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  5. GPT-6 LunavsQwen3.8 27B
Updated Sep 2026

GPT-6 LunavsQwen3.8 27B

GPT-6 Luna is cheaper than Qwen3.8 27B at $0.1/M vs $0.4/M input tokens.

GPT-6 Luna and Qwen3.8 27B compared across 20 shared prompts
SpecGPT-6 LunaQwen3.8 27B
Input price$0.1/M tokens$0.4/M tokens
Output price$0.5/M tokens$3/M tokens
Context window1.1M tokens262K tokens
WeightsClosedOpen
Free API (OpenRouter)NoYes (1 provider)
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 27B3,682 words
This is a general contractor-side risk review, not legal advice. The agreement is heavily one-sided and should not be signed as drafted. Many provisions may be negotiable, and some may be unenforceable depending on jurisdiction, but you should not rely on unenforceability as your main protection.
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 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 27B4,268 words
Below is a comprehensive issue register based on the architecture you described. I’m assuming: “change event” can be a small edit, possibly per keystroke or per paragraph. Documents are stored as HTML blobs. Each API server owns only its own WebSocket clients.
Read the full answer

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 27B3,174 words
Tidal Cellar Koji-Cured Scallop, Oyster, Black Garlic, 24-Month Parmigiano, Apple, and Sea Fennel Menu line: Koji-cured sea scallop, oyster-apple gel, charred celeriac, black garlic and 24-month Parmigiano velouté, apple-hazelnut shard, sea fennel–chive oil, black garlic–oak ash.
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Advanced Investment Memo (IC Memo)

You are writing an internal investment committee memo for a long/short equity fund. Company (fictional): LedgerLift (ticker: LLLT) – B2B…

GPT-6 Luna896 words
LedgerLift (LLLT) — Investment Committee Memo Recommendation: Short 12-month price target: $28–$42 At $46, LedgerLift’s price implies continued strong growth and margin expansion; our DCF scenarios value the shares at approximately $17–$42, with the base case near $28.
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Qwen3.8 27B837 words
Fictional internal memo; not investment advice. Recommendation: Short (moderate conviction) 12-month PT range: $30–$44 Thesis: LLLT is a high-quality, cash-rich spend-management SaaS business, but at $46 it embeds a peer EBIT multiple that its 20% FY26E EBIT margin and conservative DCF do not support.
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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 Luna1,874 words
12-week longevity and performance plan This plan is for a generally healthy adult who wants to improve fitness, metabolic health, sleep, and cognitive performance—not a substitute for medical care or a promise to slow aging.
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Qwen3.8 27B3,703 words
90-Day Longevity & Performance Biohacking Protocol Important: This is an advanced, high-intensity optimization plan. It is not medical advice. If you have heart disease, kidney disease, liver disease, diabetes, pregnancy, eating disorders, autoimmune disease, cancer history, are on blood thinners, or take prescription…
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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.…

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 27B2,587 words
The 3 weakest claims are the ones that carry the investment thesis: product feasibility, market size, and proof of demand/valuation. Weakest claim: “MindMeld AI reads your brainwaves to predict what you want to type before you think it” + “94% accuracy. Works with any language, any device.”
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Our Verdict
GPT-6 Luna
GPT-6 Luna
Qwen3.8 27B
Qwen3.8 27BRunner-up

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

GPT-6 Luna costs 6.0x less per token.

Slight edge

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-6 Luna
Input
$0.10
4.0× cheaper
Output
$0.50
6.0× cheaper
Qwen3.8 27B
Input
$0.40
Output
$3.00

GPT-6 Luna is cheaper on both: 4.0× input, 6.0× output.

Where to run it

19 hosts, cheapest first

GPT-6 Luna3 hosts
HostInOutContextUptime
Azure AI Foundry$0.10 in·$0.50 out·1.1M·88.4% upOpenAI$0.10 in·$0.50 out·1.1M·99.9% upAmazon Bedrock$0.11 in·$0.55 out·1.1M·99.8% up
Qwen3.8 27B16 hosts
HostInOutContextUptime
WWafer$0.09 in·$4.40 out·262k·100% upRReka$0.09 in·$4.40 out·262k·99% upDDekaLLM$0.10 in·$4.40 out·262k·99.9% upDDarkbloomfp4$0.10 in·$1.80 out·262k·99.7% upDDeepInfrabf16$0.15 in·$1.88 out·262k·98% upPPhala$0.20 in·$2.08 out·262k·99.1% up
10 more hostsFewer hosts
MMancerfp8$0.20 in·$2.50 out·262k·99.4% upCChutesfp8$0.24 in·$2.20 out·262k·99.1% upPParasailfp8$0.24 in·$2.20 out·262k·100% upAAkashMLfp8$0.25 in·$2.20 out·262k·99.8% upIIonstreamfp8$0.25 in·$2.55 out·262k·97.4% upCCoreWeavefp8$0.40 in·$3.00 out·262k·99.7% upNNovita$0.42 in·$3.00 out·1M·99.8% upAlibaba Cloud$0.42 in·$2.55 out·1M·99.8% upCloudflare Workers AI$0.45 in·$3.20 out·262k·93.5% upVVenicefp8$0.45 in·$3.20 out·262k·98.8% 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 27B is developed by Qwen. GPT-6 Luna has a 1.1M token context window vs Qwen3.8 27B'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 Luna and Qwen3.8 27B 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 27B costs $0.4/M input tokens. GPT-6 Luna is $0.30/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 27B 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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Model pages

GPT-6 Luna logo
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