OpenAI GPT-5.5 (2026-04-23): 128K output tokens, multimodal input, τ²-Bench 93.9
openai/gpt-5.5-2026-04-23 is a language model trained by OpenAI and offered through OrcaRouter. It accepts file, image, and text inputs and can generate up to 128,000 tokens in a single completion.…
The model is well-suited for tasks that combine multimodal input with extensive output. Examples include: generating a financial report from uploaded spreadsheets (file) and charts (image); writing an analysis of a photograph combined with a text prompt; producing long-form code from architectural diagrams; and creating detailed scientific explanations that reference figures. Its high τ²-Bench score suggests it excels at reasoning over long contexts, making it a good choice for multi-step logical problems, narrative generation, and complex summarization. The model maintains coherence over very long outputs, which is a key advantage over models with smaller output windows.
For simple tasks that do not require multimodal input or extremely long output, a smaller or older model may be more cost-effective. For example, if your use case involves short Q&A or basic text generation, models like OpenAI's GPT-4o-mini or GPT-3.5 Turbo might suffice. Additionally, if you do not need file or image input, a text-only model could reduce latency and cost. The 128K output capacity is best utilized when you actually need that length; for shorter outputs, you are paying for unused capacity potential. OrcaRouter offers a range of models, so you can select the cheapest option that meets your task requirements.
To use multimodal input, you send a request to OrcaRouter's API with the model ID "openai/gpt-5.5-2026-04-23" and include content blocks for each modality. Text is provided as standard string content. Images can be passed as URLs or base64-encoded data. Files are uploaded via the API and referenced by ID. The model processes all modalities together, allowing it to reason across them. For example, you could ask the model to read a PDF (file), look at an infographic (image), and then answer questions based on both. The model returns a text completion up to 128,000 tokens.
τ²-Bench is a benchmark designed to evaluate long-context reasoning capabilities. A score of 93.9 places this model among the top performers for tasks that require maintaining logical consistency over extended sequences. It likely reflects the model's ability to handle multi-step reasoning, avoid error accumulation, and accurately process information across large outputs. While the exact methodology of τ²-Bench is not detailed here, a high score suggests that the model is reliable for complex analytical work. Users should consider this benchmark alongside other metrics relevant to their specific use case.
Speed and latency depend on several factors including input size, output length, and current API load. Generating 128K tokens will naturally take longer than generating a short response. OrcaRouter provides streamed responses to handle long outputs efficiently. Typical latencies for this model are not published, but you can expect that larger outputs will require more time. For real-time applications, consider using a shorter model or setting reasonable max_tokens limits. Testing with your specific workload is recommended to understand latency characteristics. OrcaRouter's infrastructure helps reduce variability, but network and processing overhead still apply.
Strengths include high output capacity (128K tokens), multimodal input support, and strong long-context reasoning as measured by τ²-Bench. The model is likely to perform well on tasks that require generating long, coherent content from diverse inputs. Limitations may include higher cost compared to smaller models and potential latency for very long outputs. Additionally, while it handles image and file inputs, its performance on specific visual tasks (e.g., fine-grained object recognition) may not match specialized vision models. The model's knowledge cutoff is implied by its version date (2026-04-23), meaning it cannot know about events after that date. Users should verify facts for critical applications.
Pricing for openai/gpt-5.5-2026-04-23 is based on token usage, with separate rates for input and output tokens. OrcaRouter charges per million tokens, and the exact rates are available on the OrcaRouter pricing page. Given the model's large output limit (128K tokens), generating long completions will naturally incur higher output costs. There may also be surcharges for file or image processing. It is important to monitor token consumption to control expenses. OrcaRouter provides usage analytics to help track costs. There are no fixed monthly fees; you pay only for what you use.
The primary trade-off is between the model's advanced capabilities and its cost. For tasks that genuinely require 128K output and multimodal input, this model can be more efficient than alternative approaches (e.g., multiple calls to a smaller model). However, for shorter or simpler tasks, using a cheaper model (like GPT-4o-mini or GPT-3.5 Turbo) will reduce costs. Additionally, you can limit output tokens to a lower number to avoid paying for unused capacity. OrcaRouter does not charge for failed requests or caching (where applicable), but you should review the specific pricing policy for this model.
OrcaRouter may implement caching mechanisms that reduce costs for repeated identical prompts. However, caching behavior for this model is not explicitly documented here. Typically, read-only operations on cached responses can lower token consumption. If caching is enabled, you might see reduced costs for common queries. Developers should check the OrcaRouter documentation for the latest caching policies. As a best practice, design prompts to be reproducible and consider using external caching strategies if the API does not provide them. Note that dynamic or user-specific prompts may not benefit from caching.
To use the model, set your API endpoint to https://api.orcarouter.ai/v1 and include the model ID "openai/gpt-5.5-2026-04-23" in your request. The API is OpenAI-compatible, so you can use the standard OpenAI SDK or any HTTP client that supports the chat completions endpoint. Pass your API key in the Authorization header. Structure your messages array with role and content. For multimodal inputs, include image_url or file_id properties in the content. Set max_tokens to a value up to 128,000. The response will contain the generated text. Example curl snippet (not provided here) is available in OrcaRouter's documentation.
Key parameters include: model (string, set to "openai/gpt-5.5-2026-04-23"), messages (array of message objects), max_tokens (integer up to 128,000), temperature (float, typically 0-2), top_p (float), n (number of completions), stream (boolean), stop (array of strings), and presence_penalty/frequency_penalty. For multimodal input, use content parts with supports for text, image_url (with detail option), and file_id. The model supports function calling and tool use (if enabled by OpenAI). Refer to OrcaRouter's documentation for the complete list of supported parameters and any model-specific defaults.
Migrating to OrcaRouter's API is straightforward because the API is OpenAI-compatible. Replace your existing base URL with https://api.orcarouter.ai/v1 and change the model ID to "openai/gpt-5.5-2026-04-23". Update your authentication to use an OrcaRouter API key. No changes are needed to the request format if you are already using OpenAI's chat completions structure. Ensure your system handles potential differences in rate limits and latency. OrcaRouter provides migration guides and support. Test with a few requests before switching production traffic.
Both are OpenAI models, but gpt-5.5-2026-04-23 offers a significantly larger output limit (128K tokens versus GPT-4o's 4,096 tokens) and supports file and image input (GPT-4o also supports vision, but file handling may differ). The τ²-Bench score of 93.9 likely exceeds GPT-4o's performance on long-context reasoning, though direct comparisons are not provided. Pricing is expected to be higher due to the larger output capacity. For short tasks, GPT-4o may be more cost-effective. For tasks requiring very long outputs or multimodal file input, the newer model is better suited.
Claude 3.5 Sonnet (by Anthropic) has a maximum output of 8,192 tokens, far less than 128K. It also supports text and image input but not file uploads in the same manner. On long-context reasoning, Claude models are known for high performance, but specific benchmark comparisons are not available. Pricing for Claude models is per-token as well. The key differentiator is the output capacity; if you need to generate very long content, this OpenAI model is a clear choice. Otherwise, the decision may depend on other factors like safety preferences or ecosystem integration.
Gemini 1.5 Pro (by Google) offers a large context window up to 1 million tokens but output limits are lower (around 8,192 tokens). It supports text, image, audio, and video. The τ²-Bench score for Gemini is not provided, but Google's models generally excel on multimodal tasks. The OpenAI model's strength lies in its high output token limit and strong long-context reasoning score. Choosing between them involves trade-offs: Gemini offers larger input context and additional modalities, while this model provides longer outputs. Both can handle multimodal inputs, but file handling specifics differ.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="openai/gpt-5.5-2026-04-23",
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.
What developers are saying this week
GET /api/public/models/openai/gpt-5.5-2026-04-23Open @misc{orcarouter_gpt_5_5_2026_04_23,
title = {openai/gpt-5.5-2026-04-23 API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.5-2026-04-23}
}openai. (n.d.). openai/gpt-5.5-2026-04-23 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.5-2026-04-23