// head_to_head
Claude Haiku 4.5 vs Muse Spark 1.3
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 4.5 and Muse Spark 1.3 are priced within ~10% of each other.
Claude Haiku 4.5 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 4.5
- Blended / 1M
- $2.00
- Context
- 200K
- Released
- Oct 15, 2025
- Overall score
- Not evaluated
meta
Muse Spark 1.3
- Blended / 1M
- $2.00
- Context
- 1.0M
- Released
- Sep 2, 2026
- Overall score
- 81.6
Specs and pricing
| Metric | Claude Haiku 4.5 | Muse Spark 1.3 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 81.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.1210 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.00 | $2.00 |
| Input price / 1M | $1.00win | $1.25 |
| Output price / 1M | $5.00 | $4.25win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.100win | $0.150 |
| Context window | 200K | 1.0Mwin |
| Max output tokens | 64K | 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 4.5 on top, Muse Spark 1.3 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 4.5 | Muse Spark 1.3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $575.20/mo | $560.80/mo |
| RAG assistant 8K in / 600 out × 100K requests | $740.00/mo | $815.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $696.00/mo | $724.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1,330/mo | $1,514/mo |
| Bulk classification 500 in / 20 out × 5M requests | $2,550/mo | $3,000/mo |
Which should you pick?
You need to fit large documents in one call
Muse Spark 1.3
Wider context window — 1.0M against 200K.
Claude Haiku 4.5 vs Muse Spark 1.3 FAQ
Which is better, Claude Haiku 4.5 or Muse Spark 1.3?
Claude Haiku 4.5 and Muse Spark 1.3 are priced within ~10% of each other. Claude Haiku 4.5 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 4.5 cheaper than Muse Spark 1.3?
They cost about the same. Both land near $2.00 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Claude Haiku 4.5 or Muse Spark 1.3 have a bigger context window?
Muse Spark 1.3 has the larger context window: 200K for Claude Haiku 4.5 against 1.0M for Muse Spark 1.3. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Claude Haiku 4.5 and Muse Spark 1.3 support prompt caching?
Both publish a cached-input rate: $0.100 per million for Claude Haiku 4.5 and $0.150 for Muse Spark 1.3, against full input rates of $1.00 and $1.25. 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 2026-06-25, 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.