o4-mini

o4-mini is a compact member of OpenAI's o-series reasoning models, sitting alongside the larger o3 in a family focused on multi-step thinking rather than single-shot answer generation. According to launch-day coverage, it is trained to reason, plan, and take action in one workflow, breaking down a question, choosing an approach, and walking through its logic before responding. This positions the model less as a fast autocomplete and more as a strategic collaborator suited to tasks that benefit from explicit deliberation, such as research synthesis, analytical writing, and stepwise problem solving.

Practically, o4-mini is aimed at developers and teams that want reasoning-grade behavior in a smaller, more cost-efficient footprint than the full o3. The model is documented in OpenAI's official API model catalog, confirming it as a supported endpoint for production use, and launch reporting frames it as a workhorse model built to "get things done" rather than a toy or demo. It is best fit for workflows where transparent step-by-step reasoning, tool integration, and reliable execution matter more than raw conversational fluency, making it a strong choice for agent-style pipelines, structured analysis, and complex multi-turn task automation.

Impossiblopenai/o4-minio-mini

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Quick Info

Provider: Impossibl
Model key: openai/o4-mini
Release date: Apr 16, 2025
Last updated: Apr 16, 2025
Knowledge cutoff: 2024-05
Input modalities
Output modalities
Capabilities

Cost

Input token cost: $1.10
Output token cost: $4.40

Limits

Output tokens: 100,000 tokens
Context window: 200,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about o4-mini

DevPass (LLM Gateway)

CoverageBenchmark

Independent benchmark aggregator llm-stats.com (page date September 22, 2026) ranks OpenAI's o4-mini at #146 on its composite LLM Stats Score, with category standings including Chat (58 of 133), Healthcare (70 of 245), Vision (91 of 210), Math (97 of 327), Reasoning (138 of 366), Writing (62 of 114), Search (65 of 90), The page enumerates specific benchmark scores for o4-mini sourced to openai.com, including AIME 2024 (rank 2, score 0.93/1, accuracy without tools), AIME 2025 (rank 36, score 0.93/1, accuracy without tools), MathVista (rank 5, score 0.84/1), and MMMU. These figures provide a quantitative snapshot of o4-mini's mathemati

OpenRouter

Coverage

Wikiwand's encyclopedia entry for OpenAI o4-mini corroborates the model's timeline: initial release on April 16, 2025, broader rollout including free-tier ChatGPT users on April 24, 2025, retirement announced January 29, 2026, and effective ChatGPT retirement on February 13, 2026. It identifies the predecessor as o3-mi The entry confirms o4-mini was a vision-language model capable of processing text and images, including analyzing whiteboard sketches during its chain-of-thought phase. Both o4-mini and o4-mini-high were released as vision-language models, distinguishing o4-mini from earlier text-only o-series entries. The page is a th

Model Oracle AI

Coverage

A Hacker News comment dated April 16, 2025 from an OpenAI employee (tedsanders) clarifies that in ChatGPT, o4-mini is replacing o3-mini as a straight 1-to-1 upgrade, while in the API o4-mini is introduced as a new model option with o3-mini continuing to be supported so developers who built products atop o3-mini can mai The same thread explains the rationale behind keeping both models available: launching a new model would otherwise risk breaking production apps, and developers are given the option to test both and switch when they like. The commenter also notes that naming and offering improvements were being considered later in 2025

Impossibl

CoverageBenchmark

benchmarklist.com's aggregated o4-mini page lists detailed per-eval scores that are useful as secondary technical context. On SLDBench (dated 2026-05-06), o4-mini posts a mean reward R2 of 0.62, ranking 1 of 12 (100th percentile), with strong sub-scores on data-constrained scaling law R2 (0.91), domain mixture scaling Additional eval data from the same page shows o4-mini at 82.0% overall on AgentRewardBench (2 of 57, 75th percentile), with 100.0% on AssistantBench, plus pricing listed at $1.10 per 1M input tokens and $4.40 per 1M output tokens, consistent with the llm-stats.com figure. These numbers are relevant for developers evalu