O3 Mini
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OpenAI | 200,000 token context | $1.10/M input, $4.40/M output. OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the reasoning_effort parameter, which can be set to "high", "medium", or "low" to control the thinking time of the model. The default is "medium". OpenRouter also offers the model slug openai/o3-mini-high to default the parameter to "high". The model features three adjustable reasoning effort levels and supports key developer capabilities including function calling,…OpenAI | 200,000 token context | $1.10/M input, $4.40/M output. OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the reasoning_effort parameter, which can be set to "high", "medium", or "low" to control the thinking time of the model. The default is "medium". OpenRouter also offers the model slug openai/o3-mini-high to default the parameter to "high". The model features three adjustable reasoning effort levels and supports key developer capabilities including function calling, structured outputs, and streaming, though it does not include vision processing capabilities. The model demonstrates significant improvements over its predecessor, with expert testers preferring its responses 56% of the time and noting a 39% reduction in major errors on complex questions. With medium reasoning effort settings, o3-mini matches the performance of the larger o1 model on challenging reasoning evaluations like AIME and GPQA, while maintaining lower latency and cost.
Guides for this model
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Captain's notes
Notes from 2026-10-13
o3-mini is OpenAI's cost-trimmed reasoning model aimed squarely at STEM work: science, math, and coding. Its headline feature is an adjustable reasoning effort dial — low, medium, or high — so you pay for only as much thinking as the task needs. At medium effort it matches the larger o1 on hard reasoning benchmarks, which makes it a practical default for developers who want reasoning-class results without reasoning-class bills.
Known for
- Three reasoning effort levels let you trade speed and cost against answer quality.
- Matches o1 on AIME and GPQA math and science benchmarks at medium effort.
- 39% fewer major errors on complex questions compared to its predecessor.
- Full developer toolkit: function calling, structured outputs, and streaming.
- 200,000-token context window handles long codebases and technical documents.
Watch out for
- No vision support — it cannot process images, so multimodal tasks need a different model.
- Weights are closed; you rent access via API and cannot run it locally.
- Output tokens cost four times the input rate, so verbose reasoning chains add up.
Prompts to try
Debug a tricky recursion
Here is a recursive Python function that computes the number of ways to tile a 2×N board with 1×2 dominoes. It gives wrong answers for N greater than 20. Find the bug, explain why it occurs, and give a corrected version with a brief explanation of the fix:
def tile(n):
if n == 0: return 1
if n == 1: return 1
return tile(n-1) + tile(n-2)
Prove a combinatorics result
Prove that for any positive integer n, the sum of the first n odd numbers equals n squared. Give a proof by induction, then a second proof using a geometric or combinatorial argument. Keep each proof self-contained and explain every step.
Plan a multi-step agent
Design a minimal tool-calling agent that answers questions about a local SQLite database. List the tools it needs, the JSON schema for each tool call and response, the decision loop in pseudocode, and the two or three failure modes you would guard against first.
Compare with
- gpt-oss-120bOpen-weights model131,072 token context · $0.15/M input, $0.60/M output
- gpt-oss-20bOpen-weights model131,072 token context · $0.02/M input, $0.09/M output
- GPT-5.6 SolAPI-only model1,050,000 token context · $4.00/M input, $20.00/M output
- GPT-6.1 SolAPI-only model1,050,000 token context · $2.00/M input, $10.00/M output
More text models from openai
Questions people ask
- Can I run o3-mini locally or download the weights?
- No. o3-mini is an API-only model from OpenAI; the weights are not public and there is no way to run it on your own hardware.
- What does o3-mini cost and how large is its context?
- It costs $1.10 per million input tokens and $4.40 per million output tokens, with a 200,000-token context window.
- How does o3-mini differ from o1 and from o3?
- o3-mini is smaller and cheaper than both, but at medium reasoning effort it matches o1 on hard math and science benchmarks; it trades some ceiling for lower latency and cost, and it adds the reasoning_effort parameter that o1 does not expose.
Captain's verdictThe reasoning effort dial is the real selling point: sensible default, easy to crank up when a problem earns it.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
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Updated Oct 22, 2026
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O3 Mini is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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