gpt-4o-audio-preview-2025-06-03gpt-4o-audio-preview adds support for audio inputs, allowing the model to understand nuances in audio recordings and enrich responses. It currently does not generate audio outputs, and audio input is billed per million audio tokens.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="gpt-4o-audio-preview-2025-06-03", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="gpt-4o-audio-preview-2025-06-03",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelinputvoiceresponse_formatspeedinstructionsstream_formatUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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GPT-4o Mini TTS is OpenAI's cost-efficient text-to-speech model, designed to convert text into natural-sounding audio output. It supports a variety of voices and tones, enabling flexible and expressive speech generation. Optimized for scalability and low cost, it is well suited for real-time voice applications, content narration, and high-volume audio generation workflows.
GPT-4o Mini Transcribe is a smaller, cost-efficient speech-to-text model built on GPT-4o Mini's audio capabilities. It is designed for high-volume transcription workloads, delivering reliable performance with lower cost and latency. Priced per token (input and output), it provides transparent, fine-grained billing, making it well suited for scalable transcription pipelines, real-time applications, and cost-sensitive deployments.
GPT-4o Transcribe is OpenAI's high-quality speech-to-text model built on GPT-4o's audio capabilities. It delivers accurate transcription with strong language understanding, making it suitable for a wide range of audio processing tasks. Priced per token (input and output), it offers transparent, fine-grained billing, making it well suited for workflows that require scalable transcription, integration with LLM pipelines, and cost-aware processing.
Whisper Large V3 Turbo is an optimized version of OpenAI's Whisper Large V3 speech recognition model, designed for high-speed and cost-efficient transcription. It supports 99+ languages and accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg. With a ~12% word error rate and real-time speed factors up to 216×, it delivers fast, scalable performance for latency-sensitive and high-throughput transcription workloads, making it ideal for real-time and large-scale speech processing applications.
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GPT-5.4 is OpenAI's latest frontier model, unifying the GPT and Codex lines into a single system designed for both general intelligence and advanced software engineering workflows. It supports text and image inputs and features a 1M+ token context window (≈922K input, 128K output), enabling high-context reasoning, coding, and multimodal analysis within a single workflow. The model delivers improved performance in coding, document understanding, tool use, and instruction following, and is designed as a strong default for complex tasks. It can generate production-quality code, synthesize information across large datasets, and execute multi-step workflows with fewer iterations and greater token efficiency.
GPT-5.1 is the full-capability successor to GPT-5, offering stronger general reasoning, better instruction following, and a more natural conversational style. It uses adaptive reasoning to stay fast on simple questions while thinking more deeply on complex tasks, producing clearer, more grounded explanations. It shows steady improvements across math, coding, and structured analysis, with more coherent long-form output and more reliable tool use.
GPT-5.1 is the full-capability successor to GPT-5, offering stronger general reasoning, better instruction following, and a more natural conversational style. It uses adaptive reasoning to stay fast on simple questions while thinking more deeply on complex tasks, producing clearer, more grounded explanations. It shows steady improvements across math, coding, and structured analysis, with more coherent long-form output and more reliable tool use.
GPT-5.4 mini brings the core capabilities of GPT-5.4 into a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs and delivers strong performance across reasoning, coding, and tool use, while reducing latency and cost for large-scale deployments. Designed for production environments, GPT-5.4 mini balances capability and efficiency, making it well suited for chat applications, coding assistants, and scalable agent workflows. It provides reliable instruction following, solid multi-step reasoning, and consistent performance across diverse tasks with improved cost efficiency.
Initialized observational baseline with no recorded failures