Muse Ember API Pricing, Context Window and Cost Per Task

How much does Meta's Muse Ember cost?

Muse Ember is reported to cost about 10 cents per million input tokens and 50 cents per million output tokens on Meta's API. It has a 1 million token context window and can write up to 512,000 tokens in a single reply. Meta positions it as its smallest, most efficient model, built for repetitive agent work such as tool calls, coding tasks, data extraction and reading long documents. The pricing comes from a hands-on review, not an official price sheet, so confirm it on Meta's API page before you budget.

What is Muse Ember, and who is it for?

Muse Ember is the smallest model in Meta's Muse family. Meta describes it as built for the work agents repeat all day. An "agent" here means an AI setup that takes a goal, then makes a chain of tool calls (search a file, run a script, edit a document) until the job is done. Agents call the model many times per task, so small per-call savings add up.

It also accepts images, audio and video as input, which matters when the task starts with a screenshot, a diagram or a scanned page.

If you use AI for occasional one-off questions, the price barely matters. If you or your team run the same workflow hundreds of times a week, such as cleaning exports, summarizing documents or triaging requests, the price per task is the number that decides which model you use.

What do the context window and output limit mean in practice?

The context window is how much text the model can consider at once, including your instructions, your documents and its own earlier steps. Muse Ember's is 1 million tokens. A token is roughly three quarters of an English word, so that is on the order of several long books or a very large pile of reports in one request.

The output limit is how much it can write back in one reply. Muse Ember's is 512,000 tokens. Most work never comes near that. It matters when you ask for something large, like a full report, a long script or a big structured data file, and you don't want it cut off halfway.

Two cautions:

• A large window is a ceiling, not a recommendation. Filling it costs money on every call.
• Models can handle long inputs less reliably as they get longer. Test with your own documents.

What does that pricing look like in real dollars?

Per-million prices are hard to picture, so here is the arithmetic at the reported rates ($0.10 per million input, $0.50 per million output). These are illustrations, not measured results.

The agent row shows why agents are different. Each step usually resends the growing conversation, so input tokens pile up fast. Cheap input pricing helps most there.

Output tokens cost five times as much as input tokens at these rates. Prompts that ask for short, structured answers keep the bill down.

How does Muse Ember compare with Claude Haiku and GPT 6 Luna?

Meta shared a chart, with numbers from the independent firm Artificial Analysis, that plots intelligence against the cost of finishing a task. Muse Ember sits in the top left corner, meaning higher intelligence at a lower cost per task than both Claude Haiku and GPT 6 Luna.

Meta also published a benchmark table. According to the reviewer who went through it, Muse Ember beat both rivals on coding, on agentic tasks and on professional work over long documents.

Treat both with care:

• The chart comes from Meta, even though the underlying numbers are Artificial Analysis's. Companies choose which comparisons to show.
• The sources I have don't include exact per-token prices for Haiku or Luna, so I can't give a side-by-side price table. Check Anthropic's and OpenAI's pricing pages for current rates.
• The reviewer is a regular user of both Haiku and Luna and found Muse Ember better in his tests, but that is one person's experience on a handful of tasks.

Why is cost per task different from price per token?

Price per token is the sticker price. Cost per task is what you actually pay to get a finished, correct result. The two can diverge for three reasons:

1. Token usage differs by model. A model that thinks longer or writes more verbosely uses more output tokens for the same job.
2. Retries cost money. If a cheaper model fails a task half the time, you pay twice.
3. Tool calls add up. An efficient agent that reaches the answer in fewer steps costs less than one that wanders.

That is why the Artificial Analysis chart plots cost per task and not per token. The reviewer's point is that Muse Ember's tool calling was quick and didn't waste time, which is what brings cost per task down.

How did Muse Ember do on real work?

The reviewer ran several tests through an agent setup. These are single attempts, not benchmarks, but they show the range.

The pattern: strong on structured, tool-driven, verifiable work. Weaker on creative and cultural judgment. That fits a small model built for agent chores.

What does this mean for you?

If you use AI at work through a company-approved tool, you probably can't pick the model, and this changes little today. It does signal that capable small models are getting cheaper, which tends to show up later as more AI features in the software you already use.

If you build or run automations (report cleanup, document extraction, inbox triage), Muse Ember is worth a test. A model priced at cents per million tokens makes high-volume jobs cheap enough to run on everything instead of a sample.

If you manage a budget, ask for cost per completed task, not cost per token.

A simple test you can copy:

Pick 10 real tasks from your own work (not demos).
Run each through Muse Ember, Claude Haiku and GPT 6 Luna.
For each run, record:
- input tokens and output tokens used
- total cost at the current published prices
- did the result need fixing? (yes/no, and how long)
- number of retries
Compare cost per ACCEPTED result, not cost per run.

Also keep sensitive data out of any test until your company has approved the provider.

Can I build my own cost-per-task tracker?

If you run these comparisons, the log tends to end up in a spreadsheet that nobody maintains. With Remy, you can describe a small internal tool in plain English: a form to log each task, the model used, tokens in and out and whether the result was accepted, plus a dashboard that shows cost per accepted result by model. Remy builds the full-stack app, with a database and sign-in, and deploys it to a live URL. You own the code. Remy bills AI tokens at cost, so the tracker itself doesn't add a markup to your testing.