GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for...
GPT-5.4 is an OpenAI large language model with a context window of 1,050,000 tokens and max output of 128,000 tokens. It processes text, image, and file inputs. The model was evaluated on GPQA…
GPT-5.4 excels at language understanding, generation, reasoning, and multimodal interpretation. Its large context window supports tasks like multi-step instruction following, long-form content creation, and complex dialogue. The model is particularly strong on graduate-level science reasoning, scoring 92.0 on GPQA Diamond. It can also handle file‑based data extraction and image description. When selecting a model, consider if your use case truly requires the full context or if a cheaper model suffices.
With a 1,050,000-token context, GPT-5.4 can ingest entire books, lengthy reports, or thousands of lines of code in a single prompt. This eliminates the need for document splitting and allows the model to consider all information simultaneously. Output is capped at 128,000 tokens, so summaries or extractions can be similarly long. For tasks that do not need full long-context, smaller models may be more cost-effective.
Yes, GPT-5.4 supports image and file inputs alongside text. Images can be provided in standard formats (JPEG, PNG, etc.) and the model can answer questions about visual content. Files (e.g., PDF, CSV) are uploaded and processed as part of the context. This multimodal capability is useful for analyzing diagrams, extracting data from tables, or cross-referencing text with graphics. All input modalities count toward the context token limit.
If your task does not require the full 1,050,000-token context or multimodal input, consider models with smaller context windows or limited modalities to reduce cost. For example, simple single‑turn queries, short texts, or tasks that do not benefit from extensive reasoning can be handled by models like GPT-4o mini or GPT-4.1 nano. Evaluate your prompt length and complexity before selecting GPT-5.4 to avoid paying for unused capacity.
GPT-5.4 achieved a score of 92.0 on GPQA Diamond, a benchmark of 198 multiple‑choice questions covering graduate‑level physics, chemistry, and biology. This score indicates high accuracy on expert‑level scientific reasoning. No other benchmark scores are available for this model in the provided facts. Users should evaluate performance on their own domain‑specific tasks.
A score of 92.0 means GPT-5.4 correctly answered 92% of the GPQA Diamond questions. GPQA Diamond is designed to test knowledge and reasoning that a human expert would possess after years of graduate study. It includes multi‑step problems, interpretation of scientific data, and nuanced concept application. This benchmark is often used to gauge a model's ability to handle complex, domain‑specific queries.
Strengths: very long context (1,050,000 tokens), high scientific reasoning (92.0 GPQA Diamond), multimodal input (text, image, file). Limitations: no pricing information provided; latency increases with context length; extremely large contexts may hit token limits or degrade response quality on peripheral details. The model does not support real‑time streaming or voice input. For tasks that are not science‑heavy, other models may be equally capable at lower cost.
Inference speed is not specified in the provided facts. Generally, models with larger parameter counts and longer context windows take longer to process each token. Users should expect higher latency compared to smaller models like GPT-4o mini. OrcaRouter may have its own caching or optimization layer, but actual throughput depends on request size and concurrent load. Testing with representative prompts is recommended.
Pricing details for GPT-5.4 on OrcaRouter are not provided in the facts. Typically, OpenAI model pricing is based on per‑token input and output rates, and OrcaRouter may apply its own markup or offer bundled plans. To get current pricing, consult OrcaRouter's pricing page or contact their sales team. Costs scale with context length because every token is charged.
Using the full 1,050,000‑token context window incurs costs proportional to the total number of input tokens. If your task uses only a fraction of that capacity, you are still billed for the entire prompt. Therefore, it is cost‑efficient to keep prompts as short as possible while still meeting requirements. Output tokens up to 128,000 are billed as well. For very long outputs, consider truncating or using multiple iterations.
OrcaRouter may offer caching mechanisms to avoid reprocessing identical prompt prefixes, but this is not confirmed in the provided facts. If enabled, prompt caching can reduce latency and cost for repeated queries. Check OrcaRouter documentation for cache policies. Without caching, each unique prompt is charged fully.
Without exact pricing, a direct comparison is not possible. Generally, models with larger context windows and higher benchmark scores command higher per‑token prices. GPT-5.4 is likely more expensive per token than smaller models like GPT-4o or GPT-4.1. Users should evaluate total cost based on expected average prompt and output lengths, and consider whether the performance gains justify the price difference.
Use the OpenAI‑compatible base URL https://api.orcarouter.ai/v1 and set the model parameter to openai/gpt-5.4. Authentication requires an OrcaRouter API key. Example curl request: curl https://api.orcarouter.ai/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"model":"openai/gpt-5.4","messages":[{"role":"user","content":"Hello"}]}'.
The API supports standard chat‑completion parameters: model (string), messages (array of role/content), max_tokens (integer up to 128,000), temperature, top_p, frequency_penalty, presence_penalty, stop, stream (boolean), and n. For multimodal inputs, include message content as an array of objects with type text/image_url/file. Refer to OrcaRouter's API documentation for exact schema.
Yes, because OrcaRouter provides an OpenAI‑compatible API. Replace your existing base URL with https://api.orcarouter.ai/v1 and update the model name to openai/gpt-5.4. Your OpenAI client library (e.g., openai Python package) can be reconfigured by changing the base_url and api_key. Ensure your code handles possible differences in error response formats or rate limits.
The model ID on OrcaRouter is openai/gpt-5.4. This string must be passed in the model field of the request body. It distinguishes GPT-5.4 from other models available through the same API endpoint. Using a wrong ID will result in an error. The provider is openai, but the model is hosted and routed by OrcaRouter.
GPT-5.4 offers a much larger context window (1,050,000 vs. 128,000 tokens) and a higher GPQA Diamond score (92.0 vs. not provided for GPT-4o). GPT-4o supports text and image but not file uploads, and has lower max output (16,384 tokens vs. 128,000). GPT-5.4 is more capable for long‑context and scientific reasoning, but likely more expensive and slower. GPT-4o remains a good choice for shorter, simpler tasks.
Claude 3.5 Sonnet offers 200,000‑token context; GPT-5.4 surpasses that with 1,050,000. However, benchmark comparisons are limited: GPT-5.4 scores 92.0 on GPQA Diamond, while Claude 3.5 Sonnet scores 78.0 (publicly known). No direct comparison with Gemini 2.0 Pro or Llama 3.1 405B is available from provided facts. GPT-5.4 is competitively strong on science reasoning but users should test on their own data.
GPT-5.4 provides a larger context window (1,050,000 vs. Claude's 200,000) and higher max output (128,000 vs. 8,192). On GPQA Diamond, GPT-5.4 scores 92.0; Claude 3.5 Sonnet scores 78.0. This suggests GPT-5.4 may perform better on nuanced scientific document analysis. However, model availability, pricing, and ecosystem integration on OrcaRouter should be considered. For very long documents, GPT-5.4's larger context is advantageous.
Smaller models (e.g., GPT-4o mini, GPT-4.1 nano) have lower cost, faster inference, and smaller context windows. GPT-5.4 trades cost and speed for higher accuracy on complex tasks and the ability to handle massive contexts. Your decision should be based on required performance on high‑stakes questions (like GPQA Diamond) and context length demands. If your task is simple, a smaller model is likely more efficient.
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",
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 | Cache read / 1M |
|---|---|---|---|
| ≤ 272K | $2.50 | $15.00 | $0.250 |
| ≤ ∞ | $5.00 | $22.50 | $0.500 |
| 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-5.4Open @misc{orcarouter_gpt_5_4,
title = {GPT-5.4 API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.4}
}OpenAI. (2026). GPT-5.4 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.4