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Qwq 32B
Qwq 32B
A model dedicated to enhancing the inference ability of artificial intelligence, particularly excelling in mathematics and programming. It has the ability of in-depth self-reflection and self-questioning, but there are certain limitations in language mixing and recursive/infinite inference modes.
Intelligence(Medium)
Speed(Relatively Slow)
Input Supported Modalities
No
Is Reasoning Model
131,072
Context Window
32,768
Maximum Output Tokens
2024-11-28
Knowledge Cutoff
Pricing
- /M tokens
Input
- /M tokens
Output
￥3.38 /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
32,500.0M
Context Length
131.07k tokens
Training Data Cutoff
2024-11-28
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
0
Release Date
2025-03-05
Response Speed
99.410,675 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
5806
Large Language Model Intelligence Level
Coding Index
4942
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
76.4
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
59.3
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
8.2
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
63.1
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
35.8
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
97.6
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
95.7
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
78
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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