Llama 3.2 1B Instruct
Meta AIhuggingface.co/meta-llama/Llama-3.2-1B-Instruct
Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate...
Updated 7 h ago · first seen 11 Sept 2026
model_01M294WWNFWCDT508TQ20HTV9D
Overview
Identity
Identity block not returned by the API for this entity.
Openness
Openness not classified yet — no sourced evidence to place this model in the ontology.
Key facts
- Release date
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 8 h agomedium
- Knowledge cutoff
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
- Model card
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 13 h agomedium
- Openrouter id
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
Architecture
- Architecture
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Model type
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Parameters
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 13 h agomedium
- Tokenizer
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
- Weights dtype
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- File size
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Library name
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Pipeline tag
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 13 h agomedium
- Hugging Face repo
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
Capabilities
Modalities
- Modalities
- text
- Input
- text
- Output
- text
Capabilities
Tool calling
No
OpenRouter public model & pricing listing · T2
Structured output
No
OpenRouter public model & pricing listing · T2
Reasoning
Unavailable
Vision
Unavailable
Audio
Unavailable
Fine-tuning available
Unavailable
- Context window
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
- Max output
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
- Knowledge cutoff
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
- Languages
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Tokenizer
Source:OpenRouter public model & pricing listingT2observed 13 h agomedium
Providers & Pricing1
All offers in the price terminal →USD per 1M tokens as published by each provider (USD). Rows are append-only: every change is kept in the history below.
Price history
Output price · USD / 1M tokens 1 provider
- OpenRouter
- OpenRouterfirst observed $0.20111 Sept 2026
Input price · USD / 1M tokens 1 provider
- OpenRouter
- OpenRouterfirst observed $0.02711 Sept 2026
Hardware fit37
Assumptions (6)
- Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead.
- bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
- KV cache approximated at 0.5 GB per 8 192 tokens of context, independent of architecture (GQA/MLA models need less).
- A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
- Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
- Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
Papers2
- arXiv:2204.05149Active35
- arXiv:2405.16406Active35
Timeline4
Full timeline →Llama 3.2 1B Instruct: release date changed from 2024-09-18 to 2024-09-25
Release date18 Sept 2024→25 Sept 2024openrouterLlama 3.2 1B Instruct: release date changed from 2024-09-25 to 2024-09-18
Release date25 Sept 2024→18 Sept 2024huggingfaceOpenRouter lists Llama 3.2 1B Instruct at $0.027 in / $0.201 out per 1M tokens
openrouter
Change history35
Viewing AI Atlas as of 1 Jan 2025 — attributes exactly as the atlas knew them on that day; later corrections are not shown.
Back to today →Llama 3.2 1B Instruct was not yet in AI Atlas on 1 Jan 2025
Release daterelease_date3
Opennessopenness1
Licenselicense1
Architecturearchitecture1
Parametersparameter_count1
Context windowcontext_length1
Max outputmax_output_tokens1
Knowledge cutoffknowledge_cutoff1
Modalitiesmodalities1
Input modalitiesmodalities_input1
Output modalitiesmodalities_output1
Languageslanguages1
Tokenizertokenizer1
File sizefile_size_gb1
Hugging Face repohf_repo1
Pipeline tagpipeline_tag1
Model cardmodel_card_url1
Downloadsmetric.downloads1
Likesmetric.likes1
Descriptiondescription1
Gatedgated1
Hf inference providershf_inference_providers1
Instruct typeinstruct_type1
Last modifiedlast_modified1
Library namelibrary_name1
Downloads all timemetric.downloads_all_time1
Model typemodel_type1
Openrouter idopenrouter_id1
Structured outputstructured_output1
Supported parameterssupported_parameters1
Tool callingtool_calling1
Weights dtypeweights_dtype1
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
Provenance
Attributed facts
33
Source tiers
T233
Freshest observation
7 h ago
Conflicts
None
Source documents 3
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (65/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →