OpenAI GPT-5.4 Pro with 1.05M context and 128K output, accessed via OrcaRouter API.
openai/gpt-5.4-pro-2026-03-05 is a large language model from OpenAI, accessed through OrcaRouter's API. It is a version of the GPT-5.4 Pro series released on March 5, 2026. The model supports three…
The model excels at tasks requiring deep understanding of long contexts and multimodal inputs. It can summarize documents of over a million tokens, answer questions based on detailed source material, and generate comprehensive reports. For coding, it can debug, explain, and refactor large codebases. It supports translation, creative writing, and data extraction from files. Its multimodal capability allows it to analyze images (e.g., screenshots, diagrams) and interpret file contents simultaneously, enabling complex workflows like automated invoice processing or scientific paper review.
Best use cases include processing entire legal case files, analyzing multi-hundred-page technical manuals, generating long-form content like books or script drafts, and performing complex reasoning over large datasets. In enterprise settings, it can be used for contract review, compliance checking, and knowledge management where documents are lengthy. Developers benefit from its ability to maintain context across very large codebases for code review, documentation generation, and refactoring. Multimodal tasks such as interpreting charts, medical images, or handwritten notes alongside text are also strong applications.
Choose a cheaper model for short sentence-level tasks, simple classification, or high-throughput scenarios where large context is unnecessary. For single-turn chat, translation of brief text, or basic summarization of short articles, models with smaller context windows (e.g., 128K tokens) offer lower cost and faster response times. Also, if your input is purely text and under 100K tokens, a smaller model may suffice. The 1.05M context window adds computational overhead; using it for tiny prompts is inefficient. Evaluate average input length and task complexity to decide.
The model processes long documents in a single context pass, using its attention mechanism to relate information across the entire window. It can accurately retrieve facts, perform reasoning, and generate coherent summaries even when the relevant details are far apart. For example, it can answer questions linking information from page 1 and page 1000 of a book. However, very long contexts may increase latency and token usage. For optimal performance, structure your prompts clearly and place important information near the beginning or end of the context.
No specific benchmark scores are provided in this catalog entry. However, based on its design as a large-pro model, it is expected to perform well on tasks that benefit from long-context reasoning, such as needle-in-a-haystack retrieval, multi-document QA, and long-form summarization. Its multimodal input allows it to understand and reason about images in context. Earlier versions of GPT have shown strong performance on language understanding and generation benchmarks. This model likely improves on those with the extended context and larger output capacity.
Speed depends on input length, output length, and server load. Models with very large context windows naturally have higher latency per request due to the computational cost of processing many tokens. The maximum output of 128,000 tokens may result in long generation times for full-length outputs. For real-time applications, consider using a smaller model or limiting token counts. OrcaRouter's API may offer streaming responses to reduce time to first token. For detailed latency expectations, refer to OrcaRouter's documentation or perform your own benchmarks with representative inputs.
The model may exhibit reduced performance on very long contexts due to attention dilution, though it is optimized for such use. Multi-hop reasoning across distant parts of a large context can still fail. It is not a search engine and may hallucinate when information is missing. Image understanding may struggle with low-resolution, heavily distorted, or complex diagrams. Output length of 128K tokens is a limit; extremely long generation may require multiple calls. Additionally, cost at high token counts can be substantial. Always validate critical outputs for accuracy.
Compared to earlier GPT models like GPT-4 or GPT-4o, this model offers a dramatically larger context window (1.05M vs typically 128K) and increased max output (128K vs 4K-8K). It also adds file input modality, which earlier models did not support. The exact performance gains on standard benchmarks are not provided, but the larger context enables tasks that were previously infeasible, such as processing entire books without chunking. If you have not moved past GPT-4, this model represents a significant upgrade in capacity.
Pricing details for this model are not provided in this catalog entry. Typically, OpenAI models are billed per token for input and output, with additional charges for image processing. For exact rates, consult OrcaRouter's pricing page or your account agreement. Note that the large context window and high output limit mean that a single request can consume millions of tokens, resulting in higher costs per call compared to smaller models. To manage costs, you can cap max tokens and limit input length to only necessary content.
The main trade-off is between capability and cost. Using a 1.05M context window for short inputs wastes capacity and money. Similarly, generating 128K tokens is expensive; for shorter outputs, reduce the max_tokens parameter. If your task can be accomplished with a smaller model (e.g., GPT-4o-mini), that will be cheaper. However, for tasks that genuinely need the large context, this model may be more cost-effective than chunking data across multiple API calls with a smaller model, as it avoids additional round trips and manual stitching.
Caching policies are not specified in this catalog entry. Some API providers offer prompt caching to reduce costs for repeated prefix tokens. Check OrcaRouter's documentation or contact support to learn if caching is available for this model. If caching is supported, you may save on input tokens by sending duplicate context across multiple requests. If not, consider designing your prompts to avoid redundant data when possible. Always review OrcaRouter's terms of service for the most current information.
To estimate costs, calculate the approximate number of tokens in your input and expected output. You can tokenize text using libraries like tiktoken. For images, a fixed token cost applies (varies by image size; consult OrcaRouter documentation). Multiply token counts by the per-token price (input, output, and image) and sum. Use test calls with representative data to refine estimates. Since this model's pricing is not provided, you must obtain the rate card separately. Always monitor usage via OrcaRouter's dashboard to avoid surprises.
You call the model via OrcaRouter's OpenAI-compatible API. The base URL is https://api.orcarouter.ai/v1. Use the model ID "openai/gpt-5.4-pro-2026-03-05" in your requests. Authenticate with an API key provided by OrcaRouter. The endpoint is /chat/completions for chat-style interactions. Example Python code: openai.ChatCompletion.create(model="openai/gpt-5.4-pro-2026-03-05", messages=[...], max_tokens=128000). The API supports standard parameters. Ensure your client uses the OrcaRouter base URL and your API key.
Standard OpenAI chat completion parameters are supported: model (required), messages (array of objects with role and content), max_tokens (up to 128000), temperature (0-2, default 1), top_p, n, stop, presence_penalty, frequency_penalty, logit_bias, user, stream (boolean for streaming), and response_format (e.g., json_object). For multimodal inputs, include image parts in the content with type "image_url" or file attachments as per OrcaRouter's documentation (since file input is non-standard, check specifics). Parameters like functions, tools, and tool_choice may also be available.
To migrate from any OpenAI-compatible API, change your base URL to https://api.orcarouter.ai/v1 and update the model name to "openai/gpt-5.4-pro-2026-03-05". Use your OrcaRouter API key instead of the previous provider's key. All other code (messages structure, parameters) remains identical if you were using OpenAI's SDK. For non-OpenAI APIs, you may need to adjust to the OpenAI request format. Test with a simple request first. OrcaRouter may have different rate limits; review their documentation. For file inputs, ensure your client can send files as base64 or URLs as required.
Base URL: https://api.orcarouter.ai/v1. Model ID: "openai/gpt-5.4-pro-2026-03-05". You must include the exact model ID string in each request. The base URL points to the v1 API endpoint that implements the OpenAI schema. Use these values in your code regardless of the programming language. For example, in JavaScript: openai.baseURL = 'https://api.orcarouter.ai/v1'; openai.model = 'openai/gpt-5.4-pro-2026-03-05'. Ensure no trailing slashes or extra characters.
GPT-4o has a context window of 128K tokens and max output of 16K tokens (approx). This model offers 8x more context and 8x more output. GPT-4o also supports multimodal inputs (text, image, audio in some versions) but lacks the file input modality. This model includes file support. In terms of capability, GPT-4o is sufficient for most tasks under 100K tokens. If your work requires processing extremely long documents or files, this model is a clear upgrade. For short tasks, GPT-4o may be more cost-effective.
Claude 3.5 Sonnet by Anthropic has a context window of 200K tokens and max output of 8K tokens. This model surpasses it in both metrics (1.05M context, 128K output). Claude also supports multimodal inputs (text, image) but not file input. Claude is known for strong instruction following and safety features. For very long-context tasks, this model may outperform Claude. However, Claude may be a better choice if your priority is cost or if you need a smaller model with lower latency. Both models are available via OrcaRouter.
Gemini 1.5 Pro by Google offers a context window of up to 1 million tokens (comparable) and max output of 8K tokens. This model has a slight edge in context (1.05M vs 1M) and a much larger output (128K vs 8K). Gemini also supports multimodal inputs (text, image, audio, video) and file input, but video is not supported by this model. Gemini may excel in tasks involving audio or video. For pure text, image, and file processing with very long context and output, this model is competitive.
Choose this model when you require the largest available context window (1.05M tokens) and the largest output capacity (128K tokens) in a single API call. It is particularly advantageous for tasks like summarizing entire book series, analyzing complete legal archives, or generating exhaustive reports. If your input includes files (e.g., PDFs, spreadsheets) alongside text and images, this model supports all three. For simpler tasks, alternatives like GPT-4o-mini, Claude Haiku, or Gemini Flash offer lower cost and faster response; select based on trade-offs.
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-2026-03-05",
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-pro-2026-03-05Open @misc{orcarouter_gpt_5_4_pro_2026_03_05,
title = {openai/gpt-5.4-pro-2026-03-05 API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.4-pro-2026-03-05}
}openai. (n.d.). openai/gpt-5.4-pro-2026-03-05 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.4-pro-2026-03-05