Command A vs Gemini Computer Use

At a Glance

Compare
Command ACohere
Gemini Computer UseGoogle DeepMind
Pricing and Limits
Input priceFrom · USD / 1M tokens$2.50Cohere · Aug 29, 2026$1.25Google AI · Aug 29, 2026
Output priceFrom · USD / 1M tokens$10.00Cohere · Aug 29, 2026$10.00Google AI · Aug 29, 2026
Context windowMaximum documented tokens256K128K
Model facts checkedAug 29, 2026View model evidence →Aug 29, 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

FieldCommand AGemini Computer Use
DeveloperCohereGoogle DeepMind
FamilyCommand AGemini Tools
ModelCommand AGemini Computer Use
VersionCommand AGemini Computer Use
Lifecycleactivepreview
ReleasedUnknownUnknown
Knowledge cutoff2024-06-01Unknown
Input modalitiesTextText, Image
Output modalitiesTextText
Context window256K128K
Total parameters111BUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessCohere (Standard)Google AI (Standard), Google Gemini (Standard)
Capabilitiesagents, chat, citations, multilingual, rag, structured_outputs, toolsgeneration, reasoning, tools

Command A Capabilities

agentschatcitationsmultilingualragstructured outputstools
Serving providers1
Canonical IDcoherelabs/command-a-03-2025

Gemini Computer Use Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Command A vs Gemini Computer Use FAQs

Is Command A or Gemini Computer Use better for coding?+

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

Which is cheaper, Command A or Gemini Computer Use?+

Command A is $2.50 and Gemini Computer Use is $1.25 per million tokens, so Gemini Computer Use is cheaper on this metric. Command A is $10.00 and Gemini Computer Use is $10.00 per million tokens, so they are tied on this metric.

Which has a larger context window, Command A or Gemini Computer Use?+

Command A has the larger sourced context window. Command A supports 256K and Gemini Computer Use supports 128K.

Which performs better in benchmarks, Command A or Gemini Computer Use?+

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

Can Command A or Gemini Computer Use be self-hosted?+

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

Can Command A and Gemini Computer Use understand images?+

Command A is not documented with image input; Gemini Computer Use is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Command A or Gemini Computer Use?+

Gemini Computer Use has the larger sourced maximum output: Command A supports 8K and Gemini Computer Use supports 64K output tokens.

Do Command A and Gemini Computer Use support reasoning and tool use?+

Command A: tool calling. Gemini Computer Use: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Command A or Gemini Computer Use?+

Command A has 1 sourced provider route; Gemini Computer Use has 2, so Gemini Computer Use has broader tracked availability.

Which offers better value, Command A or Gemini Computer Use?+

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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