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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.
7B/67B2B/7B
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.
4k8k
Overview
CompanyDeepSeekGoogle
Release dateNov 29 2023Feb 21 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

DeepSeek-LLMvsClaude Haiku 5.5Gemma 1vsClaude Haiku 5.5DeepSeek-LLMvsGPT-6.1 SolGemma 1vsGPT-6.1 SolDeepSeek-LLMvsMuse Spark OpenGemma 1vsMuse Spark OpenDeepSeek-LLMvsGrok 4.7Gemma 1vsGrok 4.7DeepSeek-LLMvsMistral Large 4Gemma 1vsMistral Large 4DeepSeek-LLMvsKimi K3Gemma 1vsKimi K3

Frequently asked questions

DeepSeek-LLM and Gemma 1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-LLM shipped 84 days before Gemma 1, so benchmark comparisons should account for the intervening progress.

DeepSeek-LLM has 7B/67B parameters, while Gemma 1 has 2B/7B. Context windows are 4k (DeepSeek-LLM) vs 8k (Gemma 1).

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