Qwen2 Instruct 72B

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Qwen2-72BInstruct是一個具有720億個參數的指令調優語言模型，支持高達131072個令牌的上下文長度。它是新的Qwen2系列的一部分，該系列已經超越了大多數開源模型，並在各種基準測試中展示了與專有模型的競爭力。

Intelligence(Relatively Weak)

Speed(Slow)

Input Supported Modalities

Is Reasoning Model

131,072

Context Window

131,072

Maximum Output Tokens

Knowledge Cutoff

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Pricing

- /M tokens

Input

- /M tokens

Output

- /M tokens

Blended Price

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Input

Output

Qwen2.5 Turbo

Qwen Turbo

Wan2.2

Basic Parameters

Qwen2 Instruct 72BTechnical Parameters

Parameter Count

72,000.0M

Context Length

131.07k tokens

Training Data Cutoff

Open Source Category

Open Weights (Permissive License)

Multimodal Support

Text Only

Throughput

Release Date

2024-06-07

Response Speed

31.001,368 tokens/s

Benchmark Scores

Below is the performance of Qwen2 Instruct 72B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

32.6

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

19.39

Indicator of AI model performance on coding tasks

Math Index

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

42.4

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

62.2

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

37.1

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

3.7

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

15.9

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

82.6

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

70.1

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)

14.7

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

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

131k

Context Length

Gemini 2.0 Flash Lite (Preview)

Output tokens/million

Output tokens/million

Output tokens/million

8192

Context Length

Empowering the Future, Your AI Solution Knowledge Base

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