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  5. Muse Glimmer 30BvsQwen3.5 397B A17B
Updated Aug 2026

Muse Glimmer 30BvsQwen3.5 397B A17B

Muse Glimmer 30B is cheaper than Qwen3.5 397B A17B at $0.3/M vs $0.6/M input tokens.

Muse Glimmer 30B and Qwen3.5 397B A17B compared across 13 shared prompts
SpecMuse Glimmer 30BQwen3.5 397B A17B
Input price$0.3/M tokens$0.6/M tokens
Output price$1.2/M tokens$3.6/M tokens
Context window131K tokens262K tokens
Parameters30B397B (17B active)
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedAug 2026Feb 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 13 prompts both answered. Formatting stripped, words untouched.

Ethical Dilemma with Stakeholders

You're the CEO of a pharmaceutical company. Your best-selling drug ($2B annual revenue, used by 4 million patients for chronic pain) has…

Muse Glimmer 30B862 words
You have a patient safety issue, not a PR issue. The 6 month regulatory reporting timeline is the formal process, not the ethical or legal obligation to act now. Waiting for more data while patients continue to be exposed is criminal negligence exposure, not risk mitigation.
Read the full answer
Qwen3.5 397B A17B1,322 words
This is a crisis scenario that defines a CEO's legacy. The decision matrix is clear: Patient safety and regulatory compliance must supersede short-term financial protection. Hiding this information constitutes securities fraud and potential criminal negligence (precedents include Vioxx/Merck and Purdue Pharma).
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…

Muse Glimmer 30B813 words
This architecture will work for a demo but will break badly under real concurrent use. Here are the critical failure modes, race conditions and bottlenecks: WebSocket fan-out is server-local Failure: Step 3 broadcasts only to clients on THAT server.
Read the full answer
Qwen3.5 397B A17B1,249 words
This architecture contains several critical flaws that would prevent it from functioning as a usable real-time collaborative editor. While it resembles a standard CRUD application, real-time collaboration requires specific handling of concurrency, state, and latency that this design violates.
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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…

Muse Glimmer 30B525 words
1. For the experienced software engineer Think of an LLM as a massive, stateless API with no rules, just a huge compressed lookup table learned from data. It learns by training a transformer — essentially a stack of attention layers — as a next-token predictor via gradient descent on trillions of tokens.
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Qwen3.5 397B A17B569 words
The Experienced Software Engineer Think of training an LLM not as "teaching" it, but as extreme lossy compression. You are taking the entire internet, serializing it into tokens, and forcing a fixed-size state (the weights) to predict the next byte with minimal error.
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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…

Muse Glimmer 30B485 words
Here is a simple, beginner-friendly 1-month plan built around 3 foundational pillars for health and longevity. The goal isn't perfection, it's consistency. The 3 Pillars: Move Daily, Eat Whole, Sleep Well Pick 1-2 small habits per pillar to start. Don't try to change everything at once.
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Qwen3.5 397B A17B641 words
Here is a simple, actionable 1-month plan designed to build sustainable habits for health and longevity. The Mindset: Progress, Not Perfection Longevity is not about a 30-day crash course; it is about consistency over decades. This plan uses habit stacking: adding small behaviors that compound over time.
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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…

Muse Glimmer 30B321 words
Dish Name: "Midnight Pantry Pantry Hero Pasta" A creamy-ish, garlicky tomato pasta that tastes like you tried way harder than you did. All from the pantry, no fresh produce needed.
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Qwen3.5 397B A17B284 words
🌟 Golden Hour Glaze Bowls A sweet-and-savory dish that turns humble shelf-stable staples into a crispy, comforting meal. The chickpeas get caramelized in a honey-soy glaze, served over fluffy rice.
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Historical Counterfactual Analysis

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

Muse Glimmer 30B1,004 words
A transistor in 1920 does not mean a 1947 transistor. It means a crude point-contact or junction device built with 1920s materials science, metallurgy, and manufacturing. That is still a game-changer.
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Qwen3.5 397B A17B1,254 words
This scenario posits a 27-year technological head start (1920 vs. 1947). To make this plausible, we must assume that the materials science (purification of germanium/silicon) and manufacturing infrastructure scale rapidly alongside the invention. Here is the traced history of the "Silicon Twenties" through 1980.
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Our Verdict
Muse Glimmer 30B
Muse Glimmer 30B
Qwen3.5 397B A17B
Qwen3.5 397B A17B

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

Muse Glimmer 30B costs 3.0x less per token.

Too close to call
API pricing

Cost per 1M tokens

Muse Glimmer 30B
Input
$0.30
2.0× cheaper
Output
$1.20
3.0× cheaper
Qwen3.5 397B A17B
Input
$0.60
Output
$3.60

Muse Glimmer 30B is cheaper on both: 2.0× input, 3.0× output.

Where to run it

14 hosts, cheapest first

Muse Glimmer 30B4 hosts
HostInOutContextUptime
PPhala$0.30 in·$1.10 out·131k·100% upDDeepInfrabf16$0.30 in·$1.20 out·131k·100% upFFireworks$0.35 in·$1.50 out·131k·100% upTTogether$0.35 in·$1.50 out·131k·99.8% up
Qwen3.5 397B A17B10 hosts
HostInOutContextUptime
Alibaba Cloud$0.39 in·$2.34 out·262k·99.9% upDDeepInfrafp8$0.45 in·$3.00 out·262k·96.8% upPParasailfp8$0.50 in·$3.60 out·262k·98.8% upAAtlasCloudfp8$0.55 in·$3.50 out·262k·96.2% upDDigitalOcean$0.55 in·$3.50 out·131k·91.3% upPPhala$0.55 in·$3.50 out·262k·98.9% up
4 more hostsFewer hosts
GGMI Cloudfp8$0.60 in·$3.60 out·262k·79.7% upNNovita$0.60 in·$3.60 out·262k·97.2% upSStreamLake$0.60 in·$3.60 out·256k·96.9% upVVenice$0.75 in·$4.50 out·128k·73.7% up

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Muse Glimmer 30B is developed by Meta AI while Qwen3.5 397B A17B is developed by Qwen. Muse Glimmer 30B has a 131K token context window vs Qwen3.5 397B A17B's 262K. You can compare their actual outputs across 13 challenges on Rival to see how they differ in practice.

It depends on your use case. Muse Glimmer 30B and Qwen3.5 397B A17B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 13 challenges so you can judge which fits your needs best.

Muse Glimmer 30B costs $0.3/M input tokens and Qwen3.5 397B A17B costs $0.6/M input tokens. Muse Glimmer 30B is $0.30/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 Muse Glimmer 30B and Qwen3.5 397B A17B 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

Muse Glimmer 30B logo
Muse Glimmer 30B13 outputs, specs and price
Qwen3.5 397B A17B logo
Qwen3.5 397B A17B53 outputs, specs and price
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