GPT-5 Arrives: A Deep Technical Analysis of What Has Actually Changed

The release of GPT-5 has generated the predictable wave of breathless coverage and equally predictable backlash. After 200 hours of systematic testing across a diverse range of tasks, we can offer a more measured assessment of what has genuinely improved, what remains limited, and what the implications are for developers, businesses, and everyday users.

Reasoning and Multi-Step Problem Solving

The most significant improvement in GPT-5 is in multi-step reasoning tasks, particularly those requiring the model to maintain context and logical consistency across extended chains of thought. On the MATH benchmark, GPT-5 achieves 94.2% accuracy compared to GPT-4o's 76.6% — a substantial leap that reflects architectural improvements in how the model plans and executes complex reasoning sequences.

In our own testing, we found that GPT-5 handles problems that require holding multiple constraints simultaneously with markedly greater reliability. Legal analysis tasks that required cross-referencing multiple clauses and identifying contradictions, which GPT-4 would frequently fumble, were handled with impressive precision.

Code Generation and Debugging

Software development applications show perhaps the most practically significant improvements. GPT-5 achieves 72.4% on the SWE-bench benchmark, which tests the model's ability to resolve real GitHub issues from popular open-source repositories. This compares to approximately 49% for GPT-4o and represents a meaningful step toward AI systems that can autonomously handle non-trivial software engineering tasks.

In our testing, GPT-5 demonstrated a notably improved ability to understand large codebases when provided with relevant context, identify subtle bugs that require understanding of program semantics rather than just syntax, and generate code that integrates cleanly with existing architectural patterns.

Multimodal Capabilities

The vision capabilities have been substantially upgraded. GPT-5 can now process and reason about video clips up to 10 minutes in length, a capability that opens significant new application domains in content analysis, medical imaging review, and educational applications. Image understanding has also improved, with the model demonstrating better spatial reasoning and the ability to extract quantitative information from charts and graphs with greater accuracy.

Limitations That Persist

Despite the improvements, several fundamental limitations remain. The model still hallucinates factual information, particularly for obscure topics or recent events beyond its training cutoff. The frequency of hallucinations has decreased, but the model's calibration — its ability to accurately represent its own uncertainty — remains imperfect.

Long-context performance, while improved, still degrades for very long documents. Tasks requiring the model to synthesize information from across a 100,000-token context window show measurable performance degradation compared to shorter contexts.

Practical Implications

For developers building AI-powered applications, GPT-5 represents a meaningful capability upgrade that will enable new use cases, particularly in domains requiring complex reasoning and code generation. The improved reliability reduces the need for elaborate prompt engineering and output validation in many applications.

For businesses evaluating AI adoption, the improved capabilities strengthen the case for deployment in more complex, high-stakes workflows, though human oversight remains essential for consequential decisions.