GPT-5 (high) vs GPT-5.4 mini (xhigh): The Ultimate Performance & Pricing Comparison

GPT-5.4 mini (xhigh) is the stronger default for most new developer workloads because it scores higher on the available intelligence and coding indices while costing less per blended token. The data snapshot reports an intelligence index of 40 for GPT-5.4 mini (xhigh), compared with 34.7 for GPT-5. GPT-5.4 mini (xhigh) also leads the coding index at 56.1, compared with 37.8 for GPT-5. The blended price is $1.6875 per 1M tokens for GPT-5.4 mini (xhigh), versus $3.4375 for GPT-5.

That result does not make GPT-5 obsolete for every use case. GPT-5 has a reported math index of 94.3, while the supplied data contains no corresponding GPT-5.4 mini math score. GPT-5 also has substantially clearer public documentation for reasoning controls, tool calling, supported modalities, and official benchmark conditions. Developers therefore face a familiar tradeoff: GPT-5.4 mini offers better measured value and coding results, while GPT-5 offers a more documented capability envelope.

Data provided by https://artificialanalysis.ai/.

GPT-5.4 mini (xhigh) wins the measured comparison, but GPT-5 remains easier to evaluate when your workload depends on documented reasoning behavior or math performance. The supplied Artificial Analysis snapshot gives GPT-5.4 mini (xhigh) the higher intelligence index, 40 versus 34.7, and the higher coding index, 56.1 versus 37.8. It also gives both models the same latency value of 0.3 seconds, with no output-speed result for either model.

The commercial difference is large enough to affect architecture. GPT-5.4 mini (xhigh) costs $0.75 per 1M input tokens and $4.5 per 1M output tokens. GPT-5 costs $1.25 for input and $10 for output. Output-heavy agents, code generation loops, and repeated repair passes will feel that difference more strongly than short classification requests.

The research briefs also expose an evidence imbalance. OpenAI documents GPT-5 through GPT-5 for developers and the GPT-5 model documentation. For GPT-5.4 mini, the available official material is a model directory at OpenAI Models and a pricing directory at OpenAI Pricing. The supplied research does not provide a direct GPT-5.4 mini benchmark, context limit, output limit, or community test.

GPT-5.4 mini (xhigh) has the stronger measured coding profile, while GPT-5 retains the only reported math score. The coding index difference is 56.1 versus 37.8, which is large enough to justify testing GPT-5.4 mini first for code generation, debugging, refactoring, and agent tasks. The result still does not identify which repositories, languages, or repair patterns produced the advantage. A benchmark lead is a routing signal, not a guarantee for every production codebase.

The intelligence index points in the same direction, with GPT-5.4 mini (xhigh) at 40 and GPT-5 at 34.7. That alignment makes the mini model the more efficient first candidate for general developer workflows. Yet GPT-5 has a reported math index of 94.3, and the mini model has no supplied math result. A team building symbolic reasoning, quantitative verification, or math-heavy evaluation should treat the comparison as incomplete rather than assuming the coding winner also wins mathematics.

The latency data does not separate the models. Both are listed at 0.3 seconds, and neither has a reported median output speed. That means the available evidence cannot support a speed-based choice. Developers should measure time to first token, sustained generation, tool-call turnaround, and end-to-end task completion in their own stack.

GPT-5 has clearer documented controls for reasoning effort and verbosity, plus function calling, structured outputs, streaming, and custom tools, according to GPT-5 for developers and GPT-5 model documentation. The supplied research does not confirm the same details for GPT-5.4 mini. That documentation gap matters when implementation risk is more important than benchmark rank.

GPT-5.4 mini (xhigh) is the cheaper production choice, especially for workloads that generate substantial output. Its blended price is $1.6875 per 1M tokens, compared with $3.4375 for GPT-5. Input pricing is $0.75 versus $1.25, while output pricing is $4.5 versus $10. The output gap matters because coding agents often produce patches, explanations, tests, and follow-up repairs in the same workflow.

The cheaper model can still become more expensive if it requires more retries, longer prompts, extra verification, or human review. The supplied data does not measure retry rates, task success, token efficiency, or total cost per completed task for GPT-5.4 mini (xhigh). Developers should therefore compare cost per accepted result, not only cost per token. The available pricing snapshot supports a strong unit-cost advantage, but it does not prove a lower bill for every application.

GPT-5.4 mini also has listed Batch and Flex prices of $0.375 per 1M input tokens and $2.25 per 1M output tokens. Its listed Fast mode prices are $1.50 for input and $9 for output per 1M tokens. GPT-5's supplied comparison contains only its standard input, output, and blended prices. These modes can change the operational decision, but the research brief does not establish whether every mode is available to every account, region, or endpoint.

The official OpenAI Pricing page confirms the mini model's pricing entries. It does not resolve the missing question of long-context pricing. The absence of a listed long-context price is evidence of incomplete pricing information, not proof that long context is unsupported.

GPT-5.4 mini (xhigh) is the default pick for new coding and general-purpose applications that prioritize measured value. Its coding index of 56.1, intelligence index of 40, and blended price of $1.6875 create the clearest case for starting there. Use a representative evaluation set before committing, because the supplied data does not show task success rates or production reliability.

GPT-5 is the safer pick when documented API behavior is central to the design. OpenAI explicitly describes GPT-5 as a reasoning model for coding, reasoning, and agentic tasks in GPT-5 for developers. The model documentation also describes its text and image input, text output, tool features, reasoning-effort controls, and verbosity controls. Those details can reduce integration uncertainty when the application depends on structured tool use or carefully tuned reasoning.

GPT-5 is also the more defensible candidate for math-heavy work because its supplied math index is 94.3 and GPT-5.4 mini has no math score in the data snapshot. That is not proof that GPT-5.4 mini performs worse. It means the evidence is insufficient to make the mini model the evidence-backed choice for that workload.

Version management should influence the final decision. The GPT-5 documentation marks the fixed snapshot gpt-5-2025-08-07 as Deprecated and describes GPT-5 as a previous-generation model. The supplied research does not find a deprecation or replacement notice for gpt-5.4-mini; OpenAI Models still lists it in the official catalog. Teams choosing GPT-5 should plan migration work. Teams choosing GPT-5.4 mini should validate availability for their account and endpoint.

GPT-5.4 mini (xhigh) is the better starting point for most developers because the supplied comparison shows higher coding and intelligence indices at a lower blended price. GPT-5 remains relevant when documented reasoning controls, official feature detail, or the reported math result matters more than token cost.

The evidence is not balanced across the models. GPT-5 has official benchmark and feature documentation, plus limited community feedback in this Reddit discussion. The research brief finds no reliable community tests for GPT-5.4 mini and no direct mini benchmark results. That asymmetry should shape how confidently teams generalize from the available numbers.