AI Model Release Tracker

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.
7B/13B/34B253B
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
These models have no shared benchmark scores.
Overview
CompanyMetaNVIDIA
Release dateAug 24 2023Apr 8 2025
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

Code LlamavsClaude Haiku 5.5Llama Nemotron Ultra 253BvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolLlama Nemotron Ultra 253BvsGPT-6.1 SolCode LlamavsGemini 4 ArgonLlama Nemotron Ultra 253BvsGemini 4 ArgonCode LlamavsGrok 4.7Llama Nemotron Ultra 253BvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017Llama Nemotron Ultra 253BvsDeepSeek-V4.1-Flash-1017Code LlamavsMistral Large 4Llama Nemotron Ultra 253BvsMistral Large 4

Frequently asked questions

Code Llama and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 593 days before Llama Nemotron Ultra 253B, so benchmark comparisons should account for the intervening progress.

Code Llama has 7B/13B/34B parameters, while Llama Nemotron Ultra 253B has 253B. Context windows are 16k (Code Llama) vs 128k (Llama Nemotron Ultra 253B).

Direct benchmark comparisons are unavailable — Code Llama and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks.