Gemini 1.5 Flash 8B

音声、画像、ビデオ、テキストを効率的に処理できるマルチモーダルモデルです。JSONモード、関数呼び出し、コード実行、システム命令のサポート機能を備えています。高速推論に最適化されており、パラメータ数は80億です。

Intelligence(Relatively Weak)

Speed(Fast)

Input Supported Modalities

Yes

Is Reasoning Model

1,048,576

Context Window

8,192

Maximum Output Tokens

2024-10-01

Knowledge Cutoff

Pricing

￥0.58 /M tokens

Input

￥2.16 /M tokens

Output

￥0.47 /M tokens

Blended Price

Quick Simple Comparison

Gemini 2.0 Flash Thinking Experimental (Dec '24)

Gemini 2.0 Pro Experimental (Feb '25)

Gemini 1.5 Pro (May '24)

￥2.5

Basic Parameters

GPT-4.1 Technical Parameters

Parameter Count

8,000.0M

Context Length

1.0M tokens

Training Data Cutoff

2024-10-01

Open Source Category

Proprietary

Multimodal Support

Text, Image

Throughput

150

Release Date

2024-10-03

Response Speed

279.24,124 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

3083

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

2230

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

56.9

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

35.9

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

4.5

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

21.7

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

22.9

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

11.6

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

68.9

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)

3.3

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

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