OpenAI
GPT-4o, o3, and the world's most widely-used AI API
OpenAI is the creator of the GPT model family and the ChatGPT product. Their API provides access to frontier models including GPT-4o, the o-series reasoning models, and the GPT-4.1 long-context family. Pricing is competitive for frontier-tier capability, with prompt caching available on most models.
The most widely-integrated LLM API — virtually every AI framework and tool supports OpenAI natively.
- Largest ecosystem & third-party integrations
- Best-in-class function calling & structured outputs
- Prompt caching on all major models
- o3/o4-mini reasoning models for complex tasks
- 1M+ token context on GPT-4.1
- No open-weight models — full vendor lock-in
- Output pricing is among the highest for frontier tier
- Rate limits can be restrictive on lower tiers
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Full comparison tableOpenAI LLM pricing overview
OpenAI is a proprietary AI lab that develops and hosts its own large language models. OpenAI is the creator of the GPT model family and the ChatGPT product. Their API provides access to frontier models including GPT-4o, the o-series reasoning models, and the GPT-4.1 long-context family. Pricing is competitive for frontier-tier capability, with prompt caching available on most models. Common use cases include Coding, Chat, Vision, Reasoning. Headquartered in San Francisco, CA, founded 2015. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
OpenAI vs other LLM providers
OpenAI competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. The most widely-integrated LLM API — virtually every AI framework and tool supports OpenAI natively. Use the LLM provider comparison tool to see OpenAI token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose OpenAI?
OpenAI's key strengths are: Largest ecosystem & third-party integrations; Best-in-class function calling & structured outputs; Prompt caching on all major models. Limitations to consider: No open-weight models — full vendor lock-in; Output pricing is among the highest for frontier tier. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding OpenAI token pricing
OpenAI charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from OpenAI's public pricing page and updated daily. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
OpenAI context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when OpenAI makes sense
Managed inference APIs like OpenAI eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at OpenAI API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical OpenAI token price data is available in the LLM price history charts.