OpenAI

The key models as of 2026: GPT-4o (multimodal flagship — text, images, audio, video, PDFs); o3 (advanced reasoning, agentically uses tools including web search, code interpreter, and vision); o4-mini (fast, cost-efficient reasoning for coding and math); GPT-4o-mini (low-cost general-purpose); text-embedding-3-large and text-embedding-3-small (vector embeddings for semantic search). OpenAI crossed $12.7B ARR in Q1 2026, the first AI company to hit $1B monthly revenue.

The Responses API is OpenAI's new stateful agentic API that replaces the Assistants API. It provides built-in conversation state management, tool calling with web search, code interpreter, and file search, streaming with Server-Sent Events, and native support for multi-turn agentic loops. Chat Completions remains available and is better for simple, stateless request-response patterns. For agentic workflows, the Responses API eliminates custom state management code and provides production-ready tool execution infrastructure.

The Realtime API provides low-latency bidirectional audio streaming — sub-300ms round-trip for voice-to-voice AI conversations. It supports function calling during audio sessions so the AI can trigger backend actions (book appointments, look up records) while maintaining conversation flow. It's now generally available (GA) and is used to build production voice agents, live AI tutors, and real-time customer support. We build Realtime API voice agents as part of our AI development practice.

Structured Outputs guarantee that GPT-4o returns valid JSON matching a provided schema — no JSON parsing errors, no missing fields, no unexpected types. You define the output shape in JSON Schema (or Pydantic/Zod); OpenAI's constrained decoding ensures every token is valid for the schema. This is critical for production pipelines that pass GPT-4o output to downstream code — replacing fragile prompt engineering tricks with a compile-time guarantee on output shape.

OpenAI API pricing as of 2026: GPT-4o ($2.50/1M input tokens, $10/1M output); GPT-4o-mini ($0.15/1M input, $0.60/1M output); o3 ($10/1M input, $40/1M output); o4-mini ($1.10/1M input, $4.40/1M output). Batch API halves all prices for async workloads. Prompt caching cuts costs 50% on repeated context. Fine-tuning costs $25/1M training tokens. Share your expected usage volume and we'll model out cost estimates for your specific integration.

OpenAI offers HIPAA-eligible API access and BAA (Business Associate Agreement) for healthcare enterprises. For GDPR, OpenAI provides a Data Processing Agreement (DPA) and Zero Data Retention (ZDR) option where inputs and outputs are not stored or used for model training. Zero Data Retention is available for Business and Enterprise API plans. For maximum data control, we recommend building your OpenAI integration with ZDR enabled from the start.

RAG (Retrieval-Augmented Generation) grounds GPT-4o responses in your private documents — answering questions about your data without fine-tuning. Architecture: (1) chunk documents into segments, (2) generate text-embedding-3 vectors for each chunk, (3) store in a vector database (pgvector, Pinecone, Weaviate), (4) at query time, embed the question, retrieve top-k similar chunks, (5) pass retrieved context to GPT-4o with the question. We build production RAG systems with citation tracking, chunk re-ranking, and streaming response UI.

GPT-4o for: multimodal inputs (images, audio, video), conversational AI, content generation, real-time applications, and most general-purpose AI features. o3 for: complex multi-step reasoning, coding challenges, advanced math, research synthesis requiring multiple web searches, and tasks that benefit from 'thinking more before responding.' o3 is significantly slower and more expensive than GPT-4o but materially outperforms it on complex reasoning benchmarks. o4-mini provides o3-level reasoning at lower cost for coding and math specifically.

Our cost optimization stack: prompt caching (50% savings on repeated system context), Batch API (50% savings on async workloads), model routing (route simple tasks to gpt-4o-mini, complex to gpt-4o, reasoning to o4-mini), context window management (summarize conversation history to avoid token bloat), semantic caching (cache similar queries), and per-request cost logging for usage attribution. We typically cut initial OpenAI costs 40-60% with these techniques before recommending fine-tuning or open-source alternatives.

We offer OpenAI managed support including model upgrade planning (new model versions can break prompt behavior), cost monitoring and optimization, prompt library maintenance as use cases evolve, eval harness updates, and incident response for degraded model performance. We also provide team workshops on prompt engineering, Structured Outputs, and agentic patterns for development teams building OpenAI integrations in-house.