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Gemma 3 27B 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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Gemma 3 27B is the cheaper of the two; neither can be ranked on quality here.

Gemma 3 27B 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.

google

Gemma 3 27B

Blended / 1M
$0.172
Context
131K
Released
Mar 12, 2025
Overall score
Not evaluated
tool callingimage inputprompt caching

meta

Muse Spark 1.3

Blended / 1M
$2.00
Context
1.0M
Released
Sep 2, 2026
Overall score
81.6
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

Specs and pricing

MetricGemma 3 27BMuse 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.

$0.172win$2.00
Input price / 1M$0.080win$1.25
Output price / 1M$0.450win$4.25
Cached input / 1M

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

$0.040win$0.150
Context window131K1.0Mwin
Max output tokens118K944Kwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemma 3 27B on top, Muse Spark 1.3 below, both out of 100.

Agentic coding
64.1
Coding
81.1
Reasoning
89.7
Mathematics
95.9
Data analysis
79.6
Language
82.8
Instruction following
78.0

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.

WorkloadGemma 3 27BMuse Spark 1.3
Support chatbot

1.2K in / 400 out × 200K requests

$52.32/mo$560.80/mo
RAG assistant

8K in / 600 out × 100K requests

$75.00/mo$815.00/mo
Coding agent

40K in / 4K out × 20K requests

$77.60/mo$724.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$111.75/mo$1,514/mo
Bulk classification

500 in / 20 out × 5M requests

$225.00/mo$3,000/mo
Run these two through the cost calculator

Which should you pick?

You need to fit large documents in one call

Muse Spark 1.3

Wider context window — 1.0M against 131K.

You are cost-constrained

Gemma 3 27B

Cheaper on blended list price at $0.172 per million tokens.

Gemma 3 27B vs Muse Spark 1.3 FAQ

Which is better, Gemma 3 27B or Muse Spark 1.3?

Gemma 3 27B is the cheaper of the two; neither can be ranked on quality here. Gemma 3 27B 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 Gemma 3 27B cheaper than Muse Spark 1.3?

Gemma 3 27B is cheaper. On a 3:1 input:output blend, Gemma 3 27B lists at $0.172 per million tokens and Muse Spark 1.3 at $2.00 — Gemma 3 27B is 12× cheaper. Input and output are priced separately — Gemma 3 27B charges $0.080 in and $0.450 out, Muse Spark 1.3 charges $1.25 and $4.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Does Gemma 3 27B or Muse Spark 1.3 have a bigger context window?

Muse Spark 1.3 has the larger context window: 131K for Gemma 3 27B 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 Gemma 3 27B and Muse Spark 1.3 support prompt caching?

Both publish a cached-input rate: $0.040 per million for Gemma 3 27B and $0.150 for Muse Spark 1.3, against full input rates of $0.080 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.
  • 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.