Muse Ember: How to Choose It, How to Tune It, and Where the Price Stands

Short answer

Meta released Muse Ember (muse-ember) on November 9, 2026, describing it as its smallest and most cost-efficient model to date.

Most people searching for a comparison want a Meta-internal lineup: here is the cheap tier, here is the strong tier, here is whether the price gap is worth it. That table does not exist in this release. One model shipped. No other Meta model was named, priced or benchmarked.

So "how to choose" collapses into three questions the material can actually answer:

• Is there a sibling tier? No. The only cross-vendor reference points are Claude Haiku 5.5 and GPT-6 Luna, and the only claim is that Muse Ember delivers more intelligence per dollar — with no numbers attached.
• How do you tune this one model? One knob: reasoning effort, from minimal to max.
• What do you run on TeamoRouter instead? Nothing from the same vendor, because there is nothing.

muse-ember is not on TeamoRouter yet. Once it is, the same key and the same base URL with model: muse-ember is the whole change. TeamoRouter carries no Meta models today, so there is no stand-in to recommend.

The specs, mapped to decisions

Take every claim in the release and ask what decision it informs:

Choosing within Meta's lineup: not a question yet

The obvious question — "Meta has other models, which do I take?" — has no answer here. No second Meta model appears, neither stronger nor cheaper, and not even a predecessor is named.

Two practical consequences:

1. Treat any "Muse family comparison" you find as unverified. The release contains no family, just this one model. If a table sets Muse Ember beside some larger "Muse" variant, check where its numbers came from.
2. Reason back from the task instead. Without a sibling to anchor on, the only useful questions are: how shallow a reasoning pass can this output tolerate, and how many calls will you make? Both are covered below.

If Meta later publishes a model page, the axes worth comparing on are the four already in this release: positioning, context, reasoning-effort range and price per million tokens. A comparison built on anything else is probably assembled elsewhere.

Against Claude Haiku 5.5 and GPT-6 Luna: one sentence

The release's only head-to-head is a single line: Meta says Muse Ember delivers more intelligence per dollar than Claude Haiku 5.5 or GPT-6 Luna.

What you can read into it is intent. Meta is planting the model in the cheap, fast, high-volume bracket, aimed at those two vendors' lightweight models rather than their flagships.

What you cannot read into it is considerably more:

• No prices for either competitor. Intelligence per dollar is a ratio, and half of it is missing.
• No benchmark results. Not one named eval, not one score.
• No specs for those models. So multimodal input and tool calling cannot be lined up either.

Use the sentence for direction, not for procurement. A real three-way comparison needs three price sheets and three sets of evals, and none of them are in this release.

Inside the model, there is only one real knob

There is no small-cup/large-cup choice. The only thing you can adjust is reasoning effort, and the published range is minimal to max.

A few cautions, mainly so nobody over-reads the examples:

• The default is not stated. Do not assume an omitted value lands on a particular level. Pass it explicitly, or run a batch at a couple of levels against real traffic first.
• Lower is often right. Its home turf is bounded work repeated dozens of times per run — a helper function, a single tool call, a document summary. Maxing effort on those spends thinking tokens for little quality back.
• Do not push it uphill. Meta positions it as lightweight reasoning. Problems that need a flagship belong on a flagship; no effort setting closes that gap.

A workable pattern: make reasoning effort a per-task-type constant rather than one global value. When a task class starts showing retries, raise only that class.

What it is for

The release is specific about the workload, so match against it directly:

• High-frequency sub-tasks inside one agent run — writing a helper function, editing a small block, making a tool call.
• Document analysis — long documents summarised or extracted in one pass. The 1M window exists for this.
• Mixed input in a single run — code, screenshots, documents and clips sharing one window, no repeated re-chunking.

The release says nothing about what it does not support. Image generation, audio, caching and batching are all absent from both source documents, so this article will not fill them in. Before you depend on any of them architecturally, wait for the official model page's spec table.

One point deserves its own line: the model is designed to sit in a position called dozens of times per agent run. The more internal that position, the more the unit price drives total cost — and the unit price is the one thing not published. What you can settle today is where it goes in the pipeline, not how much cheaper it makes that step.

What to do now

muse-ember is not in TeamoRouter's catalog. Three things are worth doing ahead of it:

1. Make the model name a config value so launch day is a one-line change.
2. Make reasoning effort a config value too, set per task type rather than hardcoded.
3. Have the account and balance ready — top up from $5 and it runs the day it lands.

Do not go hunting for a stand-in in the meantime. TeamoRouter has no Meta models, and another vendor's lightweight model will differ on context length, tool-calling conventions and default reasoning effort. An agent tuned for Muse Ember will need re-tuning after the swap — that is a fresh integration, not a rename.

Calling it on TeamoRouter (once listed)

from openai import OpenAI

client = OpenAI(base_url="https://api.teamorouter.com/v1", api_key="YOUR_KEY")
resp = client.chat.completions.create(
    model="muse-ember",
    messages=[{"role": "user", "content": "Deduplicate the strings in this file, preserving order"}],
)
print(resp.choices[0].message.content)

muse-ember is not on TeamoRouter yet. Once it is live, the same key and model: muse-ember is all you change; until then there is no Meta model here to fall back to, so no substitute to recommend.

How to pay

No subscription: top up your balance in the console from $5, billed by the tokens each request uses. Cards are supported, and so are USDT, USDC and ETH. The same key also calls Claude and GPT models.

New accounts start with a $0 balance — top up before your first request. Top up from $5. If a request returns 402 (400 on the OpenAI format) with "wallet balance is insufficient", your balance is empty — your base URL and key are fine. Top up and retry.

FAQ

Are there other Muse models to choose between?

Not in this release. One model shipped, and no other Meta model's name, price or capability was included, so an intra-vendor comparison is not possible yet.

How does it actually stack up against Claude Haiku 5.5 and GPT-6 Luna?

The release contains one sentence: Meta says it offers more intelligence per dollar than both. There are no benchmarks, prices or specs for those two, so it reads as positioning, not a result.

Is it the cheapest option?

Meta says it is its own cheapest model, which is a claim about Meta's lineup. Across vendors it cannot be checked — the API price is unpublished, and "more intelligence per dollar" has no figure to compute.

Which reasoning-effort level should I use?

The range is minimal to max. Use low levels for the bounded tasks it repeats, and raise them where retries show up. The default is not documented, so pass the value explicitly.

Can it handle long chain-of-thought work?

Meta frames it as lightweight reasoning for high-frequency tasks inside an agent run. Long, hard problems are not its use case, and no effort setting makes up the difference.

Can I call it on TeamoRouter today?

No — muse-ember is not in the catalog. Once listed, it is the same key and base URL with only model changed.

What should I use in the meantime?

Nothing from the same vendor, because there is nothing. TeamoRouter carries no Meta models, and a cross-vendor lightweight model differs in context, tool-calling conventions and default effort. The cheaper move is to make both the model name and effort level configurable and wait for launch.

Related reading

• Muse Ember API is out: model ID, what Meta did and did not say about price, and how to call it
• Using Muse Ember without an official account: the routes available without vendor credentials or an overseas card