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GPT-4o mini Transcribe is a speech-to-text model developed by OpenAI that uses the GPT-4o mini architecture to convert spoken audio into written text. It is designed to deliver improved word error rates and more accurate language recognition compared to the original Whisper-based transcription models. The model is part of OpenAI's transcription API offerings and became available in 2025.

GPT-4o Transcribe is a speech-to-text model developed by OpenAI that uses the GPT-4o model architecture to convert spoken audio into written text. It is part of OpenAI's audio model lineup and was introduced as an improvement over the original Whisper-based transcription models, offering a lower word error rate and more accurate language recognition across a broader range of languages.

GPT-4o-mini TTS is a text-to-speech model developed by OpenAI that converts written text into natural-sounding spoken audio. It belongs to the GPT-4o mini family, which is designed to deliver capable output at a smaller computational footprint than full-scale variants. The model is accessible to developers through the OpenAI API and is intended for programmatic speech generation across a range of applications. It accepts a text input of up to 2,000 characters and returns audio output in a synthesized voice.

GPT-5.4 is a text generation model developed by OpenAI, released in March 2026 as their flagship model for professional and enterprise use. It is available in three variants — standard, Thinking, and Pro — and features a context window of 1 million tokens, the largest OpenAI has offered. The model is designed not only to plan complex tasks but to complete them reliably, with built-in computer use capabilities for orchestrating multi-step agentic workflows.

Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.

Compare GPT 5.5 and GPT 5.4 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.