built around an unusual pairing: tart green strawberry and toasted hay.
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Qwen3.8 2.4T A95B2,994 words
Cliff Nest Koji-Aged Squab, Sea Urchin, Rhubarb, Black Garlic Yield: 4 tasting portions Style: Contemporary fine-dining main course Key unusual pairing: Koji-aged pigeon/squab with sea urchin, bridged by rhubarb acidity and black-garlic depth Conceptual Narrative Cliff Nest imagines a coastal nesting site in early…
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Our Verdict
GPT-6 Luna
GPT-6 Luna
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B

Not enough votes to call it. On the specs, nothing separates them.

GPT-6 Luna costs 12x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-6 Luna
Input
$0.10
20× cheaper
Output
$0.50
12× cheaper
Qwen3.8 2.4T A95B
Input
$2.00
Output
$6.00

GPT-6 Luna is cheaper on both: 20× input, 12× output.

Where to run it

10 hosts, cheapest first

GPT-6 Luna3 hosts
HostInOutContextUptime
Azure AI Foundry$0.10 in·$0.50 out·1.1M·100% upOpenAI$0.10 in·$0.50 out·1.1M·100% upAmazon Bedrock$0.11 in·$0.55 out·1.1M·99.9% up
Qwen3.8 2.4T A95B7 hosts
HostInOutContextUptime
Alibaba Cloud$2.00 in·$6.00 out·1M·99.8% upDDeepInfrafp4$2.00 in·$6.00 out·262k·100% upModal$2.00 in·$6.00 out·1M·99.7% upNNovita$2.00 in·$6.00 out·1M·99.9% upSSiliconFlowfp8$2.00 in·$6.00 out·1M·99.9% upTTogether$2.00 in·$6.00 out·1M·100% up
1 more hostFewer hosts
VVenice$2.00 in·$6.00 out·262k·99.6% 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 Luna is developed by OpenAI while Qwen3.8 2.4T A95B is developed by Qwen. GPT-6 Luna has a 1.1M token context window vs Qwen3.8 2.4T A95B's 1.0M. 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 Luna and Qwen3.8 2.4T A95B 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 Luna costs $0.1/M input tokens and Qwen3.8 2.4T A95B costs $2/M input tokens. GPT-6 Luna is $1.90/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 Luna and Qwen3.8 2.4T A95B 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 Luna logo
GPT-6 Luna20 outputs, specs and price
Qwen3.8 2.4T A95B logo
Qwen3.8 2.4T A95B58 outputs, specs and price
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