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Llama 3.2 Instruct 1B
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Llama 3.2 Instruct 1B

Ultra-compact instruction-tuned version of Meta's Llama 3.2 series with only 1B parameters but carefully optimized. Designed for edge devices and resource-constrained environments, provides reliable instruction execution with minimal memory footprint. Inherits Llama series technical advantages, suitable for mobile applications, IoT devices, and lightweight AI applications requiring local deployment.
Intelligence(Weak)
Speed(Relatively Fast)
Input Supported Modalities
Yes
Is Reasoning Model
128,000
Context Window
-
Maximum Output Tokens
-
Knowledge Cutoff
Go Compare

Pricing

- /M tokens
Input
- /M tokens
Output
￥0.38 /M tokens
Blended Price

Quick Simple Comparison

Input

Output

Llama 4 Scout
￥0.08
Llama 4 Maverick
￥0.17
Llama 3.2 Instruct 1B

Basic Parameters

Llama 3.2 Instruct 1BTechnical Parameters
Parameter Count
Not Announced
Context Length
128.00k tokens
Training Data Cutoff
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
Release Date
2024-09-25
Response Speed
153.05,164 tokens/s

Benchmark Scores

Below is the performance of Llama 3.2 Instruct 1B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
9.69
Large Language Model Intelligence Level
Coding Index
1.82
Indicator of AI model performance on coding tasks
Math Index
7
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
20
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
19.6
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
5.3
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
1.9
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
1.7
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
40.2
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
14
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
-
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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