Meta: Llama 3.2 1B Instruct

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/).

Avg Score

39.3%

259 answers

Avg Latency

4.4s

24 runs

Pricing

$0.03

input

$0.20

output

per 1M tokens

Context

60K

tokens

Alternatives

Models with similar or better quality but different tradeoffs

Same Quality, Cheaper

Models with similar or better performance at a lower cost per token.

Model	Cost
Meta: Llama 3.2 3B Instruct	-98%
Meta: Llama 4 Scout	-74%
Meta: Llama 3.3 70B Instruct	-73%
Mistral: Ministral 3 3B 2512	-64%
Google: Gemini 2.0 Flash	-61%

Same Quality, Faster

Models with similar or better performance but lower latency.

Model	Latency
Google: Gemini 2.5 Flash Lite	-74%
Google: Gemini 2.0 Flash	-72%
Google: Gemini 2.5 Flash	-67%
Mistral: Ministral 3 8B 2512	-62%
Mistral: Ministral 3 3B 2512	-61%

Same Cost, Better

Models at a similar price point with higher benchmark scores.

Model	Score
DeepSeek: DeepSeek V3.2	+40%
Google: Gemini 2.0 Flash	+39%
Meta: Llama 4 Maverick	+36%
Google: Gemini 2.5 Flash Lite	+34%
Meta: Llama 4 Scout	+34%

Other Models from Meta

Compare performance with other models from the same creator

Model	Score	Latency	Cost/1M
Meta: Llama 4 Maverick	76.4%	2.6s	$0.38
Meta: Llama 4 Scout	74.3%	2.8s	$0.19
Meta: Llama 3.3 70B Instruct	71.7%	3.5s	$0.21
Meta: Llama 3.2 3B Instruct	53.9%	3.0s	$0.02
Meta: Llama 3.1 405B (base)	3.2%	45.0s	$4.00
Meta: Llama Guard 4 12B	—	—	$0.18
Llama Guard 3 8B	—	—	$0.04
Meta: Llama 3.3 70B Instruct	—	—	$0.0000
Meta: Llama 3.2 3B Instruct	—	—	$0.0000
Meta: Llama 3.2 90B Vision Instruct	—	—	$0.38
Meta: Llama 3.2 11B Vision Instruct	—	—	$0.05
Meta: Llama 3.1 8B Instruct	—	—	$0.03
Meta: Llama 3.1 405B Instruct	—	—	$0.0000
Meta: Llama 3.1 405B Instruct	—	—	$3.50
Meta: Llama 3.1 70B Instruct	—	—	$0.40
Meta: LlamaGuard 2 8B	—	—	$0.20
Meta: Llama 3 70B Instruct	—	—	$0.35
Meta: Llama 3 8B Instruct	—	—	$0.04

Benchmark Performance

How this model performs across different benchmarks

Benchmark	Score	Rank
Math Bench: Addition (1-10)	100.0%	24 / 35
Repetitive Onomatopoeia Response Benchmark	100.0%	11 / 17
Math Bench: Multiplication (1-10)	90.0%	33 / 35
Math Bench: Subtraction (1-10)	85.0%	33 / 35
Structured Customer Support Extraction	80.0%	17 / 17
Math Bench: Division (1-10)	74.0%	32 / 35
Motor production test	60.0%	34 / 35
Customer support	50.0%	14 / 17
SaaS Customer Support Extraction and Strategy	50.0%	16 / 16
Math Bench: Arithmetic (1-100)	40.0%	34 / 35
KUKA force Torque control programming skills	40.0%	17 / 17
Math Bench: Arithmetic (1-1000)	35.0%	34 / 35
Product Recommendations	30.0%	35 / 36
Spatial Reasoning: Germany	18.5%	34 / 35
Character Frequency Bench	14.5%	34 / 35
Venture Capital Terms Benchmark	10.6%	25 / 25
Customer service	10.0%	17 / 17
Karlsruhe Local Knowledge Benchmark	7.8%	35 / 35
Money Boy Cultural Literacy Test	2.9%	27 / 35
German Memelord Bench	2.4%	34 / 35
Karl Lorey knowlege	0.0%	36 / 36
Current Global Political Leaders	0.0%	9 / 17

Price vs Performance

Compare cost efficiency across all models

Current model

Other models

X-axis uses log scale for better visualization of price range

Score Over Time

Performance trends across all benchmark runs

Benchmark Activity

Number of benchmark runs over time

Quickstart

Get started with this model using OpenRouter

View on OpenRouter

import { OpenRouter } from "@openrouter/sdk";

const openrouter = new OpenRouter({
  apiKey: "<OPENROUTER_API_KEY>"
});

const completion = await openrouter.chat.completions.create({
  model: "meta-llama/llama-3.2-1b-instruct",
  messages: [
    {
      role: "user",
      content: "Hello!"
    }
  ]
});

console.log(completion.choices[0].message.content);

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