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  5. Owl AlphavsQwen3.6 35B A3B
Updated Apr 2026

Owl AlphavsQwen3.6 35B A3B

Owl Alpha is cheaper than Qwen3.6 35B A3B at $0/M vs $0.1612/M input tokens.

Owl Alpha and Qwen3.6 35B A3B compared across 54 shared prompts
SpecOwl AlphaQwen3.6 35B A3B
Input priceFree$0.1612/M tokens
Output priceFree$0.9653/M tokens
Context window1.0M tokens262K tokens
Weights—Open
Free API (OpenRouter)NoNo
ReleasedApr 2026Apr 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 54 prompts both answered. Formatting stripped, words untouched.

Realistic AI Interview

Simulate a fake interview with Steve Jobs in 2025 about the future of AI.

Owl Alpha327 words
[Camera clicks on. A minimalist stage. Soft lighting. A familiar black turtleneck. A knowing smile.] Interviewer: Steve, thank you for joining us. The world has changed so much. What’s your first thought about the state of AI in 2025? Steve Jobs: It’s a tool. A profoundly powerful tool. But it’s still just a tool.
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Qwen3.6 35B A3B678 words
[FICTIONAL SIMULATION – NOT A REAL INTERVIEW] This is a speculative reconstruction based on Steve Jobs’ documented philosophies, public interviews, design principles, and Apple’s technological trajectory through 2024–2025. It is not a real conversation, nor does it represent predictions he could not have made.
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Generate a Stand-Up Routine

Write a 3-minute stand-up comedy routine that isn't based on puns.

Owl Alpha207 words
Alright, hello everyone! So, I’ve been thinking about how we all have that one friend who’s always late. Not just late—like, fashionably late. Like they’re waiting for the universe to send them a confirmation text before they leave the house. “Is it okay to arrive yet? Has the planet aligned?
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Qwen3.6 35B A3B517 words
Title: High-Maintenance Electronics Time: Approx. 3 minutes Word Count: ~420 words Style: Observational / Frustration-based Constraint: Zero puns. (Walk to mic, adjust stand. Take a deep breath. Look tired.) I've reached that age where I'm just trying to negotiate with my own life.
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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) –…

Owl Alpha420 words
HarborTech Services (HTS) – LBO Underwriting Memorandum ENTRY & SOURCES/USES Entry Enterprise Value FY2025 EBITDA: $120m Purchase multiple: 12.0x Enterprise Value: $1,440m Transaction Fees 2.0% × $1,440m = $28.8m (funded by equity) Total Sources Required: $1,440m + $28.8m = $1,468.8m Debt at Close Tranche Multiple…
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Qwen3.6 35B A3B538 words
Simplified Year-by-Year Model (FY2026–FY2030) All figures in $m. Assumptions: TL amort = 1.0% of beginning balance; Mezz PIK compounds to principal; ΔNWC = 0.5% of incremental revenue; Cash Tax = 25% × max(0, EBITDA – Cash Interest).
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Adversarial Contract Review

Here is a contract for a software consulting engagement: --- SOFTWARE CONSULTING AGREEMENT This Agreement is entered into between TechCorp…

Owl Alpha1,291 words
Analysis of Exploitable Clauses in Software Consulting Agreement SCOPE — Unilateral Modification Without Compensation Problem: "Client reserves the right to modify the scope at any time without additional compensation" creates an open-ended obligation.
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Qwen3.6 35B A3B1,632 words
Disclaimer: I am an AI assistant, not an attorney. The following analysis is for informational purposes only and does not constitute legal advice. Contract law varies significantly by jurisdiction (e.g., California vs. New York vs. EU).
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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…

Owl Alpha1,063 words
Critical Architecture Analysis: Real-Time Collaborative Document Editor 🔴 CRITICAL ISSUES Stale Data Propagation Between Servers Problem: Server A's clients won't see Server B's changes for up to 2 seconds due to polling delay.
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Qwen3.6 35B A3B1,016 words
Here’s a systematic breakdown of the critical failure modes, race conditions, and scaling bottlenecks in this architecture, along with specific solutions and their trade-offs. 🔴 1. WebSocket Routing & Load Balancing Problem: Round-robin LB + per-server WebSocket state + no sticky sessions.
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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…

Owl Alpha542 words
I'll craft three distinct explanations, each tailored to the audience's background, concerns, and what they'd find compelling. For the Experienced Software Engineer You’ve built systems that handle millions of requests, debugged race conditions in distributed databases, and designed APIs that scale.
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Qwen3.6 35B A3B775 words
For the Experienced Software Engineer Training an LLM is essentially a massively parallelized optimization job. You feed billions of text tokens into a transformer architecture, which computes a differentiable function across ~100B parameters.
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Our Verdict
Owl Alpha
Owl Alpha
Qwen3.6 35B A3B
Qwen3.6 35B A3BRunner-up

Not enough votes to call it. On the specs, Owl Alpha has the edge: bigger model tier, bigger context window.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Owl Alpha
Input
$0.000
Output
$0.000
Qwen3.6 35B A3B
Input
$0.16
Output
$0.97
Where to run it

11 hosts, cheapest first

Owl Alpha

No hosts listed on OpenRouter.

Qwen3.6 35B A3B11 hosts
HostInOutContextUptime
DDarkbloomfp4$0.05 in·$0.70 out·262k·99.3% upAAkashMLfp8$0.10 in·$0.90 out·262k·100% upDDeepInfrafp8$0.10 in·$0.95 out·262k·99.2% upDDekaLLM$0.10 in·$1.00 out·262k·99.9% upVVenicefp8$0.10 in·$1.00 out·256k·99.7% upIio.netfp8$0.13 in·$0.94 out·262k·91.6% up
5 more hostsFewer hosts
PParasailfp8$0.15 in·$1.00 out·262k·99.6% upAAtlasCloudfp8$0.19 in·$1.11 out·262k·96.4% upPPhala$0.20 in·$1.27 out·262k·98.8% upSSiliconFlowfp8$0.24 in·$1.80 out·262k·97.4% upCCoreWeavefp8$0.25 in·$1.25 out·262k·100% up

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

Writing DNA

Style Comparison

Similarity
67%

Qwen3.6 35B A3B uses 2.8x more emoji

Owl Alpha
Qwen3.6 35B A3B
59%Vocabulary59%
21wSentence Length15w
0.56Hedging0.31
4.5Bold4.9
3.1Lists2.7
0.58Emoji1.60
0.88Headings0.75
0.10Transitions0.05
Based on 26 + 27 text responses
Research

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