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Llama 3.2 1B Instruct

1B

by Meta

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 efficiently in low-resource environments while maintaining strong task performance. Supporting eight core languages and fine-tunable for more, Llama 1.3B is ideal for businesses or developers seeking lightweight yet powerful AI solutions that can operate in diverse multilingual settings without the high computational demand of larger models. Click here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md). Usage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).

Chat with Llama 3.2 1B Instruct
Input Price$0.10/1M tokens
Output Price$0.10/1M tokens
Intelligence6.3
Coding0.6

Specifications

Technical details and pricing.

ProviderMeta
Context Window60,000 tokens
Release DateSep 25, 2024
ModalitiesText

Benchmarks

12 benchmark scores from Artificial Analysis.

GPQA19.6%
MMLU Pro20.0%
HLE5.3%
LiveCodeBench1.9%
MATH 50014.0%
AIME 20250.0%
AIME0.0%
SciCode1.7%
LCR5.0%
IFBench22.8%
Tau20.0%
TerminalBench Hard0.0%

Composite Indices

Intelligence, Coding, Math

Standard Benchmarks

Academic and industry benchmarks

Frequently Asked Questions

What is Llama 3.2 1B Instruct good for?

Use Llama 3.2 1B Instruct for everyday tasks like writing, summarizing, brainstorming, and getting clear explanations.

How much does Llama 3.2 1B Instruct cost?

Pricing is based on usage. Current rates are $0.10/1M tokens for input and $0.10/1M tokens for output.

Can I try Llama 3.2 1B Instruct for free?

Yes. You can start a chat instantly and test the model before deciding on a plan.

Does Llama 3.2 1B Instruct support images or audio?

Llama 3.2 1B Instruct focuses on text-based tasks.

Benchmarks and pricing are sourced from Artificial Analysis where available. OpenRouter specs are used as a fallback.