// head_to_head
Claude Haiku 5.5 vs Muse Ember
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Claude Haiku 5.5 and Muse Ember are priced within ~10% of each other.
Muse Ember does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
anthropic
Claude Haiku 5.5
- Blended / 1M
- $0.200
- Context
- 1M
- Released
- Oct 7, 2026
- Overall score
- 69.0
meta
Muse Ember
- Blended / 1M
- $0.200
- Context
- 1.0M
- Released
- Nov 9, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Claude Haiku 5.5 | Muse Ember |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 69.0 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0117 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.200 | $0.200 |
| Input price / 1M | $0.100 | $0.100 |
| Output price / 1M | $0.500 | $0.500 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.010 | $0.0040win |
| Context window | 1M | 1.0M |
| Max output tokens | 128K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Haiku 5.5 on top, Muse Ember below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Claude Haiku 5.5 | Muse Ember |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $57.09/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $71.60/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $66.24/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $132.70/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $252.00/mo |
Claude Haiku 5.5 vs Muse Ember FAQ
Which is better, Claude Haiku 5.5 or Muse Ember?
Claude Haiku 5.5 and Muse Ember are priced within ~10% of each other. Muse Ember does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Claude Haiku 5.5 cheaper than Muse Ember?
They cost about the same. Both land near $0.200 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Claude Haiku 5.5 or Muse Ember have a bigger context window?
They are effectively the same — 1M for Claude Haiku 5.5 and 1.0M for Muse Ember.
Do Claude Haiku 5.5 and Muse Ember support prompt caching?
Both publish a cached-input rate: $0.010 per million for Claude Haiku 5.5 and $0.0040 for Muse Ember, against full input rates of $0.100 and $0.100. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-11-06, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.