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Hunyuan TurboS Vision

A multi-modal lightweight model developed by Tencent Hunyuan Team, which supports video clip input and real-time API parameterized interaction, and optimizes long text processing and cross-modal reasoning capabilities. It is suitable for image-text understanding scenarios. It is a new-generation flagship large vision-language model based on the latest Hunyuan turbos, focusing on tasks related to image-text understanding, including entity recognition based on images, knowledge Q&A, copywriting creation, photo-based problem-solving, etc. It has been comprehensively improved compared with the previous generation of models.
Intelligence(Relatively Strong)
Speed(Slow)
Input Supported Modalities
Yes
Is Reasoning Model
8,000
Context Window
2,000
Maximum Output Tokens
2024-10-31
Knowledge Cutoff
Pricing
¥3 /M tokens
Input
¥9 /M tokens
Output
¥8 /M tokens
Blended Price
Quick Simple Comparison
Hunyuan-T1-20250403
¥0.14
Hunyuan-Vision
¥2.5
HunYuan-TurboS
¥0.11
Basic Parameters
GPT-4.1 Technical Parameters
Parameter Count
Not Announced
Context Length
8,000 tokens
Training Data Cutoff
2024-10-31
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
850
Release Date
2025-04-07
Response Speed
18.6 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
7530
Large Language Model Intelligence Level
Coding Index
6840
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
71.4
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
HLE
-
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
SciCode
-
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
65.3
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
AIME Score
-
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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