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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/13B/34B70B
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.
16k128k
Overview
CompanyMetaNVIDIA
Release dateAug 24 2023Oct 15 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

Code LlamavsClaude Haiku 5.5Llama 3.1 Nemotron 70BvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolLlama 3.1 Nemotron 70BvsGPT-6.1 SolCode LlamavsGemini 4 ArgonLlama 3.1 Nemotron 70BvsGemini 4 ArgonCode LlamavsGrok 4.7Llama 3.1 Nemotron 70BvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017Llama 3.1 Nemotron 70BvsDeepSeek-V4.1-Flash-1017Code LlamavsMistral Large 4Llama 3.1 Nemotron 70BvsMistral Large 4

Frequently asked questions

Code Llama and Llama 3.1 Nemotron 70B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 418 days before Llama 3.1 Nemotron 70B, so benchmark comparisons should account for the intervening progress.

Code Llama has 7B/13B/34B parameters, while Llama 3.1 Nemotron 70B has 70B. Context windows are 16k (Code Llama) vs 128k (Llama 3.1 Nemotron 70B).

Direct benchmark comparisons are unavailable — Code Llama and Llama 3.1 Nemotron 70B don't publish scores on any of the same benchmarks.