OpenAI
OpenAI o3-mini-high is the same model as o3-mini with reasoning effort set to high. o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and...
This model is still tracked for research and discovery, but it is excluded from default public rankings until it returns to active status.
59.9
Quality Score
1340
Arena ELO
Undisclosed
Parameters
200K
Context
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Feb 2025
Released
Launches
4
Benchmarks
22
Research
1
General
3
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
Quality: 18.4/100 | Price: $1.925/M tokens | Output: 230.318 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Introducing GPT‑5 for developers | OpenAI Skip to main content Research Products Business Developers Company Foundation (opens in a new window) Log in Try ChatGPT (opens in a new window) Research Products Business Developers Company Foundation (opens in a new window) Try ChatGPT (opens in a new window) Login OpenAI August 7, 2025 Product Introducing GPT‑5 for developers The best model for coding and agentic tasks. Loading… Share Introduction Introduction Coding Frontend engin
We’re sharing new research on a method for anticipating how models may behave in real-world use before release: simulating deployment with recent, de-identified user requests and studying candidate model responses. https://t.co/7RJzBfNniQ
Quality: 18.4/100 | Price: $1.925/M tokens | Output: 230.318 tok/s | MMLU: 0.802% | HumanEval: 0.734%
View sourceAs AI takes on longer, higher-stakes tasks, we want models to carry beneficial and safe behavior into new domains beyond their training—and maintain it under pressure. That’s the idea behind our new research on training models to be broadly and persistently beneficial.

Introducing LifeSciBench, a benchmark for measuring and improving how well AI supports real-world life science research. Developed with 173 scientists from biotechnology and pharmaceutical research, LifeSciBench includes 750 expert-authored tasks across seven biological research https://t.co/JDkKWcnL9F
We’re sharing new research on a method for anticipating how models may behave in real-world use before release: simulating deployment with recent, de-identified user requests and studying candidate model responses. https://t.co/7RJzBfNniQ
Let’s talk about evals. We’re always looking for better ways to measure and forecast model progress, especially as benchmarks get saturated or gamed. @tejalpatwardhan, who leads our frontier evals team, spoke to @andrewmayne about why evals matter and what models need to be https://t.co/Q3oRCuNxYB
Quality: 18.4/100 | Price: $1.925/M tokens | Output: 235.326 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 18.4/100 | Price: $1.925/M tokens | Output: 191.001 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 18.4/100 | Price: $1.925/M tokens | Output: 211.934 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 236.834 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 232.303 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 219.525 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 209.213 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 208.692 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 187.062 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 188.233 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 188.233 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 212.418 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 211.62 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 226.162 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 229.787 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 180.269 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 169.055 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 161.738 tok/s | MMLU: 0.802% | HumanEval: 0.734%
Quality: 25.2/100 | Price: $1.925/M tokens | Output: 139.125 tok/s | MMLU: 0.802% | HumanEval: 0.734%