GPT-5.4 Pro is OpenAI's most advanced model, building on GPT-5.4's unified architecture with enhanced reasoning capabilities for complex, high-stakes tasks. It features a 1M+ token context window (922K input, 128K...
OpenAI GPT-5.4 Pro is a large language model by OpenAI offering a context window of 1,050,000 tokens and a maximum output of 128,000 tokens. It accepts text, image, and file inputs, allowing users to…
GPT-5.4 Pro excels at tasks requiring retention of very long contexts. Examples include: summarizing entire book-length texts, analyzing multi-file research data, generating comprehensive reports with extensive background, maintaining coherent long-running conversations, and performing multi-modal reasoning over documents with images. Its large output token limit also allows producing lengthy generated content without needing multiple continuation calls.
For short, simple tasks like answering a single question, classifying text, or translation of a few sentences, a smaller model with lower context capacity (e.g., GPT-4o Mini or GPT-4.1 Nano) is typically more efficient in cost and latency. GPT-5.4 Pro's vast context window and high capacity come with higher per-token pricing and slower response times. Choose it only when the task genuinely requires that range.
Yes, GPT-5.4 Pro can accept images as part of multi-turn conversations with very large total context. You can include multiple images interspersed with text, all within the 1,050,000-token limit. Each image consumes tokens proportional to its resolution. This enables tasks like analyzing many pages of a scanned book with figures, or reviewing a long visual tutorial with step-by-step images.
Yes, as part of the OpenAI-compatible API, function calling and tool use are supported. You can define functions and have the model decide when to call them. The large context window allows storing many tool call histories, enabling extended agentic workflows over long sessions. This is useful for complex automation that requires many steps of reasoning and external data retrieval.
As of the current information, no publicly released benchmark scores are available for OpenAI GPT-5.4 Pro. The model's performance on standard metrics like MMLU, HumanEval, or GSM8K has not been disclosed. Without such data, direct performance comparisons to other models (e.g., GPT-5.3 Pro or Claude 4) are not possible. Users should evaluate the model internally on their specific tasks to determine suitability.
Processing 1,050,000 tokens in a single request significantly increases time-to-first-token and overall latency. The model must compute attention over the entire context, which is computationally intensive. Accuracy on tasks near the end of the context may degrade if the model struggles to locate relevant information; this is a known limitation for all long-context models. For optimal results, place critical information near the beginning or end.
Key limitations include: higher cost per token compared to smaller models, slower response times due to long context processing, possible accuracy degradation on details buried in the middle of large contexts, and lack of publicly verified benchmark performance. Additionally, the maximum output of 128,000 tokens, while large, may still require multiple calls for very long generations. Input modalities are limited to text, image, and file; audio and video are not directly supported.
Models with typical 128,000-token contexts (e.g., GPT-4o) cannot handle inputs larger than that limit. GPT-5.4 Pro's 1,050,000-token capacity allows processing roughly 8 times more text in a single request, making it superior for long-document analysis but likely overkill for short tasks. The trade-off is that smaller model queries complete much faster and cost less. Benchmarks from similarly-sized models suggest performance may be comparable on tasks fitting within smaller windows.
Pricing for GPT-5.4 Pro is not publicly detailed in the provided facts. Typically, models with very large context windows charge per token for both input and output, often at a premium compared to smaller variants. OrcaRouter bills based on total token usage. Users should consult OrcaRouter's pricing page for current rates. Because of the large context, even a single request can consume millions of tokens, so costs can accumulate quickly.
The primary trade-off is token consumption. A single request using the full 1,050,000-token context costs proportionally many times more than a request using 4,000 tokens. For applications where most queries are short, GPT-5.4 Pro is likely economically inefficient. Consider caching frequently used context or using a cheaper model for preliminary filtering. Some users may benefit from OrcaRouter's caching features to avoid reprocessing identical context.
OrcaRouter may provide caching mechanisms that can cache prompt prefixes or entire context blocks. When the same input is sent repeatedly, caching can avoid re-processing tokens, reducing both cost and latency. For GPT-5.4 Pro, caching long common prefixes (e.g., a system prompt and document) can be particularly beneficial. Check OrcaRouter's documentation for specific caching policies and pricing.
Use the standard chat completions endpoint with base URL https://api.orcarouter.ai/v1. Set the model parameter to openai/gpt-5.4-pro. Example using curl: curl https://api.orcarouter.ai/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{ "model": "openai/gpt-5.4-pro", "messages": [{"role": "user", "content": "Summarize this 10,000 page book."}], "max_tokens": 128000 }' Ensure your API key has access to this model.
The API supports all standard OpenAI chat completions parameters: model, messages, max_tokens, temperature, top_p, n, stream, stop, presence_penalty, frequency_penalty, logit_bias, user, tools, tool_choice, and response_format. For GPT-5.4 Pro, max_tokens can be set up to 128,000. The context window limit includes both input and output tokens; ensure total tokens (messages + max_tokens) do not exceed 1,050,000.
Modify your application's base URL to https://api.orcarouter.ai/v1 and change the model ID to openai/gpt-5.4-pro. Use your OrcaRouter API key instead of an OpenAI key. If your existing code uses the OpenAI Python SDK, update the base_url and model name. No other code changes are required. Ensure your API key has permission for this model. Test with a small context first to verify compatibility.
Yes, streaming is supported by setting the stream parameter to true. The API returns chunks with delta content as with standard OpenAI streaming. Note that due to the large context, time-to-first-token may be longer than with smaller models. Streaming can help display partial results to users while the full response is generated. Use the same chat.completions endpoint with stream: true.
Without benchmark scores, direct performance comparison is not possible. However, GPT-5.4 Pro's context window of 1,050,000 tokens is larger than typical GPT-5.3 Pro (which likely has a smaller context). The max output of 128,000 tokens also surpasses earlier models. In terms of modalities, both support text, image, and file. The key differentiator is context capacity, making GPT-5.4 Pro better for very long documents.
Claude 4 Opus by Anthropic also offers a large context window (commonly around 200,000 tokens). GPT-5.4 Pro's 1,050,000-token window is significantly larger. However, Claude 4 Opus may have different strengths in precision and safety. Both support multi-modal inputs. Without public benchmarks, users should evaluate on their own data. OrcaRouter may offer both models for side-by-side comparison.
Gemini Ultra 2 by Google supports a context window of up to 1,000,000 tokens (in some configurations), similar to GPT-5.4 Pro. Both have large max output capabilities. Gemini Ultra 2 also supports image and video input; GPT-5.4 Pro does not support video directly. The choice may depend on specific task requirements and ecosystem compatibility. OrcaRouter provides access to both models via the same API.
For queries that fit within 128,000 tokens or less, models like GPT-5.2 Turbo, GPT-4o Mini, or Claude 3 Haiku are more cost-effective and faster. If the task involves only text (no images), smaller text-only models can be even cheaper. GPT-5.4 Pro is best reserved for cases where its vast context is essential, such as analyzing an entire book or a massive log file. For routine chat, it's overkill.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.4-pro",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Tier | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| ≤ 272K | $30.00 | $180.00 |
| ≤ ∞ | $60.00 | $270.00 |
| Tier selected by input token count of each request | ||
Estimate based on list price
Tiered pricing — this estimate uses base-tier rates.
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-5.4-proOpen @misc{orcarouter_gpt_5_4_pro,
title = {GPT-5.4 Pro API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.4-pro}
}OpenAI. (2026). GPT-5.4 Pro API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.4-pro