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  5. MiniMax M2.7vsQwen3.8 Max Prime
Updated Sep 2026

MiniMax M2.7vsQwen3.8 Max Prime

MiniMax M2.7 is cheaper than Qwen3.8 Max Prime at $0.3/M vs $4/M input tokens.

MiniMax M2.7 and Qwen3.8 Max Prime compared across 19 shared prompts
SpecMiniMax M2.7Qwen3.8 Max Prime
Input price$0.3/M tokens$4/M tokens
Output price$1.2/M tokens$12/M tokens
Context window205K tokens1.0M tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedMar 2026Sep 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 19 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…

MiniMax M2.7417 words
“Golden Harvest Chickpea Stew” Hearty, spiced, and ready in about 40 minutes—perfect for a quick weeknight dinner using only pantry staples. Item Details Prep Time 10 minutes Cook Time 30 minutes Total Time 40 minutes Servings 4 (about 1 ½ cups each) Vegan / Gluten‑Free Yes (just make sure your canned goods are…
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Qwen3.8 Max Prime223 words
🍯 Golden Hour Noodles A sweet, savory, garlicky pasta that tastes like it took way more effort than it did. Ingredients (7) # Ingredient Amount 1 Spaghetti 8 oz (half a box) 2 Butter 3 tablespoons 3 Garlic, minced 4 cloves 4 Honey 2 tablespoons 5 Soy sauce 1 tablespoon 6 Red pepper flakes ½ teaspoon 7 Parmesan cheese…
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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…

MiniMax M2.7988 words
1. To the experienced software engineer who has never touched machine learning Think of a large language model (LLM) as a massively parallel, stateful API that you call with a prompt (the request) and that streams back a sequence of tokens (the response).
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Qwen3.8 Max Prime1,147 words
For the Software Engineer Think of it this way: you've built systems where simple rules at the node level produce emergent behavior at the system level — consensus protocols, eventual consistency, cellular automata-like patterns in distributed state machines. An LLM is a similar phenomenon at a different scale.
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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…

MiniMax M2.7953 words
🚀 1‑Month “Health & Longevity” Kick‑Start Below is a beginner‑friendly, 3‑area plan (Nutrition, Movement, and Sleep + Stress). Each week builds on the last, so you’ll never feel overwhelmed. Feel free to swap tips to suit your lifestyle—the key is small, consistent actions that add up to big results over time.
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Qwen3.8 Max Prime473 words
Your 1-Month Foundation Plan A simple, progressive plan built on three pillars. Each week, you add one small layer. No perfection required—just consistency. 🏃 Pillar 1: Movement Why: Even modest activity reduces all-cause mortality risk and improves mood, sleep, and energy within days.
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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…

MiniMax M2.71,586 words
Architecture Analysis: Real-Time Collaborative Document Editor Your architecture contains fundamental design issues that will cause data loss, inconsistency, and scaling failures under real-world conditions. Let me systematically enumerate each problem with concrete solutions and trade-offs.
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Qwen3.8 Max Prime2,271 words
Architecture Analysis: Failure Modes, Race Conditions & Scaling Bottlenecks SYNC & CONSISTENCY LAYER 1.1 Last-Write-Wins with Client Clocks (Critical) Failure Mode: Client clocks are unreliable. A user with a clock skewed +5 minutes will always "win" conflicts.
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Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

MiniMax M2.7715 words
HARBORTECH SERVICES (HTS) – 5‑year LBO underwriting ($ m) FY 2026 FY 2027 FY 2028 FY 2029 FY 2030 Revenue 972.0 1 040.0 1 102.4 1 157.6 1 215.4 EBITDA 136.1 156.0 176.4 191.0 206.6 Cash‑interest – Term Loan (9 %) 43.2 41.0 37.6 32.8 26.8 Cash‑interest – Mezz (12 %) 21.6 22.0 22.5 22.9 23.4 Total cash interest 64.8 63.1…
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Qwen3.8 Max Prime673 words
HarborTech Services (HTS) – LBO Underwriting Memo Entry & Capital Structure Item Amount FY2025 EBITDA $120.0m Entry Multiple 12.0x Enterprise Value $1,440.0m Transaction Fees (2.0% of EV) $28.8m Total Uses $1,468.8m Term Loan (4.0x, 9.0% cash, 1.0% amort) $480.0m Mezzanine (1.5x, 12.0% cash + 2.0% PIK) $180.0m Sponsor…
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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.…

MiniMax M2.71,561 words
Below are the three claims in the MindMeld AI deck that are most likely to raise red‑flags with a savvy investor. For each one I’ll spell out exactly why the statement is weak and then give a concrete, actionable fix you can use to back it up or re‑frame it.
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Qwen3.8 Max Prime834 words
The 3 Weakest Claims in the MindMeld AI Pitch The TAM Calculation (Slide 4) The Claim: "We're targeting the 3.5 billion smartphone users worldwide. TAM: $180B." Why it's weak: This is a textbook example of TAM inflation that will immediately erode investor trust.
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Our Verdict
Qwen3.8 Max Prime
Qwen3.8 Max Prime
MiniMax M2.7
MiniMax M2.7Runner-up

Not enough votes to call it. On the specs, Qwen3.8 Max Prime has the edge: newer, bigger context window.

MiniMax M2.7 costs 10x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

MiniMax M2.7
Input
$0.30
13× cheaper
Output
$1.20
10× cheaper
Qwen3.8 Max Prime
Input
$4.00
Output
$12.00

MiniMax M2.7 is cheaper on both: 13× input, 10× output.

Where to run it

9 hosts, cheapest first

MiniMax M2.78 hosts
HostInOutContextUptime
GGMI Cloudfp8$0.21 in·$0.84 out·197k·99.7% upMMara$0.24 in·$0.96 out·197k·90.6% upNNovitafp8$0.27 in·$1.08 out·205k·99.6% upAAtlasCloudfp8$0.30 in·$1.20 out·197k·98.7% upMiniMaxfp8$0.30 in·$1.20 out·205k·99.6% upDDeepInfrafp8$0.38 in·$1.70 out·197k·91.2% up
2 more hostsFewer hosts
Groq$0.60 in·$1.80 out·197k·98.8% upSSambaNova$0.60 in·$2.40 out·197k·94.9% up
Qwen3.8 Max Prime1 host
HostInOutContextUptime
Alibaba Cloud$4.00 in·$12.00 out·1M·99.9% up

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

Research

What we learned reading every model

FAQ

Common questions

MiniMax M2.7 is developed by MiniMax while Qwen3.8 Max Prime is developed by Qwen. MiniMax M2.7 has a 205K token context window vs Qwen3.8 Max Prime's 1.0M. You can compare their actual outputs across 19 challenges on Rival to see how they differ in practice.

It depends on your use case. MiniMax M2.7 and Qwen3.8 Max Prime each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 19 challenges so you can judge which fits your needs best.

MiniMax M2.7 costs $0.3/M input tokens and Qwen3.8 Max Prime costs $4/M input tokens. MiniMax M2.7 is $3.70/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 MiniMax M2.7 and Qwen3.8 Max Prime 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

MiniMax M2.7 logo
MiniMax M2.753 outputs, specs and price
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Qwen3.8 Max Prime19 outputs, specs and price
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