GPT-5.4 (xhigh) vs o3: The Ultimate Performance & Pricing Comparison

GPT-5.4 (xhigh) is the stronger default for broad developer workloads, while o3 is cheaper and has the only reported output-speed measurement. Artificial Analysis reports an Intelligence Index of 51.4 for GPT-5.4 (xhigh) and 30.4 for o3, a gap of 21 points. The same snapshot reports GPT-5.4 (xhigh) at 71.1 on the Coding Index, while no comparable o3 coding score is supplied. o3 does lead on the reported Math Index at 88.3, but GPT-5.4 (xhigh) has no corresponding value in the snapshot. Latency is tied at 0.3 seconds, and o3 is measured at 128.056 median output tokens per second while GPT-5.4 (xhigh) has no reported value.

The practical decision is therefore asymmetric. GPT-5.4 (xhigh) has broader evidence across general intelligence and coding. o3 has a lower blended price, a lower input price, a lower output price, and a strong reported mathematics score. That makes o3 attractive for cost-sensitive mathematical workloads, but the supplied official material does not establish whether o3 remains directly callable today. OpenAI's GPT-5.4 model page confirms GPT-5.4's current API documentation, while the OpenAI model directory does not list o3 in the provided research.

GPT-5.4 (xhigh) offers the more complete evidence base for general development, whereas o3 combines lower cost with a narrower but notable mathematics signal. The comparison should not be read as a complete benchmark ranking because the two models do not have matching measurements in every category.

GPT-5.4 is still directly documented as an API model and is not marked Deprecated on its model page. The same page identifies the stable alias gpt-5.4 and the snapshot gpt-5.4-2026-03-05. By contrast, the supplied OpenAI model directory does not provide current o3 availability, alias, context, output, or multimodal details. That documentation gap is a deployment risk, not proof that o3 cannot be used.

GPT-5.4 (xhigh) is the safer performance choice for mixed coding and general reasoning workloads because its available evidence spans both areas. Artificial Analysis reports GPT-5.4 (xhigh) at 51.4 on its Intelligence Index and 71.1 on its Coding Index. Those measurements suggest a model suited to tasks that move between repository understanding, implementation, explanation, and tool-mediated work. They do not guarantee success on a particular codebase, because the supplied research contains no reproducible independent coding test for either model.

O3 is more difficult to place confidently. Its reported Math Index is 88.3, which gives it a meaningful specialist case for mathematical reasoning, verification, and workloads where correctness depends on symbolic or quantitative steps. The snapshot supplies no matching GPT-5.4 mathematics value, so it cannot establish a head-to-head mathematics winner. It also supplies no o3 Coding Index value, so GPT-5.4's 71.1 should be treated as evidence of measured coverage, not as a complete proof of coding superiority.

Latency does not separate the models in the supplied data: both are listed at 0.3 seconds. Only o3 has a reported median output rate, at 128.056 tokens per second. That makes o3 the only model with a documented throughput advantage in this snapshot, but the missing GPT-5.4 value prevents a fair speed comparison. Community reports add uncertainty rather than resolution. A Hacker News discussion contains conflicting views about stability, configuration sensitivity, and latency, without a reproducible test method.

o3 is the cheaper model on every supplied price measure, but GPT-5.4 can still be the lower-cost choice if better task completion reduces retries or human intervention. The data snapshot puts o3 at $3.5 per 1M blended tokens versus $5.625 for GPT-5.4 (xhigh). Input pricing is $2 for o3 and $2.5 for GPT-5.4, while output pricing is $8 for o3 and $15 for GPT-5.4.

The important cost variable is not the headline rate alone. A workload that generates long answers, repeats failed tool calls, or requires additional review can turn a cheaper request into a more expensive workflow. The supplied research gives no controlled measurement of retries, correction time, or production task completion, so no total-cost winner can be proved beyond token pricing. A developer should therefore map price to task value: use o3 where its mathematical signal is relevant and response volume is high; consider GPT-5.4 where broader capability may reduce orchestration overhead.

Long-context usage creates a separate GPT-5.4 risk. The official GPT-5.4 pricing documentation lists standard, Batch, Flex, and Fast mode prices, while the GPT-5.4 model documentation states that inputs above 272K tokens trigger higher billing multipliers for the whole session. The supplied o3 material does not provide comparable long-context rules, so the cost comparison becomes incomplete for very large prompts.

GPT-5.4 (xhigh) has the clearer documented capability boundary, while o3 has the larger documentation gap. The official GPT-5.4 model page documents text and image input, text output, streaming, function calling, structured output, Chat Completions, Responses, and Batch API support. It also documents tool access through Responses, including web search, file search, image generation, code interpreter, hosted shell, computer use, and MCP-related tools. GPT-5.4 does not support audio input, video input, or fine-tuning according to the same official model documentation.

OpenAI's launch announcement positions GPT-5.4 for professional knowledge work, coding, tool calling, and computer operation. The GPT-5.4 announcement also reports official results including GDPval at 83.0%, SWE-Bench Pro Public at 57.7%, OSWorld-Verified at 75.0%, Toolathlon at 54.6%, and BrowseComp at 82.7%. These results are useful context, but they are not direct substitutes for the Artificial Analysis comparison because the evaluation methods and model settings differ.

For o3, the supplied official pages do not establish context size, output limit, parameters, multimodal support, stable alias, direct API availability, or current restrictions. They also do not provide an official o3 benchmark. That absence should influence architecture decisions: GPT-5.4 is easier to specify, while o3 requires an availability and compatibility check before adoption.

GPT-5.4 (xhigh) is the recommended default when one model must cover coding, general reasoning, and tool-heavy workflows. Its measured Intelligence Index of 51.4 and Coding Index of 71.1 provide the broadest relevant evidence in the supplied snapshot. Its official API documentation also gives developers a defined alias, a lockable snapshot, supported APIs, supported tools, and explicit limitations. That combination reduces uncertainty during integration and evaluation.

Choose o3 when token economics or mathematical specialization is the primary requirement and your deployment can verify access independently. Its blended price is $3.5 per 1M tokens, its output price is $8, and its Math Index is 88.3. Those are strong reasons to test o3 for high-volume mathematical workloads, scoring pipelines, or tasks where the model's documented speed value of 128.056 tokens per second is useful. The decision remains conditional because the supplied official sources do not confirm current o3 availability or API details.

A sensible evaluation sequence is small and task-specific. Test representative coding tasks, mathematical tasks, tool calls, long outputs, and failure recovery. Track successful completion, retry count, human correction, latency, and token usage. The supplied research does not provide those production metrics, and community reports are not sufficient substitutes. One Hacker News comment describes monthly spending of $300–400 during sustained GPT-5.4 use, while another comment describes a personal frontend test. Both are useful cautionary anecdotes, not controlled benchmarks.

GPT-5.4 (xhigh) is the better starting point for most developers who need one broadly capable model. Its available evidence covers general intelligence and coding, and its official API surface is documented. O3 remains worth testing for mathematical workloads and lower-cost traffic, but its current integration status is not established by the supplied official material.