Compare AI models

Specifications
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider.
1M400k
API pricingUSD per 1M tokens · lower wins
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it.
$0.10—
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one.
$0.40—
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again.
$0.01—
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page.
$0.05Google AI Studio$1.25Azure
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
$0.20Google AI Studio$10.00Azure
$40.00Google list price$750Azure
Overview
CompanyGoogleOpenAI
Release dateJun 17 2025Nov 19 2025
AccessClosedClosed
Model detailsView modelView model

Frequently asked questions

Gemini 2.5 Flash-Lite and GPT-5.1-Codex-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 2.5 Flash-Lite has a verified first-party API price: $0.10 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for GPT-5.1-Codex-Max. Gemini 2.5 Flash-Lite shipped 155 days before GPT-5.1-Codex-Max, so benchmark comparisons should account for the intervening progress.

Context windows are 1M (Gemini 2.5 Flash-Lite) vs 400k (GPT-5.1-Codex-Max).

Direct benchmark comparisons are unavailable — Gemini 2.5 Flash-Lite and GPT-5.1-Codex-Max don't publish scores on any of the same benchmarks.