OpenAI: o4 Mini High✓ Catalog verified
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.
Overview
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.
Access: available through the official OpenAI API. Context window: 200K.
Specifications
| API identifier | openai/o4-mini-high |
| Provider | OpenAI |
| Model type | Multimodal LLM |
| Context window | 200K catalog |
| Input modalities | image · text · file |
| Output modalities | text |
| Released | Apr 16, 2025 |
| Tokenizer | GPT |
| Moderated | Yes |
| Architecture modality | text+image+file->text |
API defaults
{
"top_p": null,
"temperature": null,
"frequency_penalty": null
}Pricing
Live pricing components for openai/o4-mini-high as published in the catalog.
| Pricing component | Raw unit price | Normalized | Unit |
|---|---|---|---|
| Prompt tokens | $0.0000011 | $1.10 / 1M | Per input token |
| Completion tokens | $0.0000044 | $4.40 / 1M | Per output token |
| Request fee | N/A | N/A | Per request |
| Image fee | N/A | N/A | Per image unit |
| Web search fee | $0.01 | $0.010000 | Per search request |
Cost calculator
Estimate monthly spend from your own token volumes.
Estimated Total Cost
$1.43Calculated from current list pricing in the ModelsAtlas catalog.Capabilities
| Capability | Status | What it means |
|---|---|---|
| Visual Understanding | ✓ Supported | Image and document analysis support |
| Audio Processing | Not advertised | Speech and voice aligned flows |
| Tool Calling | ✓ Supported | Supports tools / function calling |
| Self-Hosting | Not advertised | Deploy outside managed APIs |
Capability signals
Directional signals derived from model metadata and capability tags — not official benchmark submissions.
Quick start
Call OpenAI: o4 Mini High through an OpenAI-compatible client.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="openai/o4-mini-high",
messages=[{"role": "user", "content": "Explain quantum physics."}]
)
print(response.choices[0].message.content)Alternatives
Closest models by context window from a different provider.
| Model | Provider | Context | Input /1M | Actions |
|---|---|---|---|---|
| Anthropic: Claude 3 Haiku | Anthropic | 200K | $0.25 | View → |
| Anthropic: Claude 3.5 Haiku | Anthropic | 200K | $0.80 | View → |
Sources & attribution
Pricing and metadata are maintained in the ModelsAtlas catalog.
Frequently asked questions
How much does OpenAI: o4 Mini High cost?
$1.10 per 1M input tokens and $4.40 per 1M output tokens on the official OpenAI API.
What is the context window of OpenAI: o4 Mini High?
OpenAI: o4 Mini High supports up to 200K tokens of context.
Does OpenAI: o4 Mini High support tool / function calling?
Yes, OpenAI: o4 Mini High supports tool / function calling — you can register tools and the model will emit structured tool calls.
How do I access OpenAI: o4 Mini High?
Use the official OpenAI API with the model id `openai/o4-mini-high`. See the Quick start section above for code examples.