GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...
GPT-5.5 is a language model from OpenAI designed for extended output and multimodal input. It accepts files, images, and text, and can generate up to 128,000 tokens in a single completion. This large…
When provided with a file, GPT-5.5 can read and analyze its content. This includes extracting information from documents, summarizing their contents, answering questions about the material, or transforming the data into a different format. For instance, you could upload a multi-page PDF and ask for a structured summary or specific data points. The model processes the file as part of its input, combining it with any accompanying text or images. This capability is useful for automating document review, data extraction, and report generation from files.
GPT-5.5 can interpret images provided as input. This includes recognizing objects, reading text from images, describing scenes, analyzing charts, and answering questions about visual content. For example, you could provide a graph image and ask for a written interpretation of the trends, or a photo of a whiteboard and request the action items. The model processes the image alongside any text and file inputs, enabling multimodal reasoning tasks that combine visual and textual information.
While GPT-5.5 offers large output and multimodal input, it may be overkill for simple tasks. If your use case involves only short text generation (e.g., email replies, short code snippets, classification) without the need for file or image input, a smaller, cheaper model like OpenAI’s GPT-4o mini (if available) could be more cost-effective. Similarly, if you do not require 128k-token output, models with smaller output limits may suffice. Evaluate your typical input-output sizes and modality needs before selecting GPT-5.5 to avoid paying for capacity you won't use.
The model’s score of 93.9 on τ²-Bench suggests strong performance in agentic tasks that involve using tools, following multi-step instructions, and maintaining consistency over several actions. This includes scenarios like web browsing, API calling, database queries, or executing code in a loop. The large output capacity also allows the model to produce long chains of tool calls or reasoning steps without truncation. However, benchmark scores reflect performance on specific test sets; real-world agentic performance may vary depending on the complexity of tasks and the quality of tools available.
τ²-Bench is a benchmark designed to evaluate an agent’s ability to perform complex, multi-step tasks using tools. It tests how well a model can follow instructions, plan actions, use external tools (like search engines, code interpreters, or APIs), and correct errors across several steps. The score of 93.9 achieved by GPT-5.5 places it at a high-performance level on this benchmark, indicating that the model can reliably execute agentic workflows with minimal failure. This is one data point; other benchmarks may show different strengths or weaknesses.
The τ²-Bench score of 93.9 suggests GPT-5.5 is particularly strong at tasks requiring sustained reasoning and tool-use over multiple turns. The benchmark rewards models that can correctly interpret instructions, select the right tools, and recover from mistakes. Therefore, GPT-5.5 is likely well-suited for building autonomous agents that need to complete tasks with limited human intervention. Additionally, the model’s large output headroom may help in scenarios where intermediate reasoning steps are long. However, benchmarks are narrow; other capabilities like creative writing or common sense may not be directly measured by τ²-Bench.
The provided data only includes a single benchmark score (τ²-Bench) and does not specify performance on other common benchmarks like MMLU, HumanEval, or GSM8K. Therefore, general knowledge, mathematical reasoning, and code generation capabilities are not quantified here. The model’s input context length is also not stated, so you may need to test for your specific use case. Additionally, the model may handle images and files, but no latency figures or error rates are given. Users should evaluate GPT-5.5 on their own tasks to understand its practical limitations.
The provided data does not include latency or speed figures for GPT-5.5. As a large model with 128k-token output capability, response times will depend on several factors: the length of both input and output, the complexity of the request, and current server load on OrcaRouter. For short prompts and small outputs, latency may be reasonable; for max-length outputs, it could be significantly longer. To estimate speed, you can benchmark the model using OrcaRouter’s API with typical payloads for your use case. No quality-of-service guarantees are mentioned in the available information.
Pricing details for GPT-5.5 are not included in the provided data. On OrcaRouter, charges are typically based on the number of input and output tokens, with separate rates for text, image, and file processing. For exact pricing, consult OrcaRouter’s pricing page or contact their support. Note that large output tokens (up to 128k) can accumulate significant cost for long generations, so it is advisable to set an appropriate `max_tokens` parameter in your API calls to control costs.
Without specific pricing, general trade-offs apply. Models with larger output capacity and multimodal input tend to cost more per token than simpler alternatives. For tasks that do not require the full 128k output or multimodal inputs, a smaller model may be more economical. Additionally, processing images and files incurs extra cost, often calculated by the size or number of images. If your pipeline mostly uses text, consider whether file/image capabilities justify the higher price. Caching or batch processing can reduce costs; inquire with OrcaRouter about any available discounts for repeated content.
The provided information does not specify any caching mechanisms for GPT-5.5 on OrcaRouter. Caching can reduce costs by reusing previously computed results for identical or similar requests. Some API providers offer automatic caching for frequently used prompts. To determine if caching is available, check OrcaRouter’s documentation or contact their support. If caching is not available, you might implement your own cache at the application level to avoid redundant calls, especially for common queries or static inputs.
To estimate the cost, you need the per-token rates for input and output, plus any surcharges for images or files. As rates are not provided, you can start by making a few test calls with representative payloads and checking the billed amount in OrcaRouter’s usage dashboard. For text-only calls, multiply the input token count and output token count by the respective rates. For multimodal calls, count images and file sizes according to OrcaRouter’s pricing rules. Keep in mind that the 128k max output can lead to high token usage; setting a lower `max_tokens` limit can prevent unexpected bills.
You can call GPT-5.5 through OrcaRouter’s OpenAI-compatible API. The base URL is https://api.orcarouter.ai/v1. Use the model ID "openai/gpt-5.5" in your requests. Any standard OpenAI client library (Python, JavaScript, etc.) can be used by simply changing the base URL and API key. Example using Python’s openai library: set `openai.api_base = "https://api.orcarouter.ai/v1"` and `openai.api_key = "your-key"`. Then call `openai.ChatCompletion.create(model="openai/gpt-5.5", messages=[...])`.
Because OrcaRouter exposes an OpenAI-compatible endpoint, you can use most standard parameters from the OpenAI Chat Completion API. These include `temperature`, `max_tokens`, `top_p`, `frequency_penalty`, `presence_penalty`, `stop`, and `n`. For multimodal inputs, you can include image or file content in the messages array as per the OpenAI vision API format (using `content` with type `image_url` or `file`). The `max_tokens` parameter controls the output length up to 128,000. Be aware that setting `max_tokens` too high may increase latency and cost.
Migration is straightforward thanks to API compatibility. Replace your OpenAI base URL with OrcaRouter’s (https://api.orcarouter.ai/v1) and update your API key. Change the model name from `gpt-5.5` (or whatever you used on OpenAI) to `openai/gpt-5.5`. Most code changes are limited to configuration. If you were using any OpenAI-specific features not supported by OrcaRouter (e.g., some streaming or function calling variations), test them to ensure compatibility. The same messages format, including multimodal content, works as expected.
Streaming is supported through OrcaRouter’s OpenAI-compatible API. Set `stream=True` in your request to receive tokens incrementally. This can improve perceived latency for long outputs and allow you to process text as it arrives. For large outputs (up to 128k tokens), streaming can be critical to avoid timeouts. Note that streaming may affect cost calculation the same way as non-streaming (you pay for all tokens generated). Ensure your client can handle streaming responses, especially for multimodal inputs where the first event may be delayed while the model processes images.
Compared to earlier models like GPT-4, GPT-4 Turbo, or GPT-4o, GPT-5.5 offers a larger maximum output (128k vs. typically 4k-32k on older variants) and multimodal input capabilities (file, image, text). The τ²-Bench score of 93.9 suggests improved agentic performance. However, the exact architecture and training data differences are not public. Older models may be faster or cheaper for simpler tasks. For pure text tasks that don’t need large output or multimodal input, a smaller model could be more efficient.
Direct comparison is limited without benchmark data on the same tasks. Many competitors (e.g., Anthropic Claude, Google Gemini) also support multimodal input and have large context windows. The τ²-Bench score of 93.9 for GPT-5.5 is one indicator of agentic performance; other models may have similar or different scores. GPT-5.5’s 128k max output is relatively high. When choosing between providers, consider factors like pricing on OrcaRouter, API stability, specific modality support (file formats), and your application’s latency requirements.
Open-source models (e.g., Llama 3, Mistral) are often cheaper and offer full control over deployment but may lack the same level of multimodal integration or agentic performance. GPT-5.5, accessed via OrcaRouter, is a closed commercial model with API pricing. Its 128k output capacity and image/file input are features that open-source models may not match without additional infrastructure. For sensitive data requiring local deployment, open-source might be preferable. For convenience and broad capabilities out-of-the-box, GPT-5.5 on OrcaRouter is a strong option.
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-5.5",
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 | $5.00 | $30.00 | $0.500 |
| ≤ ∞ | $10.00 | $45.00 | $1.00 |
| 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-5.5Open @misc{orcarouter_gpt_5_5,
title = {GPT-5.5 API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.5}
}OpenAI. (2026). GPT-5.5 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.5