Sunk Cost sunkcost.ai Data checked 2026-09-03

Qwen3.8 27B vs Muse Glimmer 30B

Qwen3.8 27B scores higher on the intelligence index, 34 against 18. Both take the same machine to start: the cheapest here that runs either is the Framework Desktop, 32GB, at $1,269. On it, Muse Glimmer 30B is about 1.1× quicker: 11 tok/s against 10, both estimated from memory bandwidth. At 500k tokens a day the Framework Desktop, 32GB pays for itself in 17 years running Qwen3.8 27B, against 22 years running Muse Glimmer 30B.

Qwen3.8 27BMuse Glimmer 30B
Intelligence index3418
ClassSonnet-classHaiku-class
Weights16 GB17 GB
Needs at 32k19 GB18 GB
QuantisationQ4_K_MQ4_K_M
Parameters27.8B29.8B
Max context256k128k
API price per 1M$0.32 in / $2.5 out$0.3 in / $1.1 out
LicenceApache 2.0Apache 2.0
Machines here that run it30 of 3730 of 37
Cheapest machine that runs itStrix Halo Framework Desktop, 32GB $1,269Strix Halo Framework Desktop, 32GB $1,269
Summarising not rated not rated
Translation not rated not rated
Everyday coding not rated not rated
Reasoning & maths not rated not rated
Agentic work not rated not rated

Run Qwen3.8 27B on the Framework Desktop, 32GB · or Muse Glimmer 30B

Ratings are coarse on purpose: they say what a model is usable for, not where it places to the decimal.

Side by side on the Framework Desktop, 32GB

The cheapest machine that runs either model is the same one, so this is the pair doing the same work on the same hardware: Strix Halo Framework Desktop, 32GB, at $1,269.

Qwen3.8 27BMuse Glimmer 30B
Speed at 32k10 tok/s estimated11 tok/s estimated
Pay-back on this machinePays back in 17 yearsPays back in 22 years
API cost per month$6.94$5.33

Run Qwen3.8 27B on the Framework Desktop, 32GB · or Muse Glimmer 30B

How much use it takes to pay for the machine

Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both models at the five levels of use the calculator names, on the Framework Desktop, 32GB. Qwen3.8 27B pays for it sooner at every level of use, so which of them to run does not turn on how hard you work it.

A day's useQwen3.8 27BMuse Glimmer 30B
50ka few chats a day166 years222 years
200klight assistant use42 years55 years
1Ma moderate coding-assistant day8.3 years11 years
4Mheavy coding with an agent2.1 years2.8 years
20Magents running most of the day7.0 monthsits ceiling8.9 monthsits ceiling

The Framework Desktop, 32GB cannot generate 20M tokens a day on either model: at most 14.3M on Qwen3.8 27B and 14.9M on Muse Glimmer 30B. Both figures on that row are for the most it can do.

Run Qwen3.8 27B at 20M tokens a day · or Muse Glimmer 30B

Memory is not what separates them

Qwen3.8 27B needs 19 GB of memory at 32k of context and Muse Glimmer 30B needs 18 GB. Every machine priced here that runs one runs the other, at 32k of context. So the choice between them is what each is good at, how fast it runs and what the same work costs on an API, not what you have to buy to hold it.

Qwen3.8 27B is also head to head with DeepSeek V4-Flash above it on the leaderboard, Inkling Small below it and Qwen3 32B, the last-generation Qwen nearest it in size. Muse Glimmer 30B is also head to head with Qwen3.6 35B-A3B above it on the leaderboard and Gemma 4 26B-A4B below it.

The assumptions behind both columns

Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. Where nobody rents an open model by the token, its API prices are the nearest hosted model's, named beside them. Machines are the 37 here with a published price that are still sold. Change any of it in the calculator.

More head to head: every machine that runs Qwen3.8 27B · every machine that runs Muse Glimmer 30B · every other match-up · both against the frontier · the quickest pay-back at each level of use