Meta Muse Ember vs Claude Haiku 5.5 vs GPT-6 Luna: Small Model Comparison

What this comparison covers

Three small, cheap models are competing for the same job: the high-volume, repetitive work that sits underneath a larger system. Meta Muse Ember, Claude Haiku 5.5, and GPT-6 Luna are all pitched at that tier. The headline claim is that Muse Ember lands level with Haiku 5.5 on the Artificial Analysis index while costing roughly a third as much.

That claim matters because small models are rarely used once. They run thousands of times a day: classifying tickets, extracting fields from documents, drafting routine replies, filling forms. At that volume, a cost difference of two thirds changes what you can afford to automate.

It is worth being precise about what an index score does and does not tell you. The Artificial Analysis index is an aggregate. It blends several evaluations into one number so models can be compared quickly. Two models with the same aggregate can still behave very differently on your specific task, because the aggregate hides which individual evaluations drove the result. Treat the index as a reason to run a test, not as a verdict.

How the three models line up

Here is a comparison frame you can fill in. The only cell the source material supports with confidence is the Muse Ember versus Haiku 5.5 relationship on the index and on cost.

Fill in the blanks from primary sources: the model card, the pricing page, and the benchmark leaderboard itself. Do not rely on a summary, including this one, for numbers you will build a budget around.

Why Automation Bench and OS World matter

Most benchmark tables measure how well a model answers questions. Delegated work is different. It asks a model to carry out steps, often across several tools, and to recover when something goes wrong.

Automation Bench is aimed at that second kind of work: multi-step business tasks where the model has to choose actions and complete a workflow. OS World tests whether a model can operate a computer environment, clicking through interfaces and completing tasks the way a person would.

If your use case is "read this email and tag it," neither benchmark is critical, and the index plus a quick test is enough. If your use case is "log in, pull the report, reconcile it against a spreadsheet, and post a summary," these two results are much closer to what you will experience. A model that matches a rival on the index but trails on OS World may still be the wrong pick for agent work.

The core idea: plan big, delegate small

The practical recommendation from the source material is simple. Use a large model to plan, and hand the execution to a small, cheap one.

The reasoning is about where intelligence is actually needed. Deciding how to break down a goal, which steps depend on which, and what counts as done is hard. Executing a well-specified step ("extract the invoice number and total from this PDF text and return JSON") is much easier. Paying large-model prices for the easy part is wasteful.

A typical split looks like this:

1. The planner (large model) reads the goal, breaks it into discrete tasks, writes a clear brief for each, and defines what a correct output looks like.
2. The workers (small models) each take one brief and return a result in a fixed format.
3. The reviewer (large model or simple code) checks the outputs against the criteria and sends failures back or escalates them.

The big model is called a handful of times per job. The small model is called many times. That is exactly where a model like Muse Ember, at about a third of Haiku 5.5's cost, changes the economics.

Worked example: weekly support triage

Say your team receives a few hundred support emails a day and needs them sorted into billing, bug, feature request, and other, with an urgency flag.

Planner step. Give the large model a sample of real emails and ask it to produce a labeling guide: the four categories, two or three edge-case rules, and the criteria for "urgent." This is a one-time or occasional call.

Worker step. For each email, send the small model the guide plus the email and ask for a single JSON object with category, urgent, and a one-sentence reason. Keep the output format rigid so it can be parsed without guessing.

Review step. Have code check that the JSON is valid and the category is one of the four allowed values. Route anything malformed, or anything the worker marked with low confidence, to the large model.

Because the small model only does classification against a clear guide, this is the kind of task where a cheaper model with an index score level with Haiku 5.5 should hold up. You are not asking it to invent anything.

Worked example: a spreadsheet clean-up job

Now a task closer to what Automation Bench targets. You have a messy export with inconsistent date formats, duplicate customers, and free-text company names.

The planner decides the sequence: normalize dates first, then dedupe on email, then standardize company names. It writes each as a separate brief with an example of input and expected output.

Each worker handles one column or one rule across a batch of rows. Keeping the briefs narrow is the key. A small model asked to "clean this spreadsheet" will improvise. A small model asked to "convert every value in this list to YYYY-MM-DD, and return the string UNPARSEABLE if you cannot" will do the job and tell you where it failed.

Worked example: a computer-use task

OS World style work is the hardest of the three for a small model. Imagine an agent that has to open an internal admin page, find a customer record, and update a field.

Here, split the job even more finely. Let the planner decide the route through the interface. Let the small model handle one screen at a time, with a brief such as "On this page, click the Customers tab and report what you see." Add a checkpoint after each step so a wrong click is caught early, not ten steps later.

This is also where you should lean hardest on the published OS World result for each candidate model. If a model scores noticeably lower there, add more checkpoints or keep the planner closer to the loop.

A delegation brief template

Every worker call should carry the same five parts. Copy this and adapt it:

ROLE: You are handling one step of a larger task.
TASK: [One sentence describing exactly what to do.]
INPUT: [The data, pasted or referenced.]
RULES: [Two to five specific rules, including edge cases.]
OUTPUT FORMAT: [Exact structure, such as JSON keys. State what to return when the input cannot be handled.]

The last line matters most. Small models fail quietly when they have no sanctioned way to say "I can't." Give them one.

How to test before you switch

Do not move production traffic on the strength of a benchmark table. Run a short, honest test.

1. Collect 50 to 100 real examples of the task, including ugly ones.
2. Get reference answers from the large model or from a human.
3. Run each candidate (Muse Ember, Haiku 5.5, GPT-6 Luna) with the identical brief.
4. Score the outputs on accuracy, format validity, and how often the model refused or went off-script.
5. Compare cost per correct answer, not cost per call. A cheaper model that needs retries twice as often may lose its price advantage.

That last metric is the one that decides most real choices. If Muse Ember is about a third of the price and gets close to the same number of correct answers, it wins easily. If it needs far more retries on your data, the gap narrows.

Where small models still fall short

Cheap models are not a replacement for a strong one. Expect weaker performance on long chains of reasoning, ambiguous instructions, and tasks where the right answer depends on context that is not in the prompt. They can also be more sensitive to how the brief is worded.

The fix is rarely "use a bigger model everywhere." It is usually a tighter brief, a stricter output format, or an added check. That is the whole point of the planner and reviewer roles: they absorb the difficulty so the worker does not have to.

Where Remy fits

If you want to put a delegation workflow like this to work without wiring it up by hand, Remy is an AI that builds and deploys business apps from a plain description. You describe the process, such as triaging support email or cleaning a weekly export, and Remy produces the working app, so model choice becomes a cost decision you can revisit later rather than a build you have to redo.