GPT-4o vs GPT-5.4

Choose GPT-4o if you want…

• ✓Faster output (~198.3 tokens/sec)
• ✓Lower price ($4.38 / 1M blended)

Choose GPT-5.4 if you want…

• ✓Higher intelligence (Artificial Analysis index 57)
• ✓Larger context window (1.1M)

GPT-4o

Speed is GPT-4o's defining trait. Where comparable models average 61 tokens per second, GPT-4o delivers nearly 200 — and its native audio pipeline hits 320ms response latency, making it the practical choice for voice interfaces and real-time chat. It also collapses text, image, and audio processing into a single unified model rather than routing across separate systems, which produces more coherent multimodal reasoning without the awkward handoffs. Users feel this difference acutely. When OpenAI tried to retire GPT-4o in early 2026, the backlash was fierce enough to reverse the decision — petitions, mass unsubscribe threats, and user surveys suggesting 95% found no adequate replacement. That kind of loyalty comes from how the model feels in practice: snappy, versatile, fluent across 50+ languages, and capable of web search that reasoning-focused models like o1 lack. The honest caveat: GPT-4o trades raw reasoning depth for speed. It scores below average on Artificial Analysis's Intelligence Index and struggles with complex multi-step logic. For hard reasoning or large-document tasks, newer models outclass it. For fast, general-purpose, multimodal work, few match it.

GPT-5.4

GPT-5.4 was built for the actual work that happens inside offices — financial modeling, legal analysis, complex codebases, and multi-step document workflows — rather than for chasing narrow benchmarks. That strategic shift shows in the numbers: it matched or outperformed human professionals in 83% of head-to-head comparisons, and developers have called its coding output "flawless," with some declaring it the definitive choice for complex software engineering work. Native computer-use capabilities let it operate browsers and desktop apps directly, and it scored above the human baseline on UI interaction tasks. The 1.05 million token context window handles large codebases and lengthy legal documents in a single pass, though you need to configure it explicitly — the default is 272K. Where GPT-5.4 falls short is nuance: it tends to interpret requests too literally, missing the intent behind ambiguous prompts in ways that Claude handles more naturally. Writing personality is another common frustration, with verbose follow-up suggestions that can feel mechanical. For structured professional tasks where thoroughness and tool integration matter more than prose feel, it is the strongest model in the GPT-5 line prior to the release of GPT-5.5.