Gemini Computer Use vs Kimi K2.5

At a Glance

Compare
Gemini Computer UseGoogle DeepMind
Kimi K2.5Moonshot AI
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.25Google AI · Aug 29, 2026$0.45Deepinfra · Aug 29, 2026
Output priceFrom · USD / 1M tokens$10.00Google AI · Aug 29, 2026$2.25Deepinfra · Aug 29, 2026
Context windowMaximum documented tokens128K262K
Model facts checkedAug 29, 2026View model evidence →Aug 28, 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 UseKimi-K2.5
DeveloperGoogle DeepMindMoonshot AI
FamilyGemini ToolsKimi K2 5
ModelGemini Computer UseKimi-K2.5
VersionGemini Computer UseKimi-K2.5
Lifecyclepreviewactive
ReleasedUnknown2026-01-27
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window128K262K
Total parametersUnknown1T
Active parametersUnknown32B
LicenseUnknownother
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessGoogle AI (Standard), Google Gemini (Standard)Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)
Capabilitiesgeneration, reasoning, toolschat, generation, reasoning, tools

Gemini Computer Use Capabilities

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

Kimi K2.5 Capabilities

chatgenerationreasoningtools
Serving providers3
Canonical IDmoonshotai/Kimi-K2.5

Primary Evidence

Sources and Freshness

Questions

Gemini Computer Use vs Kimi K2.5 FAQs

Is Gemini Computer Use or Kimi K2.5 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini Computer Use and Kimi K2.5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini Computer Use or Kimi K2.5?+

Gemini Computer Use is $1.25 and Kimi K2.5 is $0.45 per million tokens, so Kimi K2.5 is cheaper on this metric. Gemini Computer Use is $10.00 and Kimi K2.5 is $2.25 per million tokens, so Kimi K2.5 is cheaper on this metric.

Which has a larger context window, Gemini Computer Use or Kimi K2.5?+

Kimi K2.5 has the larger sourced context window. Gemini Computer Use supports 128K and Kimi K2.5 supports 262K.

Which performs better in benchmarks, Gemini Computer Use or Kimi K2.5?+

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 Kimi K2.5 be self-hosted?+

Kimi K2.5 is the only model in this pair currently marked as self-hostable. Gemini Computer Use is not marked open weight; Kimi K2.5 is open weight.

Can Gemini Computer Use and Kimi K2.5 understand images?+

Gemini Computer Use is documented with image input; Kimi K2.5 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini Computer Use or Kimi K2.5?+

Neither has a larger sourced maximum output. Gemini Computer Use is 64K and Kimi K2.5 is —.

Do Gemini Computer Use and Kimi K2.5 support reasoning and tool use?+

Gemini Computer Use: reasoning, tool calling, and image input. Kimi K2.5: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini Computer Use or Kimi K2.5?+

Gemini Computer Use has 2 sourced provider routes; Kimi K2.5 has 3, so Kimi K2.5 has broader tracked availability.

Which offers better value, Gemini Computer Use or Kimi K2.5?+

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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