Gemini Computer Use vs Muse Spark 1.3

At a Glance

Compare
Gemini Computer UseGoogle DeepMind
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.25Google AI · Aug 29, 2026Not reported
Output priceFrom · USD / 1M tokens$10.00Google AI · Aug 29, 2026Not reported
Context windowMaximum documented tokens128KNot reported
Model facts checkedAug 29, 2026View model evidence →Sep 4, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldGemini Computer UseMuse Spark 1.3
DeveloperGoogle DeepMindMeta
FamilyGemini ToolsMuse Spark
ModelGemini Computer UseMuse Spark 1.3
VersionGemini Computer UseMuse Spark 1.3
Lifecyclepreviewpreview
ReleasedUnknown2026-09-02
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image, Video
Output modalitiesTextText
Context window128KUnknown
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard), Google Gemini (Standard)Unknown
Capabilitiesgeneration, reasoning, toolschat, computer-use, generation, reasoning, research, tools

Gemini Computer Use Capabilities

generationreasoningtools
Serving providers2
Canonical IDgoogle-deepmind/gemini-2.5-computer-use-preview-10-2025

Muse Spark 1.3 Capabilities

chatcomputer-usegenerationreasoningresearchtools
Serving providers0
Canonical IDmeta-llama/muse-spark-1.3

Primary Evidence

Sources and Freshness

Questions

Gemini Computer Use vs Muse Spark 1.3 FAQs

Is Gemini Computer Use or Muse Spark 1.3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini Computer Use and Muse Spark 1.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini Computer Use or Muse Spark 1.3?+

Only Gemini Computer Use has a directly sourced input price: $1.25 per million tokens. Only Gemini Computer Use has a directly sourced output price: $10.00 per million tokens.

Which has a larger context window, Gemini Computer Use or Muse Spark 1.3?+

Neither model has a larger sourced context window in this comparison. Gemini Computer Use is 128K and Muse Spark 1.3 is —.

Which performs better in benchmarks, Gemini Computer Use or Muse Spark 1.3?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Gemini Computer Use or Muse Spark 1.3 be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini Computer Use is not marked open weight; Muse Spark 1.3 is not marked open weight.

Can Gemini Computer Use and Muse Spark 1.3 understand images?+

Gemini Computer Use is documented with image input; Muse Spark 1.3 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini Computer Use or Muse Spark 1.3?+

Neither has a larger sourced maximum output. Gemini Computer Use is 64K and Muse Spark 1.3 is —.

Do Gemini Computer Use and Muse Spark 1.3 support reasoning and tool use?+

Gemini Computer Use: reasoning, tool calling, and image input. Muse Spark 1.3: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini Computer Use or Muse Spark 1.3?+

Gemini Computer Use has 2 sourced provider routes; Muse Spark 1.3 has 0, so Gemini Computer Use has broader tracked availability.

Which offers better value, Gemini Computer Use or Muse Spark 1.3?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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