GPT-4 / GPT-5

GPT-4 (Generative Pre-trained Transformer 4) is OpenAI's fourth-generation large language model, released March 2023. GPT-5 is its successor, launched in 2025 with native multimodal capability, sharply improved reasoning, and a "thinking" mode that allocates more compute to hard problems. Together they have defined the consumer frontier of generative AI, powering ChatGPT (600M+ weekly active users in 2026), Microsoft Copilot, and the API behind much of the AI app ecosystem.

OpenAI does not disclose parameter counts, but credible estimates put GPT-4 at ~1.8 trillion parameters (mixture-of-experts) and GPT-5 meaningfully larger with longer effective context. Training cost reportedly exceeded $100M for GPT-4 and several hundred million for GPT-5.

What GPT-4 and GPT-5 do well

GPT-4 introduced reliable multi-step reasoning at scale — the first LLM most users found genuinely useful for non-trivial work (coding, analysis, drafting). GPT-5 extended that with:

• Native multimodality — vision, audio, and (limited) video input/output without separate models.
• "Thinking" mode — when invoked, the model spends extra time reasoning before answering, dramatically improving accuracy on math, logic, and code.
• Tool use — built-in web search, code execution, image generation (DALL-E 3), and file analysis.
• Long context — 256k–1M tokens depending on tier, enabling whole-codebase or whole-book analysis.
• Lower hallucination rate — OpenAI reports ~50% fewer factual errors than GPT-4 on internal evaluations.

A 2026 Stanford HAI evaluation found GPT-5 matches or exceeds expert human performance on 47% of benchmarked professional tasks, including legal contract review, medical triage, and competitive programming.

How GPT-4/GPT-5 compare to alternatives

In 2026, the frontier is a three-horse race: OpenAI (GPT-5), Anthropic (Claude Opus 4.7), and Google (Gemini 2.5 Pro). Strengths overlap but each has distinguishing characteristics:

• GPT-5 — Strongest at creative writing, image generation integration, voice mode. Highest brand recognition.
• Claude Opus 4.7 — Strongest at long-document analysis, code generation, and tool use. 1M-token context. Favored by developers.
• Gemini 2.5 Pro — Strongest multimodal grounding, deep Google Workspace integration, massive context window.

For PostKit's use case (structured JSON output following a tight schema), all three are viable; the choice usually comes down to cost, latency, and ecosystem fit.

Examples of GPT-4/GPT-5 in production

1. ChatGPT — Consumer chatbot; 600M+ weekly users.
2. Microsoft 365 Copilot — GPT-5 embedded across Word, Excel, PowerPoint, Outlook, Teams.
3. Khan Academy Khanmigo — GPT-4-based tutor used by 1M+ students.
4. Stripe Sigma assistant — GPT-4 for natural-language SQL on payment data.
5. Duolingo Max — GPT-4 for personalized language learning conversations.

How PostKit relates to GPT-4/GPT-5

PostKit currently uses Gemini Flash 3 for LLM tasks rather than GPT-5. The reasons are practical: Gemini Flash 3 is meaningfully cheaper per token, faster for short-form structured output, and pairs cleanly with Imagen 3 (also Google) for the image-generation half of the pipeline.

That said, PostKit's prompt architecture is model-agnostic — the same prompts run on GPT-5, Claude, or Gemini with minor adapter changes. This is deliberate: the AI provider landscape shifts every 6–12 months, and locking into one vendor's dialect is a long-term liability. Vertical AI products that survive switch models as the frontier shifts.

If you've used ChatGPT to draft a social caption, you've experienced what GPT-4/GPT-5 can do unstructured. PostKit is what happens when that capability is wrapped in platform rules, brand voice, marketing pipelines, and image generation — turning a chat into a content factory.

Frequently asked questions

Is GPT-5 a single model or a family? A family. OpenAI ships GPT-5, GPT-5 mini, GPT-5 nano, and a "GPT-5 thinking" variant — different size/cost/latency points. ChatGPT Plus routes to the appropriate variant depending on query complexity.

What does the "G" "P" "T" stand for? Generative Pre-trained Transformer. Generative = produces output. Pre-trained = trained on large unlabeled text before fine-tuning. Transformer = the underlying neural network architecture.

Is GPT-5 AGI? No. GPT-5 is enormously capable on language tasks but lacks persistent memory, embodiment, and reliable real-world grounding. OpenAI's own definition of AGI requires economic outperformance of humans across most jobs — not yet achieved.

How much does GPT-5 cost via API? Roughly $1.25–$10 per million input tokens and $10–$40 per million output tokens depending on tier (2026 pricing). Mini and nano variants are 5–20x cheaper.

Does GPT-5 have memory across conversations? ChatGPT has opt-in memory across sessions. The API is stateless — each request is independent unless you pass prior history.

Can GPT-5 see images? Yes. Native multimodal. Upload screenshots, photos, charts, or PDFs and GPT-5 reads them.

Why does GPT-5 sometimes refuse requests? Safety filters block content classified as harmful, illegal, or against OpenAI policy. Refusals can be over-broad; Anthropic and OpenAI have both reduced over-refusal rates significantly in late-2025 model updates.

Related terms

• LLM (Large Language Model)
• Generative AI
• Claude (Anthropic)
• Gemini (Google)
• Multimodal AI
• Prompt engineering
• Hallucination (AI)
• AI agent

Sources

• OpenAI — GPT-4 Technical Report (2023)
• OpenAI — GPT-5 System Card (2025)
• Stanford HAI — AI Index Report 2026