GPT-5.4 mini and nano followed GPT-5.2; this profile remains specific to GPT-5.2
Unpacking the technical trajectory of OpenAI's latest miniature and lightweight model iterations.
GPT-5.4 mini and nano followed GPT-5.2; this profile remains specific to GPT-5.2 — 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 GPT-5.2 (version gpt-5.2): GPT-5.4 mini and nano followed GPT-5.2; this profile remains specific to GPT-5.2.
GPT-5.2 is an OpenAI reasoning model supporting text and image inputs, long-context analysis, coding, and tool-driven workflows. Its published launch evaluations show improvements over GPT-5.1, although benchmark results depend on reasoning settings and do not guarantee reliable autonomous operation.
What changed in this review:
- GPQA Diamond and SWE-bench Verified now use OpenAI's GPT-5.2 Thinking launch results, not unattributed evaluation configurations.
- Added published AIME 2025 and ARC-AGI-2 launch results.
- Removed unsupported benchmark entries; differing evaluation configurations should not be interpreted as performance changes.
- Added later-family security reports without attributing their incidents to GPT-5.2.
- Prices are US dollars per million tokens for standard API input and output, excluding cached-input discounts.
- Flaws and limitations updated
Teams building on GPT-5.2 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/gpt-5-2.
References:
- https://openai.com/index/introducing-gpt-5-2/
- https://platform.openai.com/docs/models/gpt-5.2
- https://openai.com/api/pricing/
- https://openai.com/index/introducing-gpt-5-4-mini-and-nano
Generated insight by OnAirToday AI
The introduction of specialized mini and nano variants alongside the GPT-5 series indicates a strategic shift toward highly efficient, task-specific large language models. This architectural diversification allows for massive cost reduction and edge deployment without compromising the core reasoning capabilities of the 5.x lineage.

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