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OpenAI: guides & tutorials

OpenAI APIs turn scraped pages into JSON, embeddings, and tool calls. Pair GPT with Apify actors for durable browser and HTTP extraction pipelines.

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OpenAI's APIs turn scraped pages into JSON, embeddings, and tool calls, making GPT a common parsing and enrichment layer in data pipelines. These guides cover using OpenAI models alongside scrapers for durable extraction.

Pairing GPT with browser and HTTP scraping gives you both the raw pages and the structure on top. Apify actors handle collection while OpenAI handles understanding. Below you will find prompting and integration patterns for extraction at scale.

Related topics

AI agents8 min read

GPT-5.4 Native Computer Use: What It Is, How It Benchmarks, and How to Use It (2026)

· 8 min read
Yassine El Haddad
Yassine El Haddad
Software Developer & Automation Specialist

GPT-5.4 native computer use (OpenAI, 2026) means the model drives GUIs the way a user would: it reads screen state (e.g. screenshots), issues clicks and keyboard input, and loops until a task completes—paired with a very large API context for long traces. It is not a drop-in replacement for structured web extraction at scale; pair it with scrapers when pages are stable data sources.

Vendor-reported highlights​

OpenAI reports 75% on the OSWorld-Verified benchmark versus a published 72.4% human baseline on that suite, and positions GPT-5.4 as one stack for reasoning, coding, and agentic flows across ChatGPT, the API, and Codex.

At a glance​

  • What it is (vendor framing): GUI-grounded control: perceive screen state, propose actions, loop until done—not a universal substitute for structured data pipelines.
  • Headline benchmark (vendor-reported): 75% OSWorld-Verified vs. 72.4% human baseline on the same benchmark; your internal apps still need their own validation.
  • API scale (vendor-reported): Up to 1M tokens in the API (922K input / 128K output, per OpenAI)—useful for traces, not a reason to skip log compression.
  • Where it usually loses to scraping: High-volume, repeatable HTML/JSON extraction; pair computer use with Apify Actors when the page is a stable data source.

Below: what “native computer use” implies operationally, how benchmarks map to production risk, a straight comparison to scraping, a loop teams actually ship, builder checklists, and compliance guardrails. For the week’s news context, see Top 10 AI and tech stories this week (March 17–24, 2026).

Anthropic7 min read

OpenAI vs Anthropic vs Groq: Choosing the Right LLM API for Your Project in 2026

· 7 min read
Yassine El Haddad
Yassine El Haddad
Software Developer & Automation Specialist

TL;DR: Use Claude for long documents and tool use, GPT-4o for general coding and the best ecosystem, Groq for cost-sensitive real-time apps with open models. All three support OpenAI-compatible clients; migration is straightforward.

Choosing an LLM API in 2026 means balancing quality, context length, speed, and cost. This guide compares OpenAI, Anthropic, and Groq so you can pick the right provider for web scraping pipelines, content summarization, coding agents, and production AI.

AI engineering4 min read

Make.com + OpenAI Integration Guide: Setup, Patterns, and Cost Controls

· 4 min read
Yassine El Haddad
Yassine El Haddad
Software Developer & Automation Specialist

The fastest way to waste time with AI automation is connecting OpenAI to Make without architecture rules. The fastest way to create leverage is the opposite: strict inputs, validated outputs, and clear retry logic.

This guide covers the exact setup flow, then moves into production patterns you can ship safely.

Firecrawl3 min read

Firecrawl + OpenAI: Build an AI Research Assistant 2026

· 3 min read
Yassine El Haddad
Yassine El Haddad
Software Developer & Automation Specialist

Combine Firecrawl (web scrape) and OpenAI (GPT-4) to build a research assistant: scrape URLs, extract markdown, send to the model with a prompt, and return structured findings. Pipeline: user query → Firecrawl scrape → chunk if needed → GPT-4 with system + user messages → structured response.

Get Firecrawl →

Guides on this site

Frequently asked questions

Frequently Asked Questions

Send cleaned page content to the OpenAI API with a structured output schema (using the response_format parameter with JSON schema). GPT-4o returns reliably formatted JSON you can validate and insert into a database. For bulk extraction, Apify handles fetching and rendering; OpenAI handles parsing. This is faster to set up than maintaining CSS selectors and more tolerant of page layout changes.

GPT-4o-mini is the cost-effective choice for high-volume extraction — accurate on most structured content at a fraction of GPT-4o's price. Use GPT-4o for complex documents, long pages, or tasks requiring more reasoning. For embeddings used in RAG pipelines, text-embedding-3-small provides strong retrieval quality at low cost. Test accuracy on a 50–100 URL sample before choosing a model for production.

GPT-4o-mini costs $0.15 per million input tokens — a typical webpage runs 2,000–8,000 tokens, putting per-page cost below $0.002. At 100,000 pages/month, LLM costs are roughly $100–200. GPT-4o is ~10x more expensive. Use prompt caching to reduce costs on repeated system prompts, and batch the Batch API for non-time-sensitive jobs at 50% discount.

GPT-4o supports web browsing via the built-in tool in ChatGPT, but the API does not include direct browsing by default. For API-based pipelines, pair OpenAI with Apify: Apify fetches and renders pages, OpenAI processes the content. For agentic workflows, use the OpenAI Agents SDK with a web scraping tool that calls Apify actors, giving the model access to fresh web data within tool-use limits.