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.
671B9B/27B
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.
128k8k
API pricingUSD per 1M tokens · lower wins
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.
$0.25DeepInfra$0.65NextBit
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
$0.95DeepInfra$0.65NextBit
$97.50DeepInfra · fp4$163NextBit · int4
These models have no shared benchmark scores.
Overview
CompanyDeepSeekGoogle
Release dateAug 21 2025Jun 27 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Frequently asked questions

DeepSeek V3.1 and Gemma 2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemma 2 shipped 420 days before DeepSeek V3.1, so benchmark comparisons should account for the intervening progress.

DeepSeek V3.1 has 671B parameters, while Gemma 2 has 9B/27B. Context windows are 128k (DeepSeek V3.1) vs 8k (Gemma 2).

Direct benchmark comparisons are unavailable — DeepSeek V3.1 and Gemma 2 don't publish scores on any of the same benchmarks.