o3 Mini API: Endpoint & Pricing
Deprecated 23 Oct 2026o3 Mini is available on LLM.API as an OpenAI-compatible chat endpoint—route OpenAI quality through one key with transparent token pricing.
What is o3 Mini?
o3 Mini is an OpenAI chat model exposed on LLM.API under id `o3-mini`. Compact o3 reasoning model balancing performance and cost for complex tasks. Teams use it when they need reliable text generation with text, image inputs and text outputs over an OpenAI-compatible API.
Can you self-host o3 Mini?
No. We found no public weights for o3 Mini on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Skip the deploy — use LLM.API as your endpoint
No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.
Providers
LLM.API routes o3 Mini to the providers below, with discounted effective rates versus list price.
List price by provider
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $1.1; out $4.4 per 1M tokens | 200K tokens | vision, streaming, reasoning, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.
Try this model
Test o3 Mini right here — free to start.
Suggestions for your first prompt
Code snippet
Call o3 Mini through the OpenAI-compatible API — POST /v1/chat/completions.
Switching from OpenAI? Change 2 lines.
- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"Everything else stays the same — SDK, message format, tools, streaming. Set model to o3-mini and the call runs unchanged.
curl https://api.llmapi.ai/v1/chat/completions \
-H "Authorization: Bearer $LLMAPI_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "o3-mini",
"messages": [
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "none"
}'from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAPI_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="o3-mini",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
reasoning_effort="none",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.LLMAPI_KEY,
baseURL: "https://api.llmapi.ai/v1",
});
const resp = await client.chat.completions.create({
model: "o3-mini",
messages: [
{ role: "user", content: "Give me three crisp launch checklist items." },
],
});
console.log(resp.choices[0].message.content);stream = client.chat.completions.create(
model="o3-mini",
messages=[{"role": "user", "content": "Write a haiku about latency."}],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True){
"model": "o3-mini",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "none"
}ENDPOINTS
Supported endpoints & features
Base URL https://api.llmapi.ai/v1 · model ID o3-mini · OpenAI-compatible.
POST /v1/chat/completionsChat Completions — supported
| Feature | Status | How to set it |
|---|---|---|
| Function calling (tools) | Not supported | tools, tool_choice |
| Parallel tool calls | Not supported | parallel_tool_calls |
| JSON mode | Supported | response_format: {"type": "json_object"} |
| Structured outputs | Supported | response_format: {"type": "json_schema", …} |
| Streaming | Supported | stream: true |
| Vision (image input) | Supported | image_url content parts |
| Web search | Not supported | web_search |
| Reasoning effort | none · low · medium · high | reasoning_effort: "none" |
ERRORS
Errors & fallback routing
| Code | What it means | What to do |
|---|---|---|
401 | Missing or invalid API key. | Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active. |
429 | Rate limited, or the upstream provider is throttling the request. | Back off and retry with jitter; the gateway also retries the request on another provider where one is available. |
5xx | Upstream provider error or timeout. | Retry; fallback routing sends the retry to the next healthy provider for this model. |
When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.
o3 Mini in production
What the model is good at.
Multilingual drafting
Drafts and translates professional content across major business languages. Reflects OpenAI positioning for this endpoint.
Tool-ready dialogue
Works well in agent loops that call functions, browsers, or retrieval APIs.
Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics. Tuned to how teams typically call o3 Mini.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Grounded in the model's chat role rather than generic chat claims.
Multi-step reasoning
Breaks down complex problems into intermediate steps before answering. Tuned to how teams typically call o3 Mini.
What you get when you call o3 Mini through LLM.API.
Unified AI Routing
Practical: Reach o3 Mini and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.Cost Control
Compare provider price points and keep spend visible as you scale o3 Mini.
More performance, less spend.Reliability Layer
Production: Retry and route across configured providers when a single upstream blips.
Never go dark on users.Observability
Practical: Trace prompts, tokens, and errors for o3 Mini alongside the rest of your stack.
See every token, everywhere.Drop-in SDKs
Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.Model Breadth
Swap o3 Mini for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.
When to Use — When NOT to Use
Use it if...
- You need provider failover options exposed for this model id (o3 Mini)
- You want OpenAI-compatible chat completions through a single LLM.API key (o3 Mini)
- Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (o3 Mini)
- You need a general-purpose text model for assistants, agents, or content workflows (o3 Mini)
Avoid if...
- You need guaranteed real-time hard latency SLAs without benchmarking the provider
- You need pure embedding, OCR, or media generation instead of chat
- Your use case depends on unpublished proprietary benchmarks not listed here
- You require on-prem only deployment with no cloud inference
BENCHMARKS
o3 Mini benchmark scores
Intelligence index
Scale: 0-100 index points
Output speed
Scale: 0-400 tokens per second
Reference price per 1M tokens
Bars compare input and output list prices for this model.
Higher is better. The median covers every model tracked by Artificial Analysis.
Measured throughput; the full dial is 300 tokens per second.
Provider list prices, before the LLM.API discount.
Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.
COMMUNITY
What developers say about OpenAI models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to o3 Mini.
- Long-horizon coding reports on GPT-6 Astra describe a clear step up over the GPT-5.x line, with the higher reasoning tiers seen as the sweet spot for planning and implementation in large (100K+ LOC) codebases.
- The same field reports note that fast/ultra modes burn quota quickly and that very large refactors still stall, so teams tend to mix a cheap tier for routine calls with a reasoning tier for hard steps.
- Community threads temper the hype: capability gains are acknowledged, but developers still report the usual failure modes on obscure reverse-engineering and modding work.
SOURCES
Frequently Asked Questions
What is o3 Mini?
Compact o3 reasoning model balancing performance and cost for complex tasks. On LLM.API it is addressed as `o3-mini`.
What is the context length for o3 Mini?
Reported context for o3 Mini is 200K tokens. Always verify the active provider row if multiple providers are listed.
When should I choose o3 Mini?
You need a general-purpose text model for assistants, agents, or content workflows — especially when you specifically need o3 Mini.
How is o3 Mini priced on LLM.API?
Listed pricing metadata shows: In $1.1 / 1M tokens · Out $4.4 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Can I use tools or structured outputs with o3 Mini?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
Is o3 Mini a chat model?
Yes—o3 Mini is exposed as a chat/completions-style endpoint on LLM.API.
Where is the canonical page for o3 Mini?
https://llmapi.ai/models/openai-o3-mini/
What modalities does o3 Mini support?
o3 Mini accepts text, image and produces text according to its architecture metadata on LLM.API.
Which providers serve o3 Mini?
LLM.API currently lists: openai. Availability can vary by region and account.
What are limitations of o3 Mini?
Like other API models, o3 Mini can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
How do I call o3 Mini via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "o3-mini" and your LLM.API key. See the code snippet on this page.
Does o3 Mini support streaming?
Yes—at least one listed provider advertises streaming.
COMPARE
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Qwen Coder Plus
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