Muse Glimmer vs GPTsCapability by capability, what moves off ChatGPT and what stays
Meta released Muse Glimmer on August 10, 2026: 30 billion parameters, Apache 2.0, small enough at 4-bit to sit on a 24 GB card. So the question stopped being whether a local model is good enough and became a duller, more useful one. If you already built a custom GPT, which parts of it survive the move?
Published August 11, 2026 by Jim Liu, who maintains the local inference and MCP tooling behind this site
Ports as-is
Your Actions endpoint, image input, and anything that was really about keeping data on your own machines.
You rebuild
Document retrieval, browsing, the code sandbox, voice, and every seat and audit log if more than one person uses it.
Does not port
Image generation, and the GPT Store listing that was bringing you strangers.
Muse Glimmer vs GPTs: check your own build
Tick what your GPT actually uses
Every capability is judged on its own, so the verdict here is built from what you tick rather than from an overall score. Start with the one your GPT would be useless without.
Three builds, computed live from the same rules
Solo knowledge-base GPT
One person, a folder of PDFs, a private assistant that answers from them.
Portable, with one piece to build
2 port · 1 build · 0 blocked
Support GPT with Actions
Six agents, an internal order-lookup API, browsing turned on for docs.
Portable, with 3 pieces to build
1 port · 3 build · 0 blocked
Marketing GPT that draws
Two marketers, image generation in the loop, published to the GPT Store.
2 things do not port
1 port · 1 build · 2 blocked
Will it load
17 GB wanted · 23 GB usable
6 GB spare against 17 GB wanted. Comfortable at the default context ceiling.
Subscription you would stop paying
$240 over 12 months
1 seat on ChatGPT Plus at $20 per seat per month. The other side of that trade is hardware and electricity, which depends on your power price: size that with the self-hosted hardware calculator.
What Meta actually shipped on August 10
Muse Glimmer is a 30 billion parameter dense model from Meta, released under Apache 2.0. It takes interleaved text and images, and Meta names screenshots, charts and documents as the things it expects to be handed. Full precision wants over 55 GB; the 4-bit K-Quant build comes in under 20 GB, which is the number that put it on consumer machines and started this whole conversation.
The part that matters for anyone porting a custom GPT is tool use. Meta describes it as reliable across extended workflows with precise schemas, and says it works with OpenClaw and other agentic orchestration patterns. Meta lists the local runtimes it works with: Ollama, LM Studio, llama.cpp, vLLM, SGLang, Unsloth, ExecuTorch and MLX, plus Together AI, Fireworks AI and OpenRouter as inference partners for people whose laptops are not going to cope.
Meta published relative decode speedups rather than raw throughput: 3.1 times on an RTX 5090, 1.8 times on an M5 Max, 1.5 times on an M4 Max. The baseline those multiply is not stated, so nobody should be quoting a tokens per second figure from them yet, including this page.
Four things a custom GPT hands you that weights do not
Comparisons of local models against ChatGPT tend to run benchmarks and stop there. The benchmark is not what breaks when you switch. What breaks is the scaffolding you never had to think about, because it arrived pre-assembled behind a Create button.
The document store
Uploading a PDF to a custom GPT is one drag. Locally it is a chunking strategy, an embedding model, a vector store and a retrieval prompt, and the quality of your assistant now depends on four decisions you did not previously have to have an opinion about.
The sandbox
Code Interpreter is the piece people underrate. Writing Python is trivial for any competent model. Running arbitrary generated Python on a user file, safely, with a timeout and no network, is a piece of infrastructure.
The browsing pipeline
A search API is the easy half. The hard half is turning a modern web page into something worth spending context on, and doing it fast enough that the answer still feels live.
The front door
A share link, a store listing, an account system, and a bill someone else collects. If your GPT has users who are not you, this is the part of the move that has no technical solution, only a decision.
Side by side on the axes that actually decide it
| Axis | Muse Glimmer | Custom GPTs | Best for |
|---|---|---|---|
| What you are actually getting | 30B dense open weights under Apache 2.0, downloaded once | A configured assistant inside ChatGPT, rented monthly | Glimmer, if the thing you need to own is the model itself |
| Where the prompt goes | Nowhere. It stays on the machine holding the weights | To OpenAI servers, under whatever plan terms you are on | Glimmer, for work that is not allowed to leave the building |
| Images | Reads them. Interleaved text and images, screenshots, charts, documents | Reads and generates them | GPTs, the moment anyone needs a picture produced |
| Calling your own API | Tool calling with schemas held across long workflows | Actions, defined by an OpenAPI schema | A tie, which is why this is the piece that ports cleanly |
| Documents the assistant knows | You run the retrieval layer | Upload files and it handles chunking and search | GPTs, unless you already run a vector store |
| Running code on user files | You host the sandbox and expose it as a tool | Code Interpreter, already sandboxed | GPTs, and it is not close |
| Context ceiling | 131,072 by default, 262,144 maximum, contested by runtime | Whatever the current ChatGPT model exposes on your plan | Glimmer, if you want the ceiling to be a flag you control |
| Memory to run it | 17 GB at 4-bit, 58 GB at BF16 | None. It runs on hardware that belongs to someone else | GPTs, if your machine has 16 GB and a lot of Chrome tabs |
| Getting it in front of other people | You build the front door | A share link, or the GPT Store | GPTs, whenever distribution was the point |
| What it costs at 1 seat for a year | Hardware and electricity, no per-token bill | USD 240 on ChatGPT Plus at USD 20 per month | Depends entirely on whether you already own the machine |
Memory figures are the Unsloth recommended ranges at the default context ceiling. Full precision and 4-bit envelopes are Meta figures.
Muse Glimmer vs GPTs on cost, without the hand waving
One seat on ChatGPT Plus is USD 20 a month, so USD 240 a year. Two or more people means ChatGPT Business, which is USD 25 per seat monthly or USD 20 per seat billed annually, with a two seat minimum. Six seats on monthly billing is USD 1,800 a year, and that is the number local inference is being weighed against.
The honest version of the other side is that nobody can quote it for you. If the 24 GB card is already in the machine under your desk, the marginal cost is electricity and the answer is obvious. If you would be buying that card, the payback maths depends on your power price and how long the hardware stays useful. That calculation belongs in the self-hosted AI hardware calculator, which sizes the rig and the breakeven months properly rather than making this page guess.
The sharpest version of the sceptical case came from the release thread itself. As formerly_proven put it on Hacker News: "4K bucks buys you around 180 months of <insert AI subscription here> with zero upfront cost." Fifteen years of subscription, against hardware that will not be current in three. That comparison loses in two situations: the data is not allowed to leave your machines, or the hardware is already sitting under your desk.
Not ideal for: anyone whose real bill is engineering time. Two evenings rebuilding retrieval costs more than a year of Plus. The switch pays when the reason is privacy, volume, or wanting the model to still work in five years regardless of what a pricing page says then.
If you meant gpt-oss and not custom GPTs
This query gets typed by two different people. One has a custom GPT and wants to know if it can leave ChatGPT, which is everything above. The other means OpenAI open-weight models and wants a straight model against model read.
For that second reader: gpt-oss-20b and gpt-oss-120b are Apache 2.0 as well, so licensing is not the differentiator anyone expected it to be. Per OpenAI, they are sparse mixture-of-experts and text only, where Muse Glimmer is dense and takes images. On hardware, gpt-oss-20b is aimed at roughly 16 GB and gpt-oss-120b at a single 80 GB card, which leaves Muse Glimmer sitting between them at 17 GB for the 4-bit build. If vision matters to your agent loop, that gap is the whole decision. If it does not, you are choosing between two Apache 2.0 models on behaviour, and neither licence nor price will settle it for you.
How these verdicts were decided
Each of the eleven capabilities was checked against what the model documentation actually claims, not against what a local setup could theoretically be made to do. A capability is marked as porting only when the model itself covers it. It is marked as something you build when a working local equivalent exists and can be named. It is marked as not porting when there is no in-model equivalent at all, which happens exactly twice.
The specification claims come from the Meta announcement and the Unsloth model documentation, read on August 11, 2026, the day after release. Where the two disagree in precision, the memory table follows Unsloth because it is per-build rather than a single envelope. The gpt-oss figures in the disambiguation section are the exception: those come from OpenAI gpt-oss release, which is a third source and is flagged here so you do not have to wonder where a number came from.
What this page does not have: throughput measured on our own hardware. The model is one day old. Meta published speed multipliers without naming the baseline, so there is no official tokens per second figure to quote. What does exist is people posting numbers with their hardware attached on release day, which is not the same thing and is labelled as such below.
| Hardware | Setup | Reported by the person running it | Source |
|---|---|---|---|
| RTX 3090, 24 GB | long context | prefill around 1,000 tok/s, decode 75 to 100 tok/s | delicious_apple on Hacker News |
| Radeon 7900XT, 20 GB | UD-Q4_K_XL on llama.cpp with Vulkan | sits at 19 GB VRAM, 700 tok/s prompt, around 36 tok/s generation, revised to around 60 tok/s in a later edit with the vision projector disabled | jakswa on Hacker News |
| Mac Mini, 32 GB | Ollama | good results, with everything running slowly | mark_l_watson on Hacker News |
Self-reported, unaudited, single runs on machines nobody else can inspect. Useful as a rough floor for what a 24 GB card does, useless as a benchmark. Treat them the way you would treat a colleague telling you what they got.
What people said on day one
The Hacker News release thread ran to 605 comments within a day. We pulled all of them through the public Algolia API on August 11 and counted term frequencies, because the result changes how you should read this whole page.
| Term | Comments mentioning it |
|---|---|
| ChatGPT | 0 |
| custom GPT | 0 |
| GPT Store | 0 |
| OpenAI | 16 |
| Qwen | 68 |
Case-insensitive substring counts over the 605 comments that carried text, thread 49241679, pulled August 11, 2026. The people actually running this model on day one were not comparing it to ChatGPT at all. They were comparing it to Qwen3.6-27B, and arguing about memory and download formats. That is worth knowing before you read anyone framing this as a ChatGPT killer, including anyone framing it that way on a page like this one.
Still needs 32-64GB memory to run it locally. 64GB Macbook pro with an M5 chip costs more than 4k Euros in Germany. A more practical model would be a language specific (e.g Python or JVM language) and excellent at tool calling and reasoning. Maybe that way they can shrink it even more.
Optimizing speed is really the way to go. Yet 24GB is not what everyone can afford. Maybe we could take some of those 56tk/s and transfer into some free RAM space using MoE loading ? I'd be glad with a less than 10GB and more than 6tk/s model.
I wish they would release the quantized versions in a safetensor format. Many frameworks can't load PTE and GGUF.
Tried it (the full version, using 120 GB RAM), wasn't impressed, gave it some defective code, and asked it to fix all errors. It kept looping around and around and digging itself deeper and deeper into a rabbit hole; eventually, it got into a "reasoning" discussion about whether a custom compiler was used that supported the wrong syntax...
The progress compared to Qwen3.6 27B is good, not that impressive, it's a 4 months old model. [...] It is very probable that Qwen3.8 27B will crush Glimmer-30B on most benchmarks.
Worth pushing back on one of these while we are quoting it. The 32 to 64 GB figure is higher than any build in the memory table above, and the 120 GB run was full precision, which almost nobody needs. In the same thread delicious_apple reports it running on a 24 GB RTX 3090 at a street price near 1,000 dollars with performance they call acceptable. Both things are true at once, because people quote the number for whichever build they happened to pull. That gap is why the checker above asks for your machine before it says anything encouraging. The download-format complaint is the one that catches people out.
Questions people keep asking
Can Muse Glimmer replace a custom GPT?
It replaces the reasoning half. A custom GPT is a system prompt, a document store, a code sandbox, a browsing pipeline, a share link and a billing relationship, all bundled behind one Create button. Muse Glimmer is the weights. Ticking your capabilities in the checker above shows which of those bundled pieces you would be rebuilding, and roughly what each one costs you in evenings.
What does a custom GPT do that Muse Glimmer cannot?
Two things have no in-model equivalent. Nothing in the release describes image output. Meta describes multimodal input and reasoning over interleaved text and images, so drawing needs a second model sitting beside it. And it has no storefront, so a GPT whose value came from strangers finding it in the GPT Store loses that value the moment it moves to your machine.
How much memory does Muse Glimmer need?
Meta puts full precision above 55 GB and the 4-bit K-Quant build under 20 GB. The Unsloth builds are more granular: roughly 12 to 14 GB at 2-bit, 14 to 15 GB at 3-bit, 17 GB at 4-bit, 20 to 22 GB at 6-bit, 34 GB at 8-bit and 58 GB at BF16. Those are recommended memory figures at the default context ceiling, so a long prompt is charged on top.
Does Muse Glimmer support tool calling the way GPT Actions do?
Yes, and this is the part of a custom GPT that survives the move unchanged. Meta lists reliable tool use with precise schemas held across extended workflows, and says it works with OpenClaw and other agentic orchestration patterns. Your Action was an OpenAPI schema pointing at an endpoint; locally it becomes a tool definition pointing at the same endpoint.
Is Muse Glimmer free for commercial use?
The weights are Apache 2.0, which permits commercial use, modification and redistribution. There is no per-token bill. The cost moves rather than disappearing: you pay for the machine that holds 12 GB or more of weights depending on the build you pick, the electricity to keep it awake, and the hours spent rebuilding the retrieval and sandbox pieces that ChatGPT was quietly running for you.
Did you mean gpt-oss rather than custom GPTs?
People type this query for both. If you meant OpenAI open-weight models, gpt-oss-20b and gpt-oss-120b are also Apache 2.0, but per OpenAI they are sparse mixture-of-experts and text only, where Muse Glimmer is a 30B dense model that takes images. The 20b build is aimed at roughly 16 GB and the 120b build at a single 80 GB card, which puts Muse Glimmer between them on hardware.
What context length does Muse Glimmer run at?
The Unsloth builds list a 131,072 token default with 262,144 as the maximum, and the exact ceiling is contested depending on which runtime you use, so check yours rather than trusting a number in an article. Either way, raising the ceiling is a flag and the cache for a longer prompt is memory charged above the weights figure, which is how a machine that loads the model fine can still fail on your longest document.
Where can I run it if my machine is too small?
Meta lists Together AI, Fireworks AI and OpenRouter as inference partners, so you can call the same weights hosted rather than local. That keeps the Apache 2.0 licensing and the model behaviour but gives back the privacy argument, which for a lot of people was the entire reason they were reading a Muse Glimmer vs GPTs comparison in the first place.
Related tools and guides on OpenAI Tools Hub
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Offline AI Dictation Tools
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