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  5. GPT-5.4 ProvsQwen3.6 Max Preview
Updated Apr 2026

GPT-5.4 ProvsQwen3.6 Max Preview

Qwen3.6 Max Preview is cheaper than GPT-5.4 Pro at $1.04/M vs $30/M input tokens.

GPT-5.4 Pro and Qwen3.6 Max Preview compared across 17 shared prompts
SpecGPT-5.4 ProQwen3.6 Max Preview
Input price$30/M tokens$1.04/M tokens
Output price$180/M tokens$6.24/M tokens
Context window1.1M tokens262K tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedMar 2026Apr 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 17 prompts both answered. Formatting stripped, words untouched.

Mini LBO Underwrite

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

GPT-5.4 Pro731 words
Below is a simplified LBO build for HarborTech Services (HTS). All figures in $m unless noted. Rounding: tables are rounded; IRR/MOIC use unrounded math. Entry / Sources & Uses FY2025 EBITDA: 120.0 Purchase price: 12.0x EBITDA → Enterprise value = 12.0 × 120.0 = 1,440.0 Transaction fees: 2.0% of EV → 28.8 Total uses =…
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Qwen3.6 Max Preview630 words
Entry & Capital Structure Math Entry EV = 12.0x × $120m EBITDA = $1,440.0m Transaction Fees = 2.0% × $1,440m = $28.8m (equity funded) Total Uses = $1,440.0m + $28.8m = $1,468.8m Debt Raised = 5.5x × $120m = $660.0m Term Loan: 4.0x = $480.0m (9.0% cash, 1.0% mandatory amort = $4.8m/yr) Mezzanine: 1.5x = $180.0m (12.0%…
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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-5.4 Pro1,064 words
LedgerLift (LLLT) — IC Memo Recommendation: Pass 12-month PT range: $40–$47 2-sentence thesis: LedgerLift looks like a good business but only an average stock here: retention is strong (94% GRR, 123% NRR), the model is mostly subscription, and margins are inflecting, but at $46 the shares already discount a lot of the…
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Qwen3.6 Max Preview876 words
TO: Investment Committee FROM: Equity Research SUBJECT: LedgerLift (LLLT) – Short Recommendation DATE: October 24, 2024 Recommendation & Thesis Recommendation: SHORT 12-Month PT Range: $26–$30 Thesis: LLLT trades at a premium multiple that prices in sustained >20% growth and flawless margin expansion, but our model…
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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-5.4 Pro2,331 words
This architecture will work for a demo, but it has major correctness, latency, and security problems for a Google-Docs-like editor. Highest-risk flaws Client-clock last-write-wins will lose edits and can be gamed by bad clocks.
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Qwen3.6 Max Preview1,007 words
Here’s a systematic breakdown of the architectural flaws, categorized by domain. Each item includes the failure mode/race condition/bottleneck, root cause, proposed solution, and explicit trade-offs. 🔌 1.
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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-5.4 Pro609 words
The 3 weakest claims are the ones that are both most extraordinary and least well-supported. Weak claim Why it’s weak How to strengthen it 1) “MindMeld AI reads your brainwaves to predict what you want to type before you think it.” This is the biggest credibility risk in the deck.
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Qwen3.6 Max Preview883 words
Here are the 3 weakest claims in the pitch, why they raise red flags for investors, and how to fix them with concrete, investor-ready improvements. 🔹 1. Technical Claim: "94% accuracy decoding neural patterns into text. Works with any language, any device."
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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-5.4 Pro2,426 words
Below is a 12-week, high-performance longevity protocol designed for a healthy biohacker who wants to improve lifespan-relevant markers, physical performance, and cognitive output at the same time. Use it as a data-driven template, not dogma.
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Qwen3.6 Max Preview1,499 words
🧬 3-Month Cutting-Edge Longevity & Performance Protocol for Biohackers ⚠️ Medical Disclaimer: This protocol is for educational purposes and assumes you are a healthy adult with medical clearance. Fasting >24h, high-dose supplementation, intense training, and neurofeedback carry risks.
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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-5.4 Pro1,913 words
Below is a dish designed in the language of a contemporary 3-star tasting menu: precise, seasonal, technically layered, and centered on a pairing that feels surprising at first, then inevitable.
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Qwen3.6 Max Preview1,276 words
Dish Title: Abyss & Canopy Langoustine, Black Garlic, Wild Blueberry, Fermented Spruce 📖 Conceptual Narrative Abyss & Canopy explores the liminal space where the boreal forest meets the cold northern ocean.
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Our Verdict
GPT-5.4 Pro
GPT-5.4 Pro
Qwen3.6 Max Preview
Qwen3.6 Max Preview

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

Qwen3.6 Max Preview costs 29x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-5.4 Pro
Input
$30.00
Output
$180.00
Qwen3.6 Max Preview
Input
$1.04
29× cheaper
Output
$6.24
29× cheaper

Qwen3.6 Max Preview is cheaper on both: 29× input, 29× output.

Where to run it

3 hosts

GPT-5.4 Pro2 hosts
HostInOutContextUptime
Azure AI Foundry$30.00 in·$180.00 out·1.1M—OpenAI$30.00 in·$180.00 out·1.1M·98.4% up
Qwen3.6 Max Preview1 host
HostInOutContextUptime
Alibaba Cloud$1.03 in·$6.16 out·262k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 16 Sep 2026.

Writing DNA

Style Comparison

Similarity
50%

Qwen3.6 Max Preview uses 188.4x more emoji

GPT-5.4 Pro
Qwen3.6 Max Preview
50%Vocabulary62%
35wSentence Length21w
0.70Hedging0.22
7.5Bold3.8
8.3Lists2.9
0.00Emoji1.88
2.52Headings0.62
0.01Transitions0.02
Based on 12 + 26 text responses
Research

What we learned reading every model

FAQ

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Model pages

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