When should I use GPT-4o versus the o3/o4-mini reasoning models?

GPT-4o is the right default for most applications — fast, multimodal and cost-effective for generation, extraction and conversation tasks. The o3 and o4-mini reasoning models are designed for tasks requiring structured multi-step reasoning: complex mathematical problems, code debugging with multiple hypotheses, or decision-making requiring explicit chain-of-thought. Reasoning models are slower and more expensive — use them only where the reasoning quality delta justifies the cost.

Is it safe to send business data to OpenAI?

OpenAI's API does not use data submitted via the API to train models by default. For EU data privacy and GDPR, OpenAI offers a Data Processing Addendum. For stricter on-premise requirements, Azure OpenAI Service offers the same models hosted within your Azure region. We recommend reviewing your data classification before choosing between OpenAI API, Azure OpenAI or a self-hosted open-source model.

How should I estimate the cost of an OpenAI-powered application?

Cost depends on token volume (input + output tokens per request × number of requests). Start by estimating average prompt length, average response length and expected requests per day. GPT-4o is priced per million tokens — at the time of writing, input tokens are significantly cheaper than output tokens. We recommend building with logging from day one so you can measure actual token consumption before committing to a pricing model.

How does OpenAI compare to Anthropic Claude?

Both are frontier AI providers. OpenAI's ecosystem (integrations, SDKs, tooling) is larger and more mature. Anthropic Claude has a larger context window (200k tokens vs 128k for GPT-4o), stronger instruction-following in long-document tasks and better performance on Arabic and French text in our experience. For most applications, both are viable — the choice often comes down to specific task performance, pricing at your token volume and data privacy requirements. See our OpenAI vs Anthropic comparison.

AI/ML

OpenAI

GPT-4o and the Assistants API for production AI applications.

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OpenAI provides the GPT-4o family of large language models, the o3/o4-mini reasoning model series, DALL-E 3 for image generation, Whisper for speech-to-text and the Assistants API for stateful AI agent development. The platform has the largest developer ecosystem and most mature production tooling of any AI provider.

Why we chose it

Why Ascii-Core uses OpenAI

1
GPT-4o delivers frontier reasoning, code generation and multimodal understanding in a single model
2
Assistants API provides stateful threads, tool calling and file retrieval — reducing agent infrastructure code
3
Largest ecosystem of integrations, SDKs and community resources of any AI provider
4
Function calling and structured output enable reliable JSON extraction from unstructured content

Ideal use cases

When to use OpenAI

AI agent development requiring tool use, memory and multi-step reasoning
Document processing and extraction with GPT-4o vision
Code generation and review automation
Conversational AI with context management via Threads
Speech-to-text pipelines with Whisper

Trade-offs

Honest limitations

We believe in accurate trade-offs. These are the cases where a different tool may be the better choice.

Data sent to OpenAI servers — not suitable for use cases with strict on-premise data requirements without Azure OpenAI
Cost scales with token volume — high-throughput applications require careful prompt optimisation
Context window (128k for GPT-4o) limits single-context document analysis for very large corpora

Services