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Muse Spark 1.2 vs Palmyra X5

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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Muse Spark 1.2 and Palmyra X5 are priced within ~10% of each other.

Palmyra X5 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.

meta

Muse Spark 1.2

Blended / 1M
$2.00
Context
1.0M
Released
Aug 5, 2026
Overall score
78.0
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

writer

Palmyra X5

Blended / 1M
$1.95
Context
1.0M
Released
Jan 21, 2026
Overall score
Not evaluated

Specs and pricing

MetricMuse Spark 1.2Palmyra X5
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

78.0
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.2199
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$2.00$1.95
Input price / 1M$1.25$0.600win
Output price / 1M$4.25win$6.00
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.150
Context window1.0M1.0M
Max output tokens944Kwin8K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Muse Spark 1.2 on top, Palmyra X5 below, both out of 100.

Agentic coding
57.6
Coding
77.5
Reasoning
90.0
Mathematics
91.2
Data analysis
76.5
Language
78.6
Instruction following
74.3

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.

WorkloadMuse Spark 1.2Palmyra X5
Support chatbot

1.2K in / 400 out × 200K requests

$560.80/mo$624.00/mo
RAG assistant

8K in / 600 out × 100K requests

$815.00/mo$840.00/mo
Coding agent

40K in / 4K out × 20K requests

$724.00/mo$960.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$1,514/mo$1,050/mo
Bulk classification

500 in / 20 out × 5M requests

$3,000/mo$2,100/mo
Run these two through the cost calculator

Muse Spark 1.2 vs Palmyra X5 FAQ

Which is better, Muse Spark 1.2 or Palmyra X5?

Muse Spark 1.2 and Palmyra X5 are priced within ~10% of each other. Palmyra X5 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 Muse Spark 1.2 cheaper than Palmyra X5?

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 Muse Spark 1.2 or Palmyra X5 have a bigger context window?

They are effectively the same — 1.0M for Muse Spark 1.2 and 1.0M for Palmyra X5.

Do Muse Spark 1.2 and Palmyra X5 support prompt caching?

Muse Spark 1.2 publishes a cached-input rate of $0.150 per million tokens against a full input rate of $1.25. The catalogue lists no separate cached rate for Palmyra X5, which means the provider does not price it separately here — not that caching is unavailable.

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.
  • ScoresLiveBench 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.