Llama 3.2 Instruct 11B (Vision)

Llama 3.2 11B ビジュアル指令モデルは、視覚認識、画像推論、画像記述生成、および画像に関する一般的な質問への回答に最適化されたマルチモーダル大規模言語モデルです。このモデルは、テキストと画像を入力とし、生成されたテキストを出力とします。

Intelligence(Relatively Weak)

Speed(Medium)

Input Supported Modalities

Yes

Is Reasoning Model

128,000

Context Window

128,000

Maximum Output Tokens

2023-12-31

Knowledge Cutoff

Pricing

￥0.43 /M tokens

Input

￥0.43 /M tokens

Output

￥1.15 /M tokens

Blended Price

Quick Simple Comparison

Llama 4 Scout

￥0.08

Llama 4 Maverick

￥0.17

Llama 3.1 Instruct 8B

￥0.03

Basic Parameters

GPT-4.1 Technical Parameters

Parameter Count

10,600.0M

Context Length

128.00k tokens

Training Data Cutoff

2023-12-31

Open Source Category

Open Weights (Permissive License)

Multimodal Support

Text, Image

Throughput

168

Release Date

2024-09-25

Response Speed

109.052,444 tokens/s

Benchmark Scores

Below is the performance of claude-monet in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

2504

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

Math Index

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

MMLU Pro

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

46.4

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

GPQA

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

22.1

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

HLE

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

5.2

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

LiveCodeBench

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

SciCode

The model's capability in code generation for scientific computing or specific scientific domains

HumanEval

Score achieved by the AI model on the specific HumanEval benchmark test set

68.7

Score achieved by the AI model on the specific HumanEval benchmark test set

Math 500 Score

Score on the first 500 larger, more well-known mathematical benchmark tests

51.6

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)

9.3

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

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Llama 3.2 Instruct 11B (Vision)