Muse Ember
Muse Ember is a chat model from Meta, tracked on this page with measured throughput of roughly 467 output tokens per second in independent benchmarking.
API Pricing
| Provider | Input / 1M | Output / 1M | Cached / 1M |
|---|---|---|---|
| $0.100 | $0.500 | $0.0040 |
Prices updated daily. Last check: Nov 10, 2026
Muse Ember pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Benchmarks measured Nov 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- Meta
- Modalities
- Text
Capabilities
Strengths & Limitations
Strengths
- Measured output throughput of about 466.69 tokens per second in Artificial Analysis testing, which is fast for streamed text generation
- High streaming rate makes long-form generations (drafts, summaries, extended answers) complete quickly once output begins
- Chat-tuned, so it is directly usable for multi-turn assistant and conversational interfaces without additional instruction formatting work
- Attributed to Meta, a creator with multiple models tracked on this site for side-by-side throughput and pricing comparison
- Performance figures come from an independent third party (Artificial Analysis) rather than vendor-reported marketing numbers
- Listed with live multi-provider pricing on this page, so cost per request can be compared directly against alternatives
Limitations
- Time to first token of roughly 4,740 ms is slow, which is noticeable in interactive chat and voice-adjacent use cases
- We do not currently track a published context window for this model, so long-document limits must be verified with the provider
- Modality support (image or audio input) is not recorded in our metadata and should not be assumed
- Tool calling, structured output, and other API-level features are untracked for this entry and need confirmation before agentic use
- Benchmark coverage is thin — we have throughput and latency figures but no reasoning, coding, or knowledge benchmark scores
Key Features
About Muse Ember
Common Use Cases
Muse Ember's measured profile — fast sustained generation with a slow first token — points toward batch and long-output workloads rather than snappy interactive turns. It fits content drafting, document summarization, bulk rewriting, synthetic data generation, and background chat processing where a multi-second startup delay is amortized across a long response. For customer-facing chat widgets, autocomplete, or any flow where the user watches an empty box, the time-to-first-token figure is the constraint to design around, possibly with a streaming placeholder or a smaller model for short turns. Because we do not track a confirmed context window, modality support, or tool-calling behavior for this entry, verify those against the serving provider's documentation before committing it to retrieval-heavy or agentic pipelines.
Frequently Asked Questions
How much does Muse Ember cost to run?
Pricing depends on which provider serves the model and on the pricing type — per-input-token, per-output-token, and any batch or cached-input rates all differ between hosts, and they change frequently. Check the pricing table on this page for the current rates across tracked providers rather than relying on a figure quoted elsewhere.
What is Muse Ember best used for?
It suits chat and text-generation workloads where output length is substantial and a few seconds of startup latency is acceptable — summarization, drafting, rewriting, and batch conversational processing. Its roughly 467 tokens per second output rate means long responses finish quickly once generation starts.
Is Muse Ember fast?
It depends which kind of speed matters. Output throughput is high at about 466.69 tokens per second in Artificial Analysis measurements. Time to first token is slow at roughly 4,740 ms, so the model feels unresponsive at the start of a turn even though it generates quickly afterward. Both numbers vary by provider and request shape.
What context window does Muse Ember support?
We do not currently track a confirmed context window for this entry. A missing value here means unknown, not zero — consult Meta's model documentation or the API reference of whichever provider you plan to use for the authoritative limit.
Does Muse Ember support images or tool calling?
Our metadata does not record modality support or tool-calling behavior for Muse Ember, so we cannot confirm either way. If your application depends on vision input or function calling, verify directly with the serving provider's API documentation before integrating.