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  5. GPT-5.6 TerravsQwen3.8 Flash
Updated Aug 2026

GPT-5.6 TerravsQwen3.8 Flash

Qwen3.8 Flash is cheaper than GPT-5.6 Terra at $0.15/M vs $2.5/M input tokens.

GPT-5.6 Terra and Qwen3.8 Flash compared across 8 shared prompts
SpecGPT-5.6 TerraQwen3.8 Flash
Input price$2.5/M tokens$0.15/M tokens
Output price$15/M tokens$0.47/M tokens
Context window1.1M tokens1.0M tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedJul 2026Aug 2026
Side by side

Same prompt, both answers

Opening lines from 3 of the 8 prompts both answered. Formatting stripped, words untouched.

Beginner's Longevity Plan

Outline a simple, actionable 1-month plan for someone starting their journey towards better health and longevity. Focus on 3 key areas…

GPT-5.6 Terra989 words
Simple 1-Month Health & Longevity Starter Plan Focus on three high-impact areas: Nutrition: eat mostly whole, minimally processed foods Movement: build a consistent walking + strength habit Sleep & stress: protect sleep and create daily recovery time The goal is consistency, not perfection.
Read the full answer
Qwen3.8 Flash368 words
1-Month Beginner Health & Longevity Plan Focus areas: Nutrition, Movement, Sleep Week 1: Build the basics Nutrition Add 1 serving of vegetables to 2 meals per day. Drink water with each meal. Choose protein at most meals: eggs, beans, tofu, chicken, fish, yogurt. Movement Walk 10–15 minutes daily.
Read the full answer

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-5.6 Terra1,113 words
Experienced software engineer Think of an LLM as a very large, learned function approximator for sequences. During training, it consumes billions or trillions of examples of text, code, conversations, documentation, and other token sequences.
Read the full answer
Qwen3.8 Flash983 words
For an experienced software engineer Training a large language model is not “just” making it guess the next word in isolation. It is fitting an enormous parameterized function to predict the next token conditioned on all previous tokens in a sequence.
Read the full answer

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-5.6 Terra146 words
Golden Crunch Cinnamon Toast Bites Ingredients (7 pantry staples): Bread slices Butter Sugar Ground cinnamon Salt Vanilla extract Honey Prep time: 5 minutes Cook time: 8 minutes Total time: 13 minutes Instructions Make cinnamon sugar: In a small bowl, mix 2 tablespoons sugar, 1 teaspoon cinnamon, and a tiny pinch of…
Read the full answer
Qwen3.8 Flash147 words
Moon Halo Cookies A soft, golden sugar cookie with crisp edges and a tender center—made with just 7 pantry staples. Prep time: 10 minutes Cook time: 10 minutes Total time: 20 minutes Makes: About 16 cookies Ingredients 1/2 cup butter, softened 3/4 cup sugar 1 egg 1 teaspoon vanilla extract 1 1/2 cups all-purpose flour…
Read the full answer
Our Verdict
GPT-5.6 Terra
GPT-5.6 Terra
Qwen3.8 Flash
Qwen3.8 Flash

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

Qwen3.8 Flash costs 32x less per token.

Too close to call
API pricing

Cost per 1M tokens

GPT-5.6 Terra
Input
$2.50
Output
$15.00
Qwen3.8 Flash
Input
$0.15
17× cheaper
Output
$0.47
32× cheaper

Qwen3.8 Flash is cheaper on both: 17× input, 32× output.

Where to run it

4 hosts, cheapest first

GPT-5.6 Terra3 hosts
HostInOutContextUptime
Azure AI Foundry$2.00 in·$12.00 out·1.1M·99.9% upOpenAI$2.00 in·$12.00 out·1.1M·100% upAmazon Bedrock$2.20 in·$13.20 out·1.1M·100% up
Qwen3.8 Flash1 host
HostInOutContextUptime
Alibaba Cloud$0.15 in·$0.47 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GPT-5.6 Terra is developed by OpenAI while Qwen3.8 Flash is developed by Qwen. GPT-5.6 Terra has a 1.1M token context window vs Qwen3.8 Flash's 1.0M. You can compare their actual outputs across 8 challenges on Rival to see how they differ in practice.

It depends on your use case. GPT-5.6 Terra and Qwen3.8 Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 8 challenges so you can judge which fits your needs best.

GPT-5.6 Terra costs $2.5/M input tokens and Qwen3.8 Flash costs $0.15/M input tokens. Qwen3.8 Flash is $2.35/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-5.6 Terra and Qwen3.8 Flash 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-5.6 Terra logo
GPT-5.6 Terra58 outputs, specs and price
Qwen3.8 Flash logo
Qwen3.8 Flash8 outputs, specs and price
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