%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Nova Premier
Nova 系列的頂級模型，專為高性能應用設計。作為 Premier 級別的模型，具備先進的 AI 能力和企業級功能。雖然具體技術細節有限，但定位為提供卓越性能的高端解決方案。適合對 AI 能力要求極高的專業應用場景，能夠處理最複雜和最具挑戰性的任務。
Intelligence(Medium)
Speed(Relatively Slow)
Input Supported Modalities
Yes
Is Reasoning Model
1,000,000
Context Window
-
Maximum Output Tokens
-
Knowledge Cutoff
Pricing
- /M tokens
Input
- /M tokens
Output
￥36 /M tokens
Blended Price
Quick Simple Comparison
Nova Premier
Nova Pro
￥0.8
Nova Lite
￥0.06
Basic Parameters
Nova PremierTechnical Parameters
Parameter Count
Not Announced
Context Length
1.0M tokens
Training Data Cutoff
Open Source Category
Proprietary
Multimodal Support
Text Only
Throughput
Release Date
2025-04-30
Response Speed
83.56,819 tokens/s
Benchmark Scores
Below is the performance of Nova Premier in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
42.56
Large Language Model Intelligence Level
Coding Index
29.82
Indicator of AI model performance on coding tasks
Math Index
50.47
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
73.3
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
56.9
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.7
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
31.7
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
27.9
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
83.9
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
17
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
GPT 4
openai
￥216
Input tokens/million
￥432
Output tokens/million
8192
Context Length