OpenAI

GPT 5.4 Pro

OpenAIToken-basedDynamic Pricing
Alias:gpt-5.4-pro
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Compare gpt-5.4-pro API pricing, supported endpoints, capabilities and access options on voguesly.

textimagestreamingfunction_callingtoolsvisionweb_searchreasoningcontext:1050000
Starting price
View full pricing
Context
1.1M
Maximum input window
Max output
128K
Maximum tokens per response
Modalities
→
Knowledge cutoff
Aug 2025
Released
Mar 2026

Pricing by Supplier

OpenAI
-50%
Openai官方接口
standardInput ≤ 272K tokens
Input/ 1M tokens
$15$30
Output/ 1M tokens
$90$180
long_contextInput > 272K tokens
Input/ 1M tokens
$30$60
Output/ 1M tokens
$135$270

Capabilities / Supported modalities

StreamingFunction callingToolsVisionWeb searchReasoning
Input
Output

Provider & data privacy

Provider
OpenAIDocs
Tokenizer
o200k_base
License
Proprietary (commercial)Proprietary
Data retention30 daysNot used for upstream training by default

Performance

Benchmarks

Scores on standardized evaluations. Higher percentages are better — and rank percentile shows

Metrics sourced fromArtificial Analysis 2026-09-30·GPT 5.4 Pro

This model is not included in the current benchmark snapshot.

Missing models or measurements are not zero scores.

Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.

About GPT 5.4 Pro

深入检查复杂问题的关键环节

GPT-5.4 Pro 面向需要反复推敲的工作,例如梳理跨服务故障、比较架构方案、检查算法边界,或从多份技术材料中找出相互矛盾的条件。它使用更多计算进行推理,接受文本和图片,输出文字分析与代码,适合对结论细节有较高要求的任务。

百万级上下文可以容纳较长的设计文档、日志片段、代码和讨论记录。材料较多时,先说明时间顺序、版本与各自用途,再要求模型把证据组成完整链条。图片可用于补充架构图、界面状态或图表,让文字描述与可见现象一起参与分析。

把结论变成可检查的方案

排障时可以要求按“触发条件、传播路径、可观察证据、修复与验证”展开,避免只得到常见原因清单。方案比较则应明确约束,例如延迟、可用性、改造范围和恢复成本,并要求检查失败情景与反例。资料不足的部分应列成待验证问题,后续补充结果再继续推理。

模型在 Responses 中支持函数调用、搜索、文件检索和计算机操作等工具协作。应用需要配置工具权限与执行环境,并传回实际结果,让后续分析基于新的观察。

调用时预留等待时间

本模型使用 Responses API,reasoning.effort 可选 medium、high 和 xhigh,默认 medium。困难请求可能需要数分钟,应安排足够的超时和任务状态管理;应用支持时可采用后台执行。适合需要深入分析的工作,使用前可先明确本轮最重要的判断与交付结果。

Use cases and prompting

分析跨服务故障

“结合调用链、错误日志、配置变更记录和架构图,重建这次超时发生的过程。检查哪些证据支持连接池耗尽,哪些现象还不能解释;比较临时缓解与长期修复方案,给出最小验证步骤及失败时的恢复条件。”

日志很多,应怎样提供?

先标明时间范围与服务关系,保留关键上下游记录,说明哪些字段可关联同一请求,减少无关重复材料。

如何选择推理档位?

从 medium 开始,复杂且需要更深入分析的任务再考虑 high 或 xhigh,同时预留更长等待时间并检查实际结果。

API access

API documentation

Code samples

RequestPOST/v1/chat/completions
Example request
Parameters
ParameterTypeDefault / rangeDescription
temperature
number
=10 ~ 2
Sampling temperature; lower is more deterministic
top_p
number
=10 ~ 1
Nucleus sampling probability mass
max_tokens
integer>= 1Maximum number of tokens in the response
frequency_penalty
number
=0-2 ~ 2
Penalises repetition of frequent tokens
presence_penalty
number
=0-2 ~ 2
Encourages introducing new topics
stop
array—Up to 4 strings that stop generation
seed
integer—Deterministic sampling seed (best-effort)
n
integer
=1>= 1
Number of completions to generate
stream
boolean
=false
Stream tokens via Server-Sent Events
response_format
object—Force JSON object or schema-conforming output
tools
array—Tool / function declarations the model may call
tool_choice
string
autononerequired
Tool-choice policy or specific tool name
logprobs
boolean
=false
Return per-token log probabilities
top_logprobs
integer0 ~ 20Number of top log probabilities returned per token
logit_bias
object—Per-token logit bias map
user
string—End-user identifier for abuse monitoring

Replace <YOUR_API_KEY> with the API key from your token settings.

Authentication

All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.

Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.

Supported parameters

Generation parameters
ParameterTypeDefault / rangeDescription
temperature
number
=10 ~ 2
Sampling temperature; lower is more deterministic
top_p
number
=10 ~ 1
Nucleus sampling probability mass
max_tokens
integer>= 1Maximum number of tokens in the response
frequency_penalty
number
=0-2 ~ 2
Penalises repetition of frequent tokens
presence_penalty
number
=0-2 ~ 2
Encourages introducing new topics
stop
array—Up to 4 strings that stop generation
seed
integer—Deterministic sampling seed (best-effort)
n
integer
=1>= 1
Number of completions to generate
stream
boolean
=false
Stream tokens via Server-Sent Events
response_format
object—Force JSON object or schema-conforming output
tools
array—Tool / function declarations the model may call
tool_choice
string
autononerequired
Tool-choice policy or specific tool name
logprobs
boolean
=false
Return per-token log probabilities
top_logprobs
integer0 ~ 20Number of top log probabilities returned per token
logit_bias
object—Per-token logit bias map
user
string—End-user identifier for abuse monitoring

Rate limits

SupplierRPMTPMRPD
OpenAIUnlimitedUnlimitedUnlimited

No restriction

Frequently asked questions about gpt-5.4-pro

What is gpt-5.4-pro?

Compare gpt-5.4-pro API pricing, supported endpoints, capabilities and access options on voguesly.

How do I call gpt-5.4-pro?

Create an API key with access to gpt-5.4-pro, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.

How is gpt-5.4-pro priced?

Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.

What is the context window of gpt-5.4-pro?

The model catalog lists a context window of 1050000 tokens. Check the selected endpoint for request limits.

What is the maximum output of gpt-5.4-pro?

The model catalog lists a maximum output of 128000 tokens. Your request settings may set a lower limit.

How should I evaluate gpt-5.4-pro for my project?

Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.