GPT-5 Features: What You Can Do

Key insights

• Hybrid multi-model architecture: GPT-5 runs as a group of specialized models rather than one single model, and a real-time router picks the best sub-model for each prompt to balance speed and deep reasoning.
  It lets the system use less compute for simple tasks and more power when problems need deeper thought.
• Dynamic model selection: The platform offers versions like gpt-5-mini, gpt-5-nano, and a dedicated gpt-5-thinking model so the system can match cost and reasoning level to the job.
  Developers can choose smaller models for cheap, fast outputs and larger ones for complex reasoning.
• Large context window and multimodal input: GPT-5 can handle very long conversations (over 256,000 tokens) and natively processes text, images, voice, and live video in one session.
  This allows users to keep long documents or mixed media in a single interaction without losing context.
• Reduced hallucinations and stronger reasoning: The model shows a meaningful drop in incorrect or made-up answers and supports a "thinking" mode for deeper problem solving.
  It also stores user preferences and past context better, improving follow-up relevance.
• Coding and API access: GPT-5 improves coding help, especially for front-end work and large repositories, and exposes multiple model sizes via the API to balance cost and performance.
  Subscription tiers control access levels, with higher tiers offering more compute for intensive tasks.
• Microsoft Azure integration: Training and deployment used Microsoft Azure supercomputers, and the model integrates with Microsoft products like Copilot and GitHub for tighter developer workflows.
  This makes enterprise and product integration smoother for organizations already on the Microsoft stack.

Anders Jensen [MVP] presents a concise walkthrough of the latest ChatGPT release in a YouTube video that aims to demystify what GPT-5 can do for everyday users and developers alike. In the video, Jensen covers the redesigned interface and walks viewers through practical features like the Prompt Optimizer, Agent Mode, and Study Mode, as well as how to access the model via API. Consequently, this report summarizes those highlights and evaluates the tradeoffs that organizations and individuals will face when adopting the new system. Moreover, it situates the functionality in the wider context of performance, cost, and safety concerns raised by the shift to a multi-model design.

Overview of the Video

Jensen structures the video as a step-by-step course designed for non-developers and developers alike, so viewers can move from casual chat to advanced integrations. He demonstrates the new interface and explains how the product’s defaults map to common tasks, which helps reduce the learning curve for new users. In addition, Jensen emphasizes practical workflows rather than academic detail, showing how features behave in real time and how they respond to varied prompts. As a result, the video functions as both an introduction and a hands-on tour that listeners can follow along with.

Key Technical Innovations

The video highlights a shift to a hybrid multi-model architecture that dynamically routes requests to specialized sub-models, which Jensen suggests improves both speed and depth of reasoning. He also draws attention to a vastly increased context window, enabling the model to process much larger documents in one session and thereby reducing the need for repeated context loading. Furthermore, Jensen demonstrates the model’s multimodal handling of text, images, and audio, noting that unified processing streamlines workflows that previously required separate tools. However, he also notes that the routing logic and multimodal fusion add system complexity that can complicate debugging and observability.

User Features and Practical Experience

According to Jensen, the Prompt Optimizer helps users refine their prompts by suggesting clearer phrasing and structure, which tends to produce more reliable outputs for routine tasks. In addition, the video showcases Agent Mode for orchestrating multi-step tasks and Study Mode for structured learning, where the model can act as a tutor with adaptive prompts. Jensen points out that selectable chatbot personalities allow users to pick interaction styles that match their needs, which improves usability but may also affect consistency of factual answers. Consequently, these layers of personalization introduce tradeoffs between conversational tone and strictness of information delivery.

Tradeoffs: Cost, Performance, and Reliability

Jensen explains that the multi-model approach offers a range of model sizes for different tasks, allowing teams to trade accuracy for latency and cost when needed. For example, smaller models can handle routine queries at lower cost, while the specialized reasoning variant activates for complex problems, which preserves compute resources but raises orchestration overhead. Moreover, Jensen discusses improved hallucination rates and stronger coding abilities, especially for front-end work, but he stresses that no model is immune to error and that reliance on automatic routing can obscure failure modes. Thus, organizations must balance cost savings against the need for transparency, logging, and human review in sensitive applications.

Challenges for Adoption and Governance

Jensen acknowledges that the new capabilities bring practical challenges in safety, privacy, and integration, especially when systems must process large, sensitive datasets in a single context window. He further notes that while memory and long-form recall improve continuity, they require careful governance to prevent unintended data retention and to respect user consent. In addition, the video touches on the engineering burden of integrating multimodal inputs and real-time routing into existing products, which can demand specialized monitoring and debugging tools. Therefore, teams should plan for both technical and policy workstreams when deploying these features.

Access, Pricing, and Real-World Use

Finally, Jensen explains that access is available through standard chat tiers and via an API that exposes multiple model sizes, enabling developers to choose a balance of cost and capability. He clarifies that subscription levels influence available compute and feature access, so organizations must weigh predictable usage patterns against potential burst needs for heavy reasoning tasks. In addition, the video recommends staged rollouts and pilot programs to measure performance and cost in real scenarios before committing to broad deployments. Overall, Jensen’s tour is practical: it explains strengths and limitations while encouraging careful, measured adoption.

Keywords

GPT-5 features, GPT-5 capabilities, new in GPT-5, GPT-5 use cases, GPT-5 vs GPT-4, how to use GPT-5, GPT-5 release date, GPT-5 improvements