What are GPT-5's headline improvements over GPT-4 in reasoning and multimodality

Deep Reasoning and Complex Task Handling

GPT-5’s most substantial leap is its deep reasoning ability. The introduction of “thinking mode” allows the model to engage in more prolonged and deliberate problem-solving, resulting in accuracy gains across benchmarks that demand genuine critical thinking. For instance, on the GPQA benchmark—a rigorous measure of graduate-level problem-solving—GPT-5 sets a new standard, beating GPT-4’s top scores by a wide margin. Its score of 88.4% without external tools is a notable milestone for general-purpose AI.

In practical terms, GPT-5 handles complex, multistep tasks with a reliability not previously seen. It can coordinate steps, adapt to evolving prompts, and maintain context across much longer, more intricate conversations and instructions. This is not just a matter of answering harder math or logic questions; GPT-5 shows more robust agentic tool use, reliably completing complicated tasks by automatically exploiting the right AI modalities and resources when required.

Multimodality: Beyond Text

While GPT-4 introduced visual capabilities, GPT-5 pushes multimodality into new territory. The model is trained to understand and reason about a dramatically broader array of input types—spanning charts, images, audio, spatial data, and even video content. Its performance on benchmarks such as MMMU (multimodal understanding), where it achieved an 84.2% score, underscores its advanced capacity to synthesize information from mixed media sources.

GPT-5 is capable of interpreting and summarizing complex diagrams and charts, extracting information from screenshots and presentations, and providing highly accurate responses to queries involving multiple data forms. In addition, it handles cross-modal reasoning—combining, say, a text prompt with a photo or a code block with a diagram—to solve tasks that previously confounded GPT-4-based systems. Audio input processing has also seen remarkable improvement, enabling highly accurate transcription, comprehension, and reasoning over spoken language.

Efficiency and Scale

Efficiency is another headline benefit of GPT-5. Thanks to architectural changes and new hardware optimizations, GPT-5 delivers results much faster and typically at half the cost in output tokens compared to GPT-4. Despite the increase in reasoning capability, it requires fewer computational resources per unit of genuinely useful work. This means lower cost, reduced latency, and greater scalability for large-scale deployments—solving a fundamental bottleneck that limited GPT-4 in enterprise contexts.

Reliability, Factuality, and Honesty

A persistent issue with large language models has been their propensity to “hallucinate”—that is, to invent facts or give confident but false answers. GPT-5 has made radical advances in this area. Its factual error rate is 45% lower than GPT-4o’s, and when engaged in deep reasoning mode, the model shows 80% fewer hallucinations than even highly advanced prior models. The model is also much better at recognizing its own limits: when a task is underspecified or there is not enough information to give a truthful answer, GPT-5 will more often state those limits explicitly rather than guessing or faking a solution.

Moreover, GPT-5 is notably less “deceptive.” In real-world scenarios, it is less likely to give overconfident answers on missing or impossible prompts and more likely to communicate honestly about what it can and cannot do. For instance, on tests involving impossible coding challenges or prompts with missing multimodal assets, the rate of “deceptive” responses fell to about 2.1%, compared to 4.8% for the previous generation.

Expanded Context Length and Memory

GPT-5 boasts a context window twice as large as GPT-4, enabling it to follow and integrate far more information across longer conversations or more complex documents. This supports workflows in law, healthcare, and technical fields where massive records or long case histories need to be accurately remembered and referenced, bolstering utility and reducing fragmentation of context.

Personalization, Flexibility, and Tone Control

Another marked improvement is GPT-5’s on-the-fly ability to adapt tone, style, and persona. While previous models allowed for basic "instruction following," GPT-5 can switch between preset personalities such as Cynic, Robot, Listener, or Nerd and can fluidly shift style and register according to prompt context—all without the need for elaborate prompt engineering. This makes the model more usable across customer-facing scenarios, education, and creative industries, where tone and voice consistency matter.

Upgraded Model Architecture

On a technical level, GPT-5 moves past the pure Transformer model used in GPT-4, incorporating elements such as graph neural networks (GNNs) to vastly improve its ability to model relationships and context within data. This not only leads to deeper language understanding but also enhances the model’s handling of complex, multi-entity relationships and subtleties like sarcasm, irony, and emotion.

GPT-5 also shifts toward unsupervised learning with reduced reliance on hand-labeled data, drawing from much richer and more diverse training datasets, including broad multilingual corpora. As a result, it demonstrates sharper multilingual capabilities, more balanced outputs, and broader cultural fluency.

Practical Impacts Across Industries

The core improvements in GPT-5 have significant impacts in various domains:

- Healthcare: Improved reasoning and factuality mean GPT-5 can reliably assist in diagnostic support, literature synthesis, and cross-modal medical data interpretation.
- Legal Analysis: Deeper document comprehension and context retention enable effective contract review and strategic research, enhancing efficiency for legal teams.
- Coding and Software Engineering: With higher accuracy on official coding benchmarks and better handling of complex codebases, GPT-5 functions as an even more reliable assistant for developers, automating larger segments of the software lifecycle.
- Creative Professions: Enhanced multimodal abilities support richer creative applications, from interpreting and generating visual art to assisting with mixed-media storytelling and design.

Narrative Capacity and Human-Like Expressiveness

GPT-5 demonstrates more “human” narrative capabilities, excelling at coherent and expressive communication. Its responses are less formulaic and more literary, with a greater capacity to handle ambiguity, subtle metaphor, unrhymed verse, and nuanced tone shifts. This makes the model feel less like an automated system and more like a creative partner.

Safety, Bias, and Customization

GPT-5 substantially reduces sycophantic (over-agreeable) responses and features improved safeguards for safe completions, benefiting moderation, compliance, and customer support cases where explicit reliability and reduced bias are necessary. Enhanced training diversity and bias mitigation further extend the model's effectiveness across cultures and topics.

Streamlined Architecture and Model Management

With GPT-5, the model lineup has been streamlined. Rather than juggling multiple versions for different use cases (as with GPT-4, GPT-4o, and related variants), GPT-5 acts as an “intelligent router,” automatically selecting the best sub-model or processing mode for each request. This eliminates user confusion and unnecessary context switching, providing a consistent experience regardless of task complexity or modality.

Benchmarks and Quantitative Evidence

Quantitatively, GPT-5 leads across academic and real-world benchmarks:

- 94.6% on AIME 2025 Math (without tools)
- 74.9% on SWE-bench Verified coding tasks
- 88% on Aider Polyglot coding
- 84.2% on MMMU multimodal understanding
- 46.2% on HealthBench Hard (medical reasoning)
- ~45% fewer factual errors, and up to ~80% fewer errors in reasoning mode than prior models

These gains are not just theoretical: users report smarter, faster, and more natural feeling interactions across domains, making GPT-5 a clear step forward in productivity and reliability.

Conclusion