Qwen2.5 Coder Instruct 32B

Qwen2.5 Coder Instruct 32B

Qwen2.5-Coder is a model specifically designed for coding. It is trained with code data containing 55 trillion tokens, supports 92 programming languages, and has a context window of 128K. It performs excellently in code generation, completion, repair, and multi-programming tasks, while also maintaining strong performance in mathematics and general capabilities.

Qwen2.5-Coder is a model specifically designed for coding. It is trained with code data containing 55 trillion tokens, supports 92 programming languages, and has a context window of 128K. It performs excellently in code generation, completion, repair, and multi-programming tasks, while also maintaining strong performance in mathematics and general capabilities.

Intelligence(Relatively Weak)

Speed(Relatively Slow)

Input Supported Modalities

No

Is Reasoning Model

131,072

Context Window

128,000

Maximum Output Tokens

2024-03-01

Knowledge Cutoff

Pricing

￥0.65 /M tokens

Input

￥0.65 /M tokens

Output

￥1.05 /M tokens

Blended Price

Quick Simple Comparison

Qwen Turbo

Qwen2.5 Turbo

Qwen2.5 Coder Instruct 7B

Basic Parameters

GPT-4.1 Technical Parameters

Parameter Count

32,000.0M

Context Length

131.07k tokens

Training Data Cutoff

2024-03-01

Open Source Category

Open Weights (Permissive License)

Multimodal Support

Text Only

Throughput

110

Release Date

2024-11-11

Response Speed

51.83,883 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

3633

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

2830

Indicator of AI model performance on coding tasks

Math Index

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

-

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

63.5

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

41.7

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

3.8

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

29.5

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

27.1

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

90.2

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

76.7

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)

12

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

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