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Qwen3 235B A22B
Qwen3 235B A22B
A multi-modal large language model with a scale of hundreds of billions of parameters developed by Alibaba Cloud, supporting long context windows and complex reasoning tasks
Intelligence(Strong)
Speed(Slow)
Input Supported Modalities
Yes
Is Reasoning Model
128,000
Context Window
16,384
Maximum Output Tokens
2024-12-31
Knowledge Cutoff
Pricing
￥2 /M tokens
Input
￥20 /M tokens
Output
￥11 /M tokens
Blended Price
Quick Simple Comparison
Qwen-Turbo
￥0.04
Qwen-Plus-Latest
￥0.11
Qwen3-235B-A22B
￥0.28
Basic Parameters
GPT-4.1 Technical Parameters
Parameter Count
Not Announced
Context Length
128.00k tokens
Training Data Cutoff
2024-12-31
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
1,420
Release Date
2025-04-28
Response Speed
18.7 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
8930
Large Language Model Intelligence Level
Coding Index
8210
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
81.2
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
62.5
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
73.1
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
69.2
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
70.3
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
75.3
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
68.4
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
58.7
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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