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Claude Sonnet 4.6 vs Muse Spark 1.1

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 1.1 wins outright — it scores higher and costs less.

Muse Spark 1.1 leads by 2.3 points overall while listing 3.0× 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. Muse Spark 1.1 also leads on measured cost per point of capability, at $0.1139 per point.

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Claude Sonnet 4.6

Blended / 1M
$6.00
Context
1M
Released
Feb 17, 2026
Overall score
73.0
reasoningtool callingimage inputfile inputprompt caching

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Muse Spark 1.1

Blended / 1M
$2.00
Context
1.0M
Released
Jul 16, 2026
Overall score
75.3
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

Specs and pricing

MetricClaude Sonnet 4.6Muse Spark 1.1
LiveBench overall

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

73.075.3win
Cost per point

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

$0.1561$0.1139win
Blended price / 1M

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

$6.00$2.00win
Input price / 1M$3.00$1.25win
Output price / 1M$15.00$4.25win
Cached input / 1M

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

$0.300$0.150win
Context window1M1.0M
Max output tokens128K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Sonnet 4.6 on top, Muse Spark 1.1 below, both out of 100.

Agentic coding
42.6
58.5
Coding
79.3
77.2
Reasoning
84.8
87.7
Mathematicstoo close to call
87.0
87.1
Data analysis
77.9
72.5
Language
76.1
74.3
Instruction following
63.2
69.6

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.

WorkloadClaude Sonnet 4.6Muse Spark 1.1
Support chatbot

1.2K in / 400 out × 200K requests

$1725.60/mo$560.80/mo
RAG assistant

8K in / 600 out × 100K requests

$2220.00/mo$815.00/mo
Coding agent

40K in / 4K out × 20K requests

$2088.00/mo$724.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$3990.00/mo$1513.75/mo
Bulk classification

500 in / 20 out × 5M requests

$7650.00/mo$3000.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Muse Spark 1.1

Lowest measured cost per point of capability at $0.1139 per point — the gap compounds with every request.

Quality matters more than the bill

Muse Spark 1.1

Highest overall LiveBench score of the two at 75.3.

The workload is coding or agentic work

Muse Spark 1.1

Leads on agentic coding — 58.5 against 42.6.

Claude Sonnet 4.6 vs Muse Spark 1.1 FAQ

Which is better, Claude Sonnet 4.6 or Muse Spark 1.1?

Muse Spark 1.1 wins outright — it scores higher and costs less. Muse Spark 1.1 leads by 2.3 points overall while listing 3.0× 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. Muse Spark 1.1 also leads on measured cost per point of capability, at $0.1139 per point.

Is Claude Sonnet 4.6 cheaper than Muse Spark 1.1?

Muse Spark 1.1 is cheaper. On a 3:1 input:output blend, Claude Sonnet 4.6 lists at $6.00 per million tokens and Muse Spark 1.1 at $2.00 — Muse Spark 1.1 is 3.0× cheaper. Input and output are priced separately — Claude Sonnet 4.6 charges $3.00 in and $15.00 out, Muse Spark 1.1 charges $1.25 and $4.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Sonnet 4.6 vs Muse Spark 1.1: which scores higher on benchmarks?

Claude Sonnet 4.6 scores 73.0 and Muse Spark 1.1 scores 75.3 overall on LiveBench, the mean of its seven categories. That is a 2.3-point lead for Muse Spark 1.1. 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 4.6 or Muse Spark 1.1?

Muse Spark 1.1. 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 4.6 works out at $0.1561 per point and Muse Spark 1.1 at $0.1139.

Does Claude Sonnet 4.6 or Muse Spark 1.1 have a bigger context window?

They are effectively the same — 1M for Claude Sonnet 4.6 and 1.0M for Muse Spark 1.1.

Do Claude Sonnet 4.6 and Muse Spark 1.1 support prompt caching?

Both publish a cached-input rate: $0.300 per million for Claude Sonnet 4.6 and $0.150 for Muse Spark 1.1, against full input rates of $3.00 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.
  • 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.