Gemini 2.5 Computer Use pricing for blog writing

Quick start

Call Gemini 2.5 Computer Use with the Google Generative AI SDK:

import google.generativeai as genai

model = genai.GenerativeModel("gemini-2.5-computer-use")
response = model.generate_content("Your prompt here")
# Cost: ~$0.0813 per call at standard rates
# Input:  $1.25/MTok
# Output: $10.00/MTok

How Gemini 2.5 Computer Use compares on cost

Per-call cost for all models scoring 50+ on blog writing benchmarks. Hover for details.

Cheapest viable option: Claude Sonnet 5 at $0.0820/call (-1% less, 87/100 quality).

What drives blog writing cost

The most output-heavy use case tracked here. A brief of a few hundred tokens produces an article of several thousand, so output pricing is essentially the entire cost and input rate is noise. Generating section by section rather than whole-article gives you more control over both quality and spend, at the cost of more calls.

For Gemini 2.5 Computer Use specifically, that means output tokens cost 8.0x more than input, at $10.00 vs $1.25 per MTok. At this use case’s 1,000/8,000 token split, output accounts for 98% of the $0.0813 per-call cost.

Gemini 2.5 Computer Use quality benchmarks for blog writing

Benchmark data for blog writing is pending for Gemini 2.5 Computer Use. Test against your own evaluation suite before moving to production.

Performance across all task types

Gemini 2.5 Computer Use scores by task type. Blog Writing highlighted.

Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 10 weeks old, so treat the scores as directional and validate against your own evals.

About Gemini 2.5 Computer Use

Backfilled on 2026-09-23. Prices confirmed by the provider pricing page and the LiteLLM cross-check. Release date not published in a machine-readable source. Not yet benchmarked, so never recommended.

Other models from Gemini

You can compare Gemini 2.5 Computer Use head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions