OpenAI: o4 Mini High
o4 Mini High is a variant of OpenAI's o4 Mini with vision, tool use, and function calling support, but it carries no independent benchmark data of its own. While it benefits from a reputable provider, it cannot be scored on capability alone, making it suitable only for exploratory or lower-stakes use cases.
Assessment date: March 14, 2026
Our methodology takes into account a range of factors including pricing, functionality, capabilities, benchmark performance, and real-world applicability. Rankings are reviewed and updated regularly as new models are released. Issues with our rankings? Contact us
OpenAI o4-mini-high is the same model as o4-mini with reasoning_effort set to high. OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains. Despite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.
Capabilities
Architecture
| Modality | Text + Image + File → Text |
| Tokenizer | GPT |
Model Information
Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $1.10 | $0.001100 |
| Output | $4.40 | $0.004400 |
Live Performance
Live endpoint metrics — refreshed every 30 minutes.
Leaderboard Categories
External Resources
Data sourced from OpenRouter API, Artificial Analysis and Hugging Face Open LLM Leaderboard. Scores are editorially curated by our team.
Last updated: March 15, 2026 7:52 pm