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Sensenova V6 Reasoner
A large model dedicated to enhanced reasoning developed by SenseTime, which has the capabilities of multi-modal deep reasoning and long-sequence processing, and supports the analysis of 10-minute video content and complex logical deduction
Intelligence(Medium)
Speed(Slow)
Input Supported Modalities
Yes
Is Reasoning Model
200,000
Context Window
40,000
Maximum Output Tokens
2025-03-31
Knowledge Cutoff
Pricing
￥4 /M tokens
Input
￥16 /M tokens
Output
￥10 /M tokens
Blended Price
Quick Simple Comparison
SenseNova V6 Pro
￥0.39
SenseNova V6 Reasoner
￥0.56
Basic Parameters
SenseNova V6 ReasonerTechnical Parameters
Parameter Count
Not Announced
Context Length
200.00k tokens
Training Data Cutoff
2025-03-31
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
0
Release Date
2025-04-10
Response Speed
0 tokens/s
Benchmark Scores
Below is the performance of SenseNova V6 Reasoner in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
43.5
Large Language Model Intelligence Level
Coding Index
42.8
Indicator of AI model performance on coding tasks
Math Index
75.2
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
88.7
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
81.3
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
91.8
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
89.2
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
87.9
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
90.5
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
86.5
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)
GPT 5 Mini
openai
￥1.8
Input tokens/million
￥14.4
Output tokens/million
400k
Context Length
GPT 5 Standard
openai
￥63
Input tokens/million
￥504
Output tokens/million
400k
Context Length
GPT 5 Nano
openai
￥0.36
Input tokens/million
￥2.88
Output tokens/million
400k
Context Length
GPT 5
openai
￥9
Input tokens/million
￥72
Output tokens/million
400k
Context Length
GLM 4.5
chatglm
￥0.43
Input tokens/million
￥1.01
Output tokens/million
131k
Context Length
Gemini 2.0 Flash Lite (Preview)
google
￥0.58
Input tokens/million
￥2.16
Output tokens/million
1M
Context Length
Gemini 1.0 Pro
google
￥3.6
Input tokens/million
￥10.8
Output tokens/million
33k
Context Length
Qwen2.5 Coder Instruct 32B
alibaba
￥0.65
Input tokens/million
￥0.65
Output tokens/million
131k
Context Length