AI Models SoftwareApplication

GPT-4

Also known as: GPT 4, GPT4, Generative Pre-trained Transformer 4, GPT-4 Turbo, GPT-4o

OpenAI's multimodal frontier language model, capable of processing and generating text and images with top-tier performance on academic benchmarks and real-world tasks.

Updated: 2026-01-04

Definition

GPT-4 is a large-scale multimodal language model developed by OpenAI, released in March 2023. It is one of the contemporary frontier models (alongside Claude 3.5, Gemini 1.5) with top-tier performance on standardized academic benchmarks and impressive generalist capabilities on arbitrary tasks.

The designation “GPT-4” marks the evolutionary success from the GPT-2 (2019) and GPT-3 (2020) series, with substantial improvements in accuracy, reliability, hallucination reduction, and multimodal abilities.

Technical Characteristics

Size and architecture: OpenAI has not released official details. Community estimates: ~1.7 trillion parameters (unconfirmed). Architecture: Transformer decoder-only with likely Mixture of Experts (MoE) for efficiency (inference through expert routing estimates).

Modalities:

  • Text-in / Text-out: text generation from textual prompts
  • Vision: image processing (added in GPT-4o, full multimodal version)
  • Context window: 8K tokens (original), 128K tokens (Turbo, 2024)

Training:

  • Pre-training on web-scale data up to April 2024
  • Post-training with RLHF and Constitutional AI for alignment with human preferences
  • Custom fine-tuning available via API

Versions and Variants

GPT-4 (original): March 2023, 8K context, baseline performance.

GPT-4 Turbo: November 2023, 128K context, ~3x faster inference, reduced costs (~3x), knowledge cutoff April 2024.

GPT-4o: May 2024, natively multimodal (text + images), ~2x faster inference than Turbo, ~5x lower input costs.

GPT-4o mini: November 2024, smaller and more economical model in the line, performance comparable to GPT-3.5 Turbo with ~10x lower costs.

Performance and Benchmarks

Standardized academic benchmarks:

  • MMLU (general knowledge): 92.3% (GPT-4)
  • HumanEval (coding): 92% (GPT-4), among the highest
  • GPQA (scientific reasoning): 88% (GPT-4)

Comparison: GPT-3.5 achieves ~70% on these benchmarks. The difference is not marginal but significant on complex tasks.

Proprietary benchmarks: OpenAI does not publish details on privacy tests, reliability, bias reduction. External evaluation (LMSYS Chatbot Arena) shows GPT-4o persistently in top-3.

Use Cases

Content creation: writing, articles, high-quality textual creativity.

Code assistance: code generation, debugging, test generation. Performance on coding is among the best.

Analysis: document summarization, information extraction, Q&A on long texts.

Complex reasoning: multi-step problem solving, explaining abstract concepts, brainstorming.

Conversational assistance: chatbots, customer support, educational tutoring.

Data augmentation: generation of synthetic data for training and evaluation.

Practical Considerations

Costs: GPT-4o input $5/MTok, output $15/MTok (May 2026). GPT-4o mini input $0.15/MTok, output $0.60/MTok. 30-100x difference vs. local open-source models depending on volume and latency requirements.

Latency: TTFT (time-to-first-token) ~100-500ms on ChatGPT, generated tokens at ~50-100 tokens/sec. For real-time critical applications, latency can be limiting.

Rate limits: OpenAI API has limits on requests/minute and tokens/minute. At scale, rate limit becomes an architectural constraint before TCO.

Reliability and moderation: OpenAI applies content filtering on input and output (illegal content, adult, etc.). Can degrade performance on legitimate tasks requiring discussion of sensitive topics.

Open-source alternative: Llama 3, Mistral, Qwen enable on-premise deployment, without logging, with full customization. Trade-off: 10-30% lower performance, more complex operational setup.

Common Misconceptions

”GPT-4 truly understands what it says”

No. GPT-4 predicts tokens probabilistically based on statistical patterns. It has no world model, beliefs, or cognitive understanding. It produces statistically plausible output, not necessarily truthful.

”GPT-4 is the right solution for every task”

Depends. On coding, explanation, generic Q&A, it is excellent. On specialized domains (medical law, finance), fine-tuned models often exceed GPT-4 in reliability. On tasks requiring real-time info, it lacks knowledge cutoff.

”GPT-4 completely eliminates hallucination”

No. It significantly reduces it compared to GPT-3.5 (~30% less hallucination on benchmarks), but the phenomenon persists. External validation remains necessary for critical applications.

  • LLM: category of which GPT-4 is an example
  • OpenAI: organization developing GPT-4
  • Transformer: underlying architecture
  • RLHF: alignment technique used for GPT-4 training
  • Prompt Engineering: art of optimizing input to maximize GPT-4 capabilities

Sources

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