// head_to_head
Claude Sonnet 5.5 vs Muse Wonder
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 Sonnet 5.5 wins outright — it scores higher and costs less.
Claude Sonnet 5.5 leads by 5.9 points overall while listing 2.0× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. The two cost measures disagree here, which is worth knowing: Claude Sonnet 5.5 has the lower sticker price, but Muse Wonder earns each point of capability for less — $0.0201 against $0.0372 — because per-token rates do not predict how many tokens a model actually spends on a task.
anthropic
Claude Sonnet 5.5
- Blended / 1M
- $4.00
- Context
- 1M
- Released
- Sep 28, 2026
- Overall score
- 80.0
meta
Muse Wonder
- Blended / 1M
- $8.00
- Context
- 1.0M
- Released
- Oct 12, 2026
- Overall score
- 74.2
Specs and pricing
| Metric | Claude Sonnet 5.5 | Muse Wonder |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 80.0win | 74.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0372 | $0.0201win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00win | $8.00 |
| Input price / 1M | $2.00win | $4.00 |
| Output price / 1M | $10.00win | $20.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.100win | $0.200 |
| 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 Sonnet 5.5 on top, Muse Wonder 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 Sonnet 5.5 | Muse Wonder |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,143/mo | $2,286/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,440/mo | $2,880/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,336/mo | $2,672/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,655/mo | $5,310/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,050/mo | $10,100/mo |
Which should you pick?
You are running this at volume
Muse Wonder
Lowest measured cost per point of capability at $0.0201 per point — the gap compounds with every request.
Quality matters more than the bill
Claude Sonnet 5.5
Highest overall LiveBench score of the two at 80.0.
The workload is coding or agentic work
Claude Sonnet 5.5
Leads on long-horizon agentic — 63.4 against 52.6.
Claude Sonnet 5.5 vs Muse Wonder FAQ
Which is better, Claude Sonnet 5.5 or Muse Wonder?
Claude Sonnet 5.5 wins outright — it scores higher and costs less. Claude Sonnet 5.5 leads by 5.9 points overall while listing 2.0× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. The two cost measures disagree here, which is worth knowing: Claude Sonnet 5.5 has the lower sticker price, but Muse Wonder earns each point of capability for less — $0.0201 against $0.0372 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Claude Sonnet 5.5 cheaper than Muse Wonder?
Claude Sonnet 5.5 is cheaper. On a 3:1 input:output blend, Claude Sonnet 5.5 lists at $4.00 per million tokens and Muse Wonder at $8.00 — Claude Sonnet 5.5 is 2.0× cheaper. Input and output are priced separately — Claude Sonnet 5.5 charges $2.00 in and $10.00 out, Muse Wonder charges $4.00 and $20.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Claude Sonnet 5.5 vs Muse Wonder: which scores higher on benchmarks?
Claude Sonnet 5.5 scores 80.0 and Muse Wonder scores 74.2 overall on LiveBench, the weighted mean of its seven categories. That is a 5.9-point lead for Claude Sonnet 5.5. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, Claude Sonnet 5.5 or Muse Wonder?
Muse Wonder. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Claude Sonnet 5.5 works out at $0.0372 per point and Muse Wonder at $0.0201.
Does Claude Sonnet 5.5 or Muse Wonder have a bigger context window?
They are effectively the same — 1M for Claude Sonnet 5.5 and 1.0M for Muse Wonder.
Do Claude Sonnet 5.5 and Muse Wonder support prompt caching?
Both publish a cached-input rate: $0.100 per million for Claude Sonnet 5.5 and $0.200 for Muse Wonder, against full input rates of $2.00 and $4.00. 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 2026-10-25, 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.