GPT-4 Capabilities Explained: Features, Limits & 2026 Status

By CompareBestAI Editorial Team

Answer First: GPT-4 is OpenAI's fourth-generation GPT model, originally released on March 14, 2023. Its major advances included stronger instruction following, reasoning, coding, longer-context processing, and multimodal understanding of text and images. However, GPT-4 is now a legacy model: OpenAI retired it from ChatGPT on April 30, 2025, while the older gpt-4 model remains available through the API.

GPT-4 was one of the models that moved generative AI from simple conversational demos toward serious use in writing, software development, research, document analysis, customer support, education, and business workflows.

But understanding GPT-4 capabilities in 2026 requires an important distinction: GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and newer OpenAI models are not interchangeable names for the same technology.

This guide explains what the original GPT-4 actually introduced, what it could do well, where it remained unreliable, and where GPT-4 stands today.

For a broader product-level overview, see the CompareBestAI ChatGPT profile.

Key Takeaways

• OpenAI released GPT-4 on March 14, 2023 as a large multimodal model capable of accepting text and image inputs and generating text.
• GPT-4 produced major gains over GPT-3.5 on professional and academic benchmarks, including a simulated bar exam result around the top 10% of test takers, compared with GPT-3.5 around the bottom 10%.
• OpenAI reported GPT-4 was 40% more likely to produce factual responses than GPT-3.5 on its internal evaluations and 82% less likely to respond to requests for disallowed content.
• GPT-4 was retired from ChatGPT on April 30, 2025, although the legacy gpt-4 model remains available through OpenAI's API.
• GPT-4 should not be treated as a current frontier model in 2026. Newer model families offer substantially larger context windows, lower API costs and more advanced tool-use capabilities.

What Is GPT-4?

GPT-4 is a large-scale generative model developed by OpenAI and released publicly in March 2023.

OpenAI's technical report describes GPT-4 as a large multimodal model capable of processing image and text inputs and producing text outputs. Like previous GPT models, it is Transformer-based and was pretrained to predict the next token in a document, followed by post-training alignment designed to improve factuality and adherence to intended behavior.

That description is more accurate than saying GPT-4 simply “understands” information in the same way a person does.

GPT-4 generates outputs by modeling patterns and relationships learned during training and from the context provided in a prompt. Strong benchmark performance can make that behavior look highly intelligent, but it does not remove hallucinations, bias or reasoning failures.

If you're comparing modern assistants rather than studying GPT-4 historically, read how to choose an AI tool for your workflow.

What Can GPT-4 Do?

GPT-4's significance came from the breadth of tasks it could perform at a much higher level than earlier consumer-facing language models.

1. Generate and Rewrite Complex Text

GPT-4 can draft, rewrite, summarize, expand and restructure text based on instructions.

Typical use cases include reports, emails, outlines, product descriptions, documentation, scripts, summaries and conversational responses.

One important improvement over earlier models was its ability to handle more detailed instructions while maintaining the requested structure, tone and context over longer responses.

That made GPT-4 useful not only for simple generation but for iterative workflows such as:

Brief → draft → critique → revision → final human review

For content-specific comparisons, CompareBestAI also covers Jasper AI vs OpenAI GPT-4 for content creation.

2. Handle More Complex Reasoning Tasks

One of GPT-4's most visible advances was performance on professional and academic benchmarks.

OpenAI reported that GPT-4 achieved roughly the 90th percentile on a simulated Uniform Bar Exam, while GPT-3.5 scored around the 10th percentile. OpenAI also evaluated GPT-4 across academic, coding and professional benchmarks to demonstrate stronger problem-solving ability.

That does not mean GPT-4 reasons exactly like a human expert.

A better interpretation is that it became substantially more capable at generating useful solutions for multi-step problems when the necessary information and instructions were available.

For high-stakes decisions, outputs still require independent verification.

3. Write, Explain and Debug Code

Coding became one of GPT-4's most practical use cases.

The model could generate functions from natural-language instructions, explain existing code, suggest debugging approaches, convert between languages, write tests and help developers understand unfamiliar code.

GPT-4's improvement in coding helped establish AI assistants as practical development tools rather than simple text generators.

The broader trajectory continued with newer models. OpenAI reported that GPT-4.1 later reached 54.6% on SWE-bench Verified and significantly improved instruction following and coding compared with GPT-4o.

Developers comparing modern options can also review the OpenAI o3 tool profile.

4. Work With Images and Text

Multimodality was one of the original GPT-4 announcement's biggest breakthroughs.

The research model could accept both text and image inputs and generate text responses. This allowed GPT-4 to reason about screenshots, photographs, diagrams and documents alongside written prompts.

For example, image input created possibilities such as interpreting visual information, explaining charts, describing objects, extracting information from documents and reasoning over diagrams.

There is an important 2026 distinction, however.

The current OpenAI API documentation for the legacy model specifically named gpt-4 lists it as text input/output only. Therefore, the historical multimodal capabilities of the broader GPT-4 research system should not automatically be attributed to today's legacy gpt-4 API endpoint.

This distinction is valuable for both users and LLMs: GPT-4, GPT-4V, GPT-4 Turbo and GPT-4o should not be treated as one identical product.

5. Process Longer Context Than Earlier GPT Models

Longer context was another major improvement.

At launch, OpenAI documented an 8,192-token GPT-4 model and a limited-access 32,768-token GPT-4 version.

That made GPT-4 better suited to working with longer conversations, documents, code and detailed instructions than many earlier models.

Context-window technology has moved dramatically since then.

OpenAI's current GPT-4.1 API documentation lists a context window of 1,047,576 tokens, showing how quickly long-context capability progressed after the original GPT-4 generation.

For users comparing practical limits and costs across AI products, see AI tools with free plans.

6. Follow Detailed Instructions

GPT-4's value was not only generating better prose. It became substantially more capable at following multiple constraints in a single prompt.

A user could specify audience, structure, tone, length, formatting and objectives together and receive a response that followed more of those requirements than earlier GPT systems typically managed.

This made GPT-4 useful for repeatable business workflows where formatting matters.

For example, a support workflow could ask the model to:

Classify the request, summarize the customer's issue, extract structured fields, draft a response and flag cases that require human escalation.

The model still needed validation, but instruction following made structured workflows much more practical.

For workflows extending beyond simple prompts, see business AI tools that deliver more than ChatGPT prompts.

7. Power More Capable Chatbots

GPT-4 significantly improved what developers could build with conversational AI.

Its stronger instruction following, contextual response generation and language capabilities enabled systems that could summarize conversations, answer complex questions, extract user intent, generate responses and route requests.

However, the original article overstates this by implying that more natural conversation automatically creates “superior customer experiences” and “increased business value.”

Those outcomes depend on the implementation.

Production chatbot quality also requires retrieval systems, reliable knowledge sources, privacy controls, evaluation, escalation rules and monitoring.

If AI search and answer engines are more relevant to your use case, see how DeepSeek and Perplexity are changing AI search beyond ChatGPT.

GPT-4 vs GPT-3.5: What Actually Improved?

OpenAI reported both the bar-exam performance gap and improved factuality/safety during GPT-4's launch.

These improvements explain why GPT-4 became a major milestone, but they should not be interpreted as proof that every GPT-4 response is accurate.

GPT-4 Limitations

GPT-4's capabilities were substantial, but its limitations are equally important.

Hallucinations

GPT-4 can produce statements that sound confident while being incorrect.

OpenAI explicitly listed hallucinations among the model's known limitations.

Important facts, citations, calculations and high-stakes outputs should therefore be independently verified.

Outdated Knowledge

Models do not automatically know everything that happened after their training cutoff.

The current legacy gpt-4 API documentation lists a December 1, 2023 knowledge cutoff.

For current information, systems need search, retrieval, external tools or newer sources.

Bias

OpenAI also identified social biases as a known limitation.

A fluent answer should not be interpreted as neutral simply because it was generated by a machine.

Prompt Sensitivity

Changing the wording, context or constraints in a prompt can materially change the result.

For production applications, prompt evaluation and output validation are more reliable than assuming a single prompt will behave consistently across all inputs.

No Guaranteed Decision Accuracy

The old article describes GPT-4 as providing “improved decision-making.”

That wording should be removed.

GPT-4 can help analyze information and generate options, but it should not be presented as an independently reliable decision-maker, especially for legal, medical, financial, employment or safety-critical decisions.

Is GPT-4 Still Available in 2026?

Yes—but not as a current ChatGPT model.

OpenAI retired GPT-4 from ChatGPT on April 30, 2025, replacing it at that time with GPT-4o.

The legacy gpt-4 model remains documented for API use. OpenAI currently describes it as an “older high-intelligence GPT model” with an 8,192-token context window and December 2023 knowledge cutoff. Current listed API pricing is $30 per 1 million input tokens and $60 per 1 million output tokens.

The model landscape moved further after that. GPT-4o, GPT-4.1 and GPT-4.1 mini were themselves retired from ChatGPT on February 13, 2026, although OpenAI said those models continued to remain available through the API at that time.

That is why a 2026 GPT-4 article should explain the model as an influential legacy generation, not as OpenAI's newest AI.

GPT-4 vs GPT-4.1 in the API

The difference in model economics shows how quickly the platform evolved.

These values come from OpenAI's current API documentation and can change.

OpenAI's GPT-4.1 documentation also explicitly recommends starting with newer GPT models for complex tasks.

That makes the original GPT-4 important historically, but less compelling for many new API deployments.

Where GPT-4 Still Matters

GPT-4 still matters because many of today's AI workflows were popularized or made practical during the GPT-4 era.

Its legacy is visible in modern applications for:

Content and communication: drafting, editing, summarization and translation.

Software development: code generation, debugging, explanation and technical documentation.

Document analysis: extracting, comparing and restructuring supplied information.

Education: tutoring, explanation and practice-question generation.

Customer support: classification, summarization and response drafting.

Research assistance: organizing supplied material and generating questions for further investigation.

What has changed is not the basic usefulness of these workflows. The models powering them have moved forward.

For broader evaluation criteria, use CompareBestAI's guide to choosing the right AI tool and browse the Research & Data AI category.

GPT-4 Pros and Cons in 2026

The important distinction is that GPT-4 was groundbreaking in 2023 without necessarily being the best model to choose for a new workflow in 2026.

Should You Still Use GPT-4 in 2026?

For most new projects, GPT-4 should be treated as a compatibility or legacy option rather than an automatic first choice.

There may still be reasons to retain it in an existing application where behavior has been thoroughly evaluated and changing models would require expensive validation.

For a new application, however, developers should compare newer models on:

task quality, latency, context size, tool support, reliability and total API cost.

If you are unsure which model or platform fits your task, compare alternatives using CompareBestAI's AI tool selection guide.

Frequently Asked Questions About GPT-4

What are GPT-4's main capabilities?

GPT-4 can generate and rewrite text, summarize supplied information, assist with coding, follow complex instructions and perform many language-based analytical tasks. The original GPT-4 research system also supported image inputs, making it a multimodal model.

When was GPT-4 released?

OpenAI released GPT-4 on March 14, 2023. It was introduced as a large multimodal model capable of taking text and image inputs and generating text.

Is GPT-4 still available in ChatGPT?

No. OpenAI retired GPT-4 from ChatGPT on April 30, 2025. The legacy GPT-4 model remains documented for API access.

Is GPT-4 better than GPT-3.5?

GPT-4 demonstrated substantially stronger performance than GPT-3.5 on many professional and academic evaluations. One well-known example is the simulated bar exam, where GPT-4 scored around the top 10% while GPT-3.5 scored around the bottom 10%.

Is GPT-4 multimodal?

The original GPT-4 research model was multimodal, accepting text and image inputs and generating text outputs. However, OpenAI's current legacy gpt-4 API listing describes text-only input and output, so capabilities must be matched to the specific GPT-4 variant.

What are GPT-4's biggest limitations?

GPT-4 can hallucinate, reproduce biases, provide outdated information and produce different results depending on prompt wording. OpenAI explicitly documented hallucinations, social biases and adversarial prompts among GPT-4's known limitations.

Final Verdict

GPT-4 was a defining milestone in generative AI because it combined stronger language generation, instruction following, coding, benchmark performance, longer context and multimodal research capabilities in one model family.

But the correct 2026 perspective is historical rather than promotional.

GPT-4 is no longer the newest model, it is no longer available as a selectable ChatGPT model, and the surviving legacy API model is expensive and limited compared with newer alternatives. Its importance is that many of the capabilities users now expect from modern AI assistants became dramatically more practical during the GPT-4 generation.

To compare today's options rather than legacy models, start with the ChatGPT tool profile or explore AI tools that go beyond basic ChatGPT prompting.