AI model comparison

Claude Sonnet 4.6 vs Muse Spark 1.1

Compare recorded price, context, output limits, speed, latency, capabilities, and input/output support. Every missing value stays visible, and unsourced benchmarks are excluded.

Claude Sonnet 4.6 and Muse Spark 1.1 both come up when teams are choosing a model for production work, and the honest answer usually depends on your workload rather than a leaderboard. This page puts their published specifications side by side — context window, token pricing, supported inputs and outputs — so you can see where they actually differ.

Side-by-sideReviewed by our team

Anthropic

Claude Sonnet 4.6

Claude workhorse for coding agents, careful analysis, and production cost control

Context
1M
Max output
64K
Input / 1M
$3.00
Output / 1M
$15.00
Output speed

Accepts

  • Text input
  • Image input
  • PDF input

Produces

  • Text output
VS

Meta

Muse Spark 1.1

Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.

Context
1M
Max output
32K
Input / 1M
$1.25
Output / 1M
$4.25
Output speed

Accepts

  • Text input
  • Image input
  • PDF input
  • Video input

Produces

  • Text output

At a glance

Decision snapshot

This is a directional summary of the values recorded in this directory—not a universal quality verdict. Test the finalists on your own prompts before committing.

Where Claude Sonnet 4.6 leads in recorded data

  • Maximum output: 64K

Where Muse Spark 1.1 leads in recorded data

  • Input price: $1.25
  • Output price: $4.25

Context capacity

Higher listed limit

Claude Sonnet 4.6:1M

Muse Spark 1.1:1M

Same recorded value

Maximum output

Higher listed limit

Claude Sonnet 4.6:64K

Muse Spark 1.1:32K

Claude Sonnet 4.6

Input price

Lower recorded price

Claude Sonnet 4.6:$3.00

Muse Spark 1.1:$1.25

Muse Spark 1.1

Output price

Lower recorded price

Claude Sonnet 4.6:$15.00

Muse Spark 1.1:$4.25

Muse Spark 1.1

Output speed

Higher recorded throughput

Claude Sonnet 4.6:

Muse Spark 1.1:

No complete comparison

Time to first token

Lower recorded latency

Claude Sonnet 4.6:

Muse Spark 1.1:

No complete comparison

Intelligence Index

Higher directory index

Claude Sonnet 4.6:

Muse Spark 1.1:

No complete comparison

Evidence

Verified shared benchmarks

A score appears only when both records include a verified source. It is not treated as a universal ranking.

These two models haven't been measured on the same benchmark yet, so there's nothing meaningful to chart. Comparing scores from different tests would tell you more about the tests than the models — the specs and pricing above are the better guide here.

Specifications

Pricing, limits and capabilities

SpecificationClaude Sonnet 4.6Muse Spark 1.1
ProviderAnthropicMeta
Model familyclaude-sonnetmuse
Accepted inputstext, image, pdftext, image, pdf, video
Produced outputstexttext
Context window1M1M
Max output64KRecorded lead32K
Intelligence Index
Input price / 1M$3.00$1.25Recorded lead
Output price / 1M$15.00$4.25Recorded lead
Output speed
Time to first token
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputNot documentedNot documented
Structured outputNot documentedDocumented
LicenseProprietaryProprietary
ReleasedFeb 17, 2026Apr 8, 2026

Workload guidance

Which model should you choose?

On the published numbers, Muse Spark 1.1 accepts the larger context window and Muse Spark 1.1 is cheaper per input token. Which matters more depends on whether your bottleneck is document size or spend — the table above has the exact figures.

Provenance

Sources and data limits

Prices and hosted performance can change. SyncDev records source references and clearly separates documented specifications from measured values; confirm commercial terms with the provider before deployment.

Questions people ask

It compares recorded context and output limits, token prices, output speed, time to first token, Intelligence Index, modality support, tool and structured-output capabilities, release information, and only source-linked benchmarks verified on both records. Missing information is labeled rather than inferred.

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Choose any two approved model records and review them with the same evidence rules.

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