GPT-5.4 Pro API: Complete Developer Guide — Pricing, Setup & When to Use It

TL;DR: GPT-5.4 Pro costs $30/M input and $180/M output when you go direct to OpenAI. Via ofox, Pro Plan members pay roughly $24/$144 with the 20% flagship discount. The model scores 75% on OSWorld (above the human expert baseline) and 89.3% on BrowseComp — benchmarks that reflect the long-horizon agentic tasks where it earns that price. Here’s exactly how to set it up and when to choose Mini or Nano instead.

Why GPT-5.4 Pro Still Matters (Even With GPT-5.5 Out)

GPT-5.4 Pro remains OpenAI's most battle-tested model for production agentic workloads — teams running it since March haven't stopped just because GPT-5.5 appeared on April 23.

OpenAI has not deprecated GPT-5.4 Pro. It launched March 5, 2026, with a 1.05M-token context window, 128K max output, and benchmark scores that still hold up: 57.7% on SWE-Bench Pro, 75% on OSWorld (beating the 72.4% human expert baseline), 89.3% on BrowseComp. These reflect the model’s real strength — long chains of reasoning that don’t fall apart, agents that actually finish hard tasks, document analysis at scale.

If you’re evaluating whether to adopt GPT-5.4 Pro or migrate an existing stack, this guide skips the marketing summary and gives you the pricing math, the setup, and an honest view on when the cost is justified.

Pricing: The Honest Numbers

GPT-5.4 Pro is expensive. Here’s the full breakdown across the GPT-5.4 tier:

Source: OpenAI API Pricing, April 2026.

The gap between Pro and Mini is 40× on both input and output. That spread is intentional — Pro is built for tasks where errors compound across steps and cheaper models fail, not for general-purpose completion volume.

One trap teams hit: GPT-5.4 Pro has a long-context surcharge. Prompts exceeding 272K input tokens trigger 2× input pricing ($60/M) and 1.5× output pricing ($270/M) for the entire session. An agent that accumulates conversation history across hours can silently cross this threshold. Budget for it explicitly or implement context window management before you’re surprised by a billing alert.

Via ofox: ofox’s Pro Plan applies a 20% discount on flagship models. Applied to GPT-5.4 Pro:

Beyond the per-token discount, ofox gives you a single API key for GPT-5.4 Pro, Claude Opus 4.6, Gemini 3.1 Pro, and 10+ other models. That matters for teams running experiments across providers — see how a unified API gateway reduces operational overhead.

Setup in Under 5 Minutes

ofox exposes a fully OpenAI-compatible endpoint at https://api.ofox.ai/v1. If you’re already on the OpenAI Python SDK, the change is two lines:

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-ofox-key",
    base_url="https://api.ofox.ai/v1"
)

response = client.chat.completions.create(
    model="gpt-5.4-pro",
    messages=[{"role": "user", "content": "Your prompt here"}]
)
print(response.choices[0].message.content)

Every OpenAI SDK parameter works identically: temperature, max_tokens, tools, stream. No new authentication pattern, no new SDK to install. For Node.js:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-your-ofox-key",
  baseURL: "https://api.ofox.ai/v1"
});

If you’re migrating from a direct OpenAI integration, the OpenAI SDK migration guide walks through every edge case — streaming responses, function calls, handling retries.

GPT-5.4 Pro vs Mini vs Nano: Which Tier for What

The 40× price difference reflects a real capability split, not a branding exercise. Here’s how to route tasks:

Reach for GPT-5.4 Pro when:

• The task is multi-step and errors compound (code refactoring across a large codebase, agentic loops calling real APIs)
• You need coherent reasoning over 100K+ tokens of context
• The task resembles the OSWorld or BrowseComp benchmark categories — computer-use, persistent web research, complex cross-document synthesis

Reach for GPT-5.4 Mini when:

• You need near-Pro reasoning quality at significantly faster throughput
• The task is a coding or engineering problem where SWE-Bench-level capability is needed but not the absolute ceiling
• Cost is a concern and you have an eval suite to measure the quality gap

Reach for GPT-5.4 Nano when:

• The task is classification, entity extraction, intent detection, or reranking
• You’re running high-volume, short-context jobs where price-per-call is the primary constraint
• Latency is critical and the task is deterministic enough to eval cheaply

The practical workflow: write an eval for your task, run Mini against it first, only pay for Pro when Mini’s score isn’t acceptable. This pattern reduces most teams’ API spend by 60–80% without quality loss on the tasks that don’t need Pro. For the full framework, see how to reduce AI API costs.

Benchmark Numbers, Honestly Interpreted

The benchmarks OpenAI published at GPT-5.4 Pro’s launch are real, but they measure specific task types:

• SWE-Bench Pro (57.7%): Resolving actual GitHub issues in real codebases. This is the harder variant — it requires reading existing code structure and context, not generating from scratch. 57.7% is state-of-the-art as of March 2026.
• OSWorld (75%): Navigating computer interfaces — clicking, typing, managing windows, completing multi-step GUI tasks. The human expert baseline is 72.4%. GPT-5.4 Pro exceeds it.
• BrowseComp (89.3%): Locating hard-to-find facts via web search, requiring cross-referencing multiple sources and persisting through dead ends.

These benchmarks favor long-horizon, tool-using, reasoning-heavy tasks. If your workload is shorter and simpler — summarizing a document, answering a FAQ, classifying support tickets — these numbers don’t predict your quality experience. They’re measuring a different capability ceiling than you need.

For a broader view of how GPT-5.4 Pro ranks across model families and task categories, see the LLM Leaderboard (April 2026).

How GPT-5.4 Pro Compares to Claude Opus 4.6 and Gemini 3.1 Pro

GPT-5.4 Pro is substantially more expensive than either Claude Opus 4.6 or Gemini 3.1 Pro — all three are available on ofox via the same API key. The trade-off isn’t always in GPT-5.4 Pro’s favor.

Claude Opus 4.6 costs significantly less and leads on instruction-following, long-form document tasks, and structured output generation. For teams building document pipelines or coding agents that don’t require OSWorld-level computer use, Opus 4.6 often matches Pro quality at lower cost. Full pricing breakdown: Claude Opus 4.6 API Review.

Gemini 3.1 Pro has a 1M-token context window (matching GPT-5.4 Pro) and strong performance on structured data tasks and multimodal inputs. If your workload involves processing large documents or mixed text/image inputs at lower cost, Gemini 3.1 Pro is worth benchmarking directly. Full guide: Gemini 3.1 Pro API Guide.

The 2026 model comparison guide covers the full decision framework for picking across all three when you’re not committed to a single provider.

When NOT to Use GPT-5.4 Pro

Most tasks don’t need it. That’s the honest answer.

If you’re building a chatbot, a documentation Q&A, a code autocomplete, or any task where you can write a cheap eval and measure output quality — start with Mini. Only move to Pro when Mini fails your eval threshold. The 40× cost difference compounds fast at any real volume.

Beyond cost: GPT-5.4 Mini offers significantly faster throughput than Pro. For latency-sensitive applications, the speed gap matters independently of the price. Nano is even faster. If response time is part of your product SLA, factor throughput into the model choice alongside quality.

Bottom Line

GPT-5.4 Pro is the model you deploy when you need an agent to finish a hard, multi-step task without losing the thread — the OSWorld and SWE-Bench Pro numbers back that, and nothing at a similar price point has beaten them yet.

But “Pro-tier task” is a smaller category than most use cases. Run your eval on Mini first. If the score holds, you’ve saved 40× on API cost. If it doesn’t, set up via ofox with two changed lines in your existing SDK client, take the Pro Plan discount, and build your context window management strategy before you hit the 272K surcharge threshold.

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Pricing data sourced from OpenAI API documentation and ofox model catalog, April 2026.