OpenAI: o4 Mini
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.
by OpenAI
Input type: Output type
Publish time: 2025-04-16
Recent activity on o4 Mini
Tokens processed per day
Throughput
(tokens/s)
| Providers | Min (tokens/s) | Max (tokens/s) | Avg (tokens/s) |
|---|---|---|---|
| Azure | 39.1 | 119.31 | 92.05 |
First Token Latency
(ms)
| Providers | Min (ms) | Max (ms) | Avg (ms) |
|---|---|---|---|
| Azure | 2322 | 12522 | 7419.67 |
Uptime stats for o4 Mini
Uptime stats for o4 Mini across all providers
Uptime
Azure | OpenAI
( Percent )
Providers for o4 Mini
ZenMux Provider to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.
Price
| Type | Cost |
|---|---|
| Input | $ 1.1/ M tokens |
| Output | $ 4.4/ M tokens |
| Cache read | $ 0.275/ M tokens |
| Cache write 5m | - |
| Cache write 1h | - |
| Cache write | - |
| Web search | - |
| Image | - |
| Image input | - |
| Image output | - |
| Image cache | - |
| reasoning | - |
| Video | - |
| Audio | - |
| Audio cache read | - |
| Audio input | - |
| Audio & Video | - |
| Image search | - |
| Web search image | - |
Model limitation
| Type | Value |
|---|---|
| Context | 200.00K |
| Max output | 100.00K |
Supported Parameters
- max_completion_tokens
- temperature
- top_p
- frequency_penalty
- presence_penalty
- seed
- logit_bias
- logprobs
- top_logprobs
- response_format
- stop
- tools
- tool_choice
- parallel_tool_calls
Model Protocol Compatibility
- OpenAI Chat Completions
- OpenAI Responses
- OpenAI Embeddings
- Anthropic Messages
- Google Gemini
- Google Imagen
- Google Video
Sample code and API for o4 Mini
ZenMux normalizes requests and responses across providers for you.
from openai import OpenAI
client = OpenAI(
base_url="https://zenmux.ai/api/v1",
api_key="<ZENMUX_API_KEY>",
)
# Chat Completion
completion = client.chat.completions.create(
model="openai/o4-mini",
messages=[
{
"role": "user",
"content": "What is the meaning of life?"
}
]
)
print(completion.choices[0].message.content)