GPT-5.4 introduces substantial performance enhancements across several key areas, primarily focusing on accuracy, efficiency, and expanded capabilities for complex professional workflows. A significant improvement lies in its heightened accuracy, with OpenAI reporting that GPT-5.4 is 33% less likely to produce false claims and 18% less likely to contain errors in its full responses compared to GPT-5.2. This reduction in hallucinations makes the model more reliable for tasks requiring factual correctness, such as research, technical documentation, and financial analysis. Furthermore, GPT-5.4 demonstrates superior performance in knowledge-intensive tasks, achieving an 83.0% score on the GDPval benchmark, which assesses an AI's ability to produce well-specified knowledge work across 44 occupations. This marks a notable increase from GPT-5.2's 70.9%. Specific examples include an 18.9 percentage point improvement in investment banking modeling tasks, reaching 87.3% accuracy compared to GPT-5.2's 68.4%. These advancements position GPT-5.4 as a more dependable tool for professionals seeking high-quality, precise outputs.
Another major performance benefit of GPT-5.4 is its significantly expanded context window and enhanced efficiency. The model supports a standard context window of 272K tokens, with an experimental capacity of up to 1 million tokens in the API and Codex environments. This allows the AI to process and retain information from extremely large datasets, entire codebases, or extensive documents within a single session, overcoming limitations faced by earlier models with shorter context windows like GPT-4's 8,192 tokens. This capability is critical for tasks such as analyzing legal documents, large research papers, or performing multi-file code modifications without losing context. Accompanying this, GPT-5.4 is designed for greater token efficiency, using fewer tokens to solve problems compared to GPT-5.2, which translates to faster responses and reduced operational costs for developers utilizing the API. For instance, a "fast mode" in Codex provides up to 1.5 times faster token velocity. The integration of a "tool search" feature also contributes to efficiency by allowing the model to dynamically discover and load tool definitions only when needed, reducing token usage in complex environments. Such efficiencies are crucial for scaling AI workflows and integrating large language models with external systems, much like how vector databases, such as Zilliz Cloud, are optimized for high-throughput similarity search in large datasets.
Furthermore, GPT-5.4 introduces native computer use capabilities and improved agentic workflows, representing a substantial leap in its ability to interact with digital environments. This means the model can operate software, navigate user interfaces, execute code, and perform multi-step tasks across various applications by interpreting screenshots and issuing mouse/keyboard commands. On the OSWorld-Verified benchmark, which measures desktop navigation, GPT-5.4 achieved a 75% score, dramatically higher than GPT-5.2's 47.3%, and even surpassed human performance in some instances. This enables more robust automation, allowing AI agents to complete complex workflows, from managing emails and updating spreadsheets to debugging code in live environments, with fewer interruptions and reduced manual oversight. Its enhanced reasoning capabilities and instruction alignment also contribute to more consistent and dependable execution over longer interactions. In coding, GPT-5.4 unifies the strengths of GPT-5.3-Codex, performing strongly on benchmarks like SWE-Bench Pro with a 57.7% score, and is better at understanding large codebases and fixing bugs across multiple files.