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O1 Mini
O1 Mini
O1-mini is a cost-effective language model developed by OpenAI, designed specifically for advanced reasoning tasks while minimizing the use of computing resources.
Intelligence(Medium)
Speed(Fast)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
65,536
Maximum Output Tokens
2023-09-01
Knowledge Cutoff
Pricing
￥21.6 /M tokens
Input
￥86.4 /M tokens
Output
￥13.86 /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
128.00k tokens
Training Data Cutoff
2023-09-01
Open Source Category
Proprietary
Multimodal Support
Text Only
Throughput
115
Release Date
2024-09-12
Response Speed
211.91,708 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
5382
Large Language Model Intelligence Level
Coding Index
4493
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
74.2
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
60.3
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.9
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
57.6
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
32.3
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
97.2
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
94.4
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
60.3
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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