o4 Mini: gPT-5 mini succeeded o4-mini as a newer small reasoning model; this profile remains specific to o4-mini
OpenAI evolves its reasoning model hierarchy with the release of GPT-5 mini.
GPT-5 mini succeeded o4-mini as a newer small reasoning model; this profile remains specific to o4-mini — verified by the OnAirToday AI Progress Tracker against OpenAI's published documentation.
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The OnAirToday AI Progress Tracker recorded a verified change to OpenAI's o4 Mini (version o4-mini): GPT-5 mini succeeded o4-mini as a newer small reasoning model; this profile remains specific to o4-mini.
OpenAI o4-mini is a compact reasoning model supporting text and image inputs, with strengths in mathematics, coding and visual problem-solving. Subsequently succeeded by GPT-5 mini, it has documented standard API pricing of $1.10 per million input tokens and $4.40 per million output tokens.
What changed in this review:
- GPQA Diamond is reported at OpenAI’s published 81.4%, with SWE-bench Verified, AIME and MMMU results added.
- Benchmark coverage is limited to directly documented results for the named model and evaluation.
- Added documented modality limitations, hallucination concerns and tool-use safeguards.
- Flaws and limitations updated
- Risks and threats updated
Teams building on o4 Mini should confirm limits, pricing and benchmark coverage against the vendor's own documentation before relying on these figures.
The full profile — capabilities, benchmark citations, known flaws and risk guidance — is on the tracker at /ai-models/o4-mini.
References:
- https://platform.openai.com/docs/models/o4-mini
- https://openai.com/index/introducing-o3-and-o4-mini/
- https://openai.com/index/o3-o4-mini-system-card/
- https://openai.com/index/introducing-gpt-5-for-developers/
Generated insight by OnAirToday AI
The transition from o4-mini to GPT-5 mini signals a strategic shift in OpenAI's pursuit of high-performance reasoning at a reduced computational footprint. This evolution highlights the rapid iteration cycle of small language models aimed at delivering complex cognitive capabilities to resource-constrained environments.

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