O

O1

The research preview model focuses on mathematical and logical reasoning abilities, demonstrating performance improvements in tasks that require step-by-step reasoning, mathematical problem-solving, and code generation. This model shows stronger capabilities in formal reasoning while maintaining strong general abilities.
Intelligence(Relatively Strong)
Speed(Medium)
Input Supported Modalities
No
Is Reasoning Model
200,000
Context Window
100,000
Maximum Output Tokens
2024-01-01
Knowledge Cutoff
Pricing
¥108 /M tokens
Input
¥432 /M tokens
Output
¥189 /M tokens
Blended Price
Quick Simple Comparison
GPT-4.1
¥2
GPT-4.1 mini
¥0.4
GPT-4.1 nano
¥0.1
Basic Parameters
GPT-4.1 Technical Parameters
Parameter Count
Not Announced
Context Length
200.00k tokens
Training Data Cutoff
2024-01-01
Open Source Category
Proprietary
Multimodal Support
Text Only
Throughput
66
Release Date
2024-12-05
Response Speed
129.26,375 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
6189
Large Language Model Intelligence Level
Coding Index
5185
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
84.1
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
74.7
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
7.7
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
67.9
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
35.8
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
97.4
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
97
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
72.3
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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