GPT-4o vs GPT-5.4 Pro (xhigh): Which Model Should Developers Choose?

GPT-4o (Nov '24) is the safer developer choice because it has measured evaluation data and a much lower listed benchmark price, while GPT-5.4 Pro (xhigh) remains unverified in the supplied official documentation.

The comparison is unusual: the newer model has a release date of 2026-03-05 in the data snapshot, yet the supplied OpenAI Models page does not list gpt-5-4-pro. The same page does not currently list gpt-4o either. That means model age alone cannot establish availability, quality, or upgrade value.

This article separates what the data shows from what developers still need to validate. The benchmark dataset reports GPT-4o results across several tasks, but it reports no evaluation values for GPT-5.4 Pro (xhigh). Data provided by https://artificialanalysis.ai/.

GPT-4o (Nov '24) offers the only evidence-backed starting point, while GPT-5.4 Pro (xhigh) should be treated as a candidate requiring access and task-level validation.

Decision factor	GPT-4o (Nov '24)	GPT-5.4 Pro (xhigh)
Evidence in the supplied benchmark snapshot	Reported scores across intelligence, mathematics, coding-related, reasoning, and instruction-following evaluations	No reported evaluation scores
Blended price per 1M tokens	$4.375	$67.5
Input price per 1M tokens	$2.5	$30
Output price per 1M tokens	$10	$180
Median output speed	0 tokens per second	0 tokens per second
Latency	0 seconds	0 seconds
Official current model listing	Not found in the supplied OpenAI Models page	Not found in the supplied OpenAI Models page

The key selection question is therefore not simply whether a newer model should replace an older model. It is whether GPT-5.4 Pro (xhigh) can demonstrate enough task-specific improvement to justify a blended price that is $67.5 rather than $4.375 per 1M tokens.

The supplied evidence cannot answer that question yet. It does not provide a verified alias, context window, output limit, benchmark result, or documented failure profile for either named version. Developers should avoid treating the labels as confirmed production API identities until access is checked.

GPT-4o (Nov '24) is the only model with measurable performance evidence in the supplied snapshot, so GPT-5.4 Pro (xhigh) cannot be declared the quality winner.

GPT-4o records an Artificial Analysis Intelligence Index of 11.1, a mathematics index of 6, an MMLU Pro score of 0.748, and a GPQA score of 0.543. It also records 0.309 on LiveCodeBench, 0.333 on SciCode, and 0.0833333333333333 on TerminalBench Hard. These results suggest that GPT-4o has observable capability across general knowledge, difficult questions, coding tasks, scientific coding, and terminal-oriented work. They do not prove that it is best for every developer workload.

The practical meaning of these results depends on the task. A model with a reported coding-related score can still fail on repository-specific conventions, tool use, hidden requirements, or long multi-step changes. A mathematics score can indicate useful reasoning ability without predicting reliability in production business logic. The supplied material gives no test methodology detailed enough to translate these scores into a guaranteed pass rate for a particular application.

GPT-5.4 Pro (xhigh) has null values for the listed evaluations, including the intelligence, coding, mathematics, MMLU Pro, GPQA, LiveCodeBench, SciCode, and TerminalBench measures. That is an evidence gap, not evidence of poor performance. It means developers cannot make a defensible quality claim from this dataset.

The speed fields do not separate the models: both report 0 median output tokens per second and 0 seconds of latency. Those values should be read as unavailable or non-informative measurement fields for this comparison, rather than proof that the models have identical real-world response speed. An application team still needs direct latency and throughput testing.

GPT-4o (Nov '24) is the clear cost choice because GPT-5.4 Pro (xhigh) is priced far higher without supplied evidence of a compensating quality advantage.

The blended price is $4.375 per 1M tokens for GPT-4o and $67.5 for GPT-5.4 Pro (xhigh). Input tokens are priced at $2.5 and $30 respectively, while output tokens are priced at $10 and $180. The output price difference matters especially for agents, code generation, document drafting, and workflows that ask the model to produce long answers or many intermediate steps.

A lower token price does not automatically mean a lower total operating cost. GPT-4o could become more expensive in practice if it needs more retries, more tool calls, more human review, or a larger surrounding workflow to reach the same business result. GPT-5.4 Pro could be economically reasonable if it materially reduces those downstream costs. The supplied research does not establish whether that happens.

The reverse risk is just as important. Paying $67.5 per 1M blended tokens for GPT-5.4 Pro without verified benchmark results creates a direct cost increase before any benefit is proven. Developers should compare completed tasks, not only token prices. Useful measurements include successful task completion, correction work, tool-call count, and user acceptance, but the supplied brief provides none of those production measurements.

The pricing conclusion is therefore strong but conditional: choose GPT-4o for predictable budget control, and approve GPT-5.4 Pro only after a controlled evaluation shows that its output saves enough downstream effort to offset the listed prices.

GPT-4o (Nov '24) is the default recommendation for teams that need an evidence-backed and cost-conscious starting point.

Choose GPT-4o when the product needs general text generation, routine coding assistance, structured answers, or broad reasoning support and the team can validate outputs with normal application tests. Its supplied benchmark coverage gives developers something concrete to inspect, and its $4.375 blended price per 1M tokens lowers the cost of experimentation and early production traffic.

Consider GPT-5.4 Pro (xhigh) only when the team has confirmed that the exact model identifier is callable, documented the available API behavior, and tested representative tasks. The model may still be valuable for difficult reasoning or high-cost human workflows, but the supplied sources do not establish that advantage. A team should not infer it from the version name, the listed release date, or the Pro label.

For a migration decision, keep the burden of proof on the proposed upgrade. Start with the real tasks that matter: coding changes, tool-driven actions, difficult analysis, and outputs that currently require human correction. Compare the two models using the same prompts, tools, acceptance rules, and workload samples. The supplied snapshot can provide GPT-4o reference results, but it cannot provide a direct quality delta because GPT-5.4 Pro has no reported evaluation values.

The strongest present decision is simple:

• Use GPT-4o as the baseline for cost, availability checks, and quality testing.
• Use GPT-5.4 Pro only as an experimental option until access, pricing, and performance are verified.
• Revisit the choice when official documentation or reproducible tests fill the current evidence gap.

The official OpenAI Pricing page does not list either supplied model identifier, so production procurement should include an explicit availability check before implementation.

GPT-4o (Nov '24) and GPT-5.4 Pro (xhigh) both lack enough version-specific documentation to support confident production assumptions.

The supplied official material does not confirm the context window, maximum output length, stable alias, API parameters, or version-specific multimodal behavior for either named model. The official models page gives general statements about current OpenAI models supporting text and image input, text output, multilingual capability, and vision, but it does not clearly assign those properties to these two exact labels.

The research also contains no reliable Reddit, Hacker News, or X posts that clearly discuss either exact version. There are no verified community comparisons, disclosed test methods, or confirmed failure cases. Developers therefore cannot use the supplied research to claim that one model is more reliable, faster, better at coding, or safer in a particular workflow.

This uncertainty changes the buying process. Documentation verification is part of model selection, not an administrative detail. If the identifier cannot be called or the terms are unclear, a benchmark advantage would not be enough to make it a practical production choice. If GPT-5.4 Pro becomes available and performs better on the team’s real tasks, its higher price may be justified, but that conclusion remains unproven here.