Gemini Computer Use vs GPT-5.2 Pro

At a Glance

Compare
Gemini Computer UseGoogle DeepMind
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.25Google AI · Aug 29, 2026$21.00Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokens$10.00Google AI · Aug 29, 2026$168.00Openai · Sep 3, 2026
Context windowMaximum documented tokens128K400K
Model facts checkedAug 29, 2026View model evidence →Sep 3, 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 UseGPT-5.2 Pro
DeveloperGoogle DeepMindOpenAI
FamilyGemini ToolsGpt 5 2
ModelGemini Computer UseGPT-5.2 Pro
VersionGemini Computer UseGPT-5.2 Pro
Lifecyclepreviewactive
ReleasedUnknown2025-12-11
Knowledge cutoffUnknown2025-08-31
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window128K400K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard), Google Gemini (Standard)Openai (Standard), Openrouter (Standard)
Capabilitiesgeneration, reasoning, toolschat, generation, reasoning, structured_outputs, tools

Gemini Computer Use Capabilities

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

GPT-5.2 Pro Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDopenai/gpt-5.2-pro

Primary Evidence

Sources and Freshness

Questions

Gemini Computer Use vs GPT-5.2 Pro FAQs

Is Gemini Computer Use or GPT-5.2 Pro better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini Computer Use and GPT-5.2 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini Computer Use or GPT-5.2 Pro?+

Gemini Computer Use is $1.25 and GPT-5.2 Pro is $21.00 per million tokens, so Gemini Computer Use is cheaper on this metric. Gemini Computer Use is $10.00 and GPT-5.2 Pro is $168.00 per million tokens, so Gemini Computer Use is cheaper on this metric.

Which has a larger context window, Gemini Computer Use or GPT-5.2 Pro?+

GPT-5.2 Pro has the larger sourced context window. Gemini Computer Use supports 128K and GPT-5.2 Pro supports 400K.

Which performs better in benchmarks, Gemini Computer Use or GPT-5.2 Pro?+

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 GPT-5.2 Pro be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini Computer Use is not marked open weight; GPT-5.2 Pro is not marked open weight.

Can Gemini Computer Use and GPT-5.2 Pro understand images?+

Gemini Computer Use is documented with image input; GPT-5.2 Pro is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini Computer Use or GPT-5.2 Pro?+

GPT-5.2 Pro has the larger sourced maximum output: Gemini Computer Use supports 64K and GPT-5.2 Pro supports 128K output tokens.

Do Gemini Computer Use and GPT-5.2 Pro support reasoning and tool use?+

Gemini Computer Use: reasoning, tool calling, and image input. GPT-5.2 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini Computer Use or GPT-5.2 Pro?+

Gemini Computer Use has 2 sourced provider routes; GPT-5.2 Pro has 2, a tie.

Which offers better value, Gemini Computer Use or GPT-5.2 Pro?+

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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