Compare AI models

Specifications
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality.
1.3B/5.7B/6.7B/33B—
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.
16k1M
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
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
—$40.00Google list price
Overview
CompanyDeepSeekGoogle
Release dateNov 2 2023Jun 17 2025
AccessOpen WeightClosed
Model detailsView modelView model

Frequently asked questions

DeepSeek Coder and Gemini 2.5 Flash-Lite 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 DeepSeek Coder. DeepSeek Coder shipped 593 days before Gemini 2.5 Flash-Lite, so benchmark comparisons should account for the intervening progress.

Context windows are 16k (DeepSeek Coder) vs 1M (Gemini 2.5 Flash-Lite). DeepSeek Coder is open weight, while Gemini 2.5 Flash-Lite is closed.

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