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  5. GPT-6 LunavsMistral Large
Updated Sep 2026

GPT-6 LunavsMistral Large

GPT-6 Luna is cheaper than Mistral Large at $0.1/M vs $8/M input tokens.

GPT-6 Luna and Mistral Large compared across 20 shared prompts
SpecGPT-6 LunaMistral Large
Input price$0.1/M tokens$8/M tokens
Output price$0.5/M tokens$24/M tokens
Context window1.1M tokens32K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedSep 2026Feb 2024
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 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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Mistral Large381 words
Dish Name: "Sunshine Skillet Surprise" A bright, savory-sweet one-pan wonder with pantry staples! Ingredients (7 common pantry items): 1 can (15 oz) black beans (drained & rinsed) 1 can (15 oz) diced tomatoes (or crushed tomatoes) 1 cup uncooked white rice (or instant rice for faster cooking) 1 small onion (or 1 tbsp…
Read the full answer

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.
Read the full answer
Mistral Large1,965 words
This contract is heavily one-sided in favor of the Client (TechCorp Solutions Inc.) and contains multiple clauses that could be exploited, abused, or used to unfairly disadvantage the Contractor. Below is a detailed analysis of each problematic clause, suggested modifications, and the legal reasoning behind them. 1.
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?
Read the full answer
Mistral Large920 words
Here are the three weakest claims in MindMeld AI’s pitch deck, along with explanations for their weaknesses and concrete improvements: 1. Weak Claim: "94% accuracy" (Slide 3 – Solution) Why it’s weak: Lack of context: Accuracy metrics in BCI (Brain-Computer Interface) research are highly dependent on the task, dataset…
Read the full answer

Historical Counterfactual Analysis

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

GPT-6 Luna1,472 words
The most important caveat is that inventing a transistor is not the same as inventing the modern semiconductor industry. A device demonstrated in 1920 might be a crude, low-power, unreliable point-contact transistor.
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Mistral Large3,007 words
The invention of the transistor in 1920—nearly three decades earlier than its actual debut in 1947—would have had profound and cascading effects on technology, economics, and geopolitics by 1980. Below is a detailed breakdown of the first-, second-, and third-order implications, organized by domain. 1.
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.
Read the full answer
Mistral Large1,441 words
Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutions and their trade-offs: 1.
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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Mistral Large1,349 words
Michelin 3-Star Dish: *"Ocean’s Whisper & Earth’s Embrace" A symphony of contradictions—briny depths meet terranean warmth, where the ephemeral meets the eternal. Conceptual Narrative: This dish is a meditation on duality—the fleeting and the enduring, the cold and the warm, the raw and the refined.
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Our Verdict
GPT-6 Luna
GPT-6 Luna
Mistral Large
Mistral LargeRunner-up

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

GPT-6 Luna costs 48x 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
80× cheaper
Output
$0.50
48× cheaper
Mistral Large
Input
$8.00
Output
$24.00

GPT-6 Luna is cheaper on both: 80× input, 48× output.

Where to run it

4 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
Mistral Large1 host
HostInOutContextUptime
Mistral$2.00 in·$6.00 out·128k·100% 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 Mistral Large is developed by Mistral AI. GPT-6 Luna has a 1.1M token context window vs Mistral Large's 32K. 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 Mistral Large 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 Mistral Large costs $8/M input tokens. GPT-6 Luna is $7.90/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 Mistral Large 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
GPT-6 Luna20 outputs, specs and price
Mistral Large logo
Mistral Large59 outputs, specs and price
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