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Sensenova V6 Pro
Sensenova V6 Pro
The latest large model with a 600-billion-parameter multimodal MoE architecture released by SenseTime, which supports real-time video inference and cross-modal content generation
Intelligence(Strong)
Speed(Slow)
Input Supported Modalities
Yes
Is Reasoning Model
256,000
Context Window
56,000
Maximum Output Tokens
2025-04-01
Knowledge Cutoff
Pricing
￥2.8 /M tokens
Input
￥8.4 /M tokens
Output
￥4.2 /M tokens
Blended Price
Quick Simple Comparison
SenseNova V6 Pro
￥0.39
SenseNova V6 Reasoner
￥0.56
Basic Parameters
GPT-4.1 Technical Parameters
Parameter Count
Not Announced
Context Length
256.00k tokens
Training Data Cutoff
2025-04-01
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
1,800
Release Date
2025-04-10
Response Speed
38.6 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
9370
Large Language Model Intelligence Level
Coding Index
9120
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
88.5
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
82.3
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
89.1
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
87.9
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
86.7
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
90.2
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
84.1
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
85.4
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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