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o4-mini

OpenAImid

OpenAI's efficient reasoning model that balances o3-level thinking with significantly lower cost and latency. Matches o3 on many reasoning benchmarks while being faster and cheaper. The recommended reasoning model for most production use cases.

Released 2025-12-11Knowledge cutoff: 2025-06
Medium confidence|Updated 74d ago|89% source confidence

Specifications

Context Window

200K tokens

Max Output

100K tokens

Input Price

$0.550 / 1M tokens

Output Price

$2.20 / 1M tokens

Latency Tier

Moderate (speed score: 6/10)

Capability Profile

Reasoning
9/10
Coding
8.5/10
Factuality
8.5/10
Structured Output
8/10
Instruction Following
8/10
Safety & Enterprise
8/10
Cost Efficiency
7.5/10
Tool Use
7.5/10
Long Context
7/10
Speed
6/10
Creativity
5.5/10
Conversational
5.5/10
Multimodal
4.5/10

Feature Support

Vision Yes
Audio In No
Audio Out No
Video No
Image Generation No
Image Editing No
Function Calling Yes
JSON Mode Yes
Structured Output Yes
Streaming Yes
Reasoning Yes
Realtime No
Computer Use No
Web Search No

Best Use Cases

Cost-effective reasoning for math, logic, and STEM problems
Code generation requiring multi-step planning and analysis
Automated evaluation and grading systems where correctness matters
Research assistance requiring careful step-by-step analysis
Agentic coding workflows (SWE-bench class tasks)

Not Ideal For

Casual chat or conversational AI (use GPT-5.4-mini)
Multimodal-heavy tasks (limited vision, no audio)
Simple classification where reasoning overhead is wasteful
Creative writing or brainstorming

Strengths

Best cost-to-reasoning-quality ratio in the market
Approaches o3 on most benchmarks at ~55% of the cost
Adjustable reasoning effort for speed-quality tradeoff
Solid function calling — better than o3 for agentic use

Weaknesses

Still slower than non-reasoning models (5-30s typical)
Visible gap vs o3 on the hardest competition-math problems
Creative writing is mediocre
Reasoning token overhead makes it expensive for trivial tasks

Edge Cases & Notes

At 'low' reasoning effort, speed approaches normal models but accuracy on hard problems drops significantly
SWE-bench Verified performance is surprisingly strong — competitive with GPT-5.4 on agentic coding
Hidden reasoning tokens can balloon on adversarial or very long prompts

Provider Notes

Recommended over o3 for most reasoning tasks unless the absolute hardest problems are involved. Available via OpenAI API with Tier 2+ access. Batch API available.

Benchmarks

MMLU90.1%
HumanEval92%
Arena Elo1370
GSM8K97.8%

Benchmark Notes

GSM8K 97.8%, AIME ~72%. Remarkably close to o3 on most benchmarks. SWE-bench Verified ~55%. Cost-adjusted performance is class-leading for reasoning.

Research Meta

Last Evaluated

2026-03-15

Source Confidence

89%

Evaluation Method

LMSYS Arena, SWE-bench, AIME, GPQA, cost-quality analysis

Needs Re-evaluation

No

Sources

  • OpenAI o4-mini announcement
  • LMSYS Chatbot Arena
  • Independent reasoning evaluations

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