o4-mini

Summary

o4-mini is OpenAI's compact reasoning model — successor to o3-mini, optimized for coding and visual reasoning. With a 200K context window, 100K max output, and chain-of-thought reasoning, it brings cost-efficient inference to production agentic workflows.

Overview

o4-mini is OpenAI's latest small reasoning model — compact, fast, and surprisingly powerful for its size and price. It is optimized specifically for coding and visual reasoning tasks, where it delivers performance well above its cost tier. As the successor to o3-mini in the o-series lineage, o4-mini brings OpenAI's chain-of-thought reasoning capabilities to a price point accessible for production-scale agentic pipelines that need to think, not just complete.

In OpenAI's lineup, o4-mini fills the role of a reasoning-capable workhorse: when a task genuinely needs step-by-step logical inference but you don't want to pay for full GPT-5.2 Thinking or o3, o4-mini is the practical choice.

Specifications

  • Developer: OpenAI
  • Model String: o4-mini
  • Release Date: 2025
  • Type: Reasoning-optimized Large Language Model, Multimodal (text + vision)
  • Context Window: 200,000 tokens
  • Max Output: 100,000 tokens
  • Access: OpenAI API, ChatGPT (Plus/Team/Enterprise), Azure OpenAI Service
  • Pricing: See OpenAI pricing page for current rates

Capabilities

Reasoning: Uses OpenAI's chain-of-thought approach to work through problems step by step before responding. Effective for math, logic, multi-step planning, and problems where the answer isn't immediately obvious from pattern matching.

Coding: Strong coding performance for its size — OpenAI highlights it as particularly effective for software engineering tasks, debugging, and code reasoning. A good choice for coding agents that need to reason about complex problems without paying full GPT-5.2 Codex rates.

Visual Reasoning: Handles image inputs with a reasoning layer — can analyze visual information and reason about it, not just describe it.

Large Output Window: 100,000 token max output — useful for generating large code files, detailed analyses, or long structured documents in a single response.

Limitations

The 200K context window is smaller than GPT-4.1 and GPT-4.1 mini (1M tokens), which limits its use for very large codebase analysis or long-document tasks. Reasoning models also have higher latency than non-reasoning models — each response involves an internal thinking step before output, which adds time. For straightforward tasks that don't require reasoning, GPT-4.1 mini will be faster and cheaper.

Recent Developments

  • Succeeded o3-mini: Released as the next generation of OpenAI's small reasoning model, with improved coding and visual capabilities over its predecessor.
  • Agentic Use Cases: OpenAI has positioned o4-mini as particularly strong in multi-step agentic workflows where the model needs to reason through decisions, not just execute instructions.

Last Updated

February 26, 2026

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