GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series — the tier built for the hardest work: deep multi-step reasoning, large-scale software engineering, and long-horizon agentic workflows. It is especially strong at command-line and multi-file coding tasks, planning and executing across many tool calls while staying coherent over a 1.05M-token context window, and can emit up to 128K output tokens in a single response. It accepts text, image, and file inputs with text output, and exposes configurable reasoning effort so callers can trade latency and cost against depth per request. As a first-class OpenAI Responses model it plugs directly into agent frameworks, structured-output pipelines, and tool-calling loops. Use Sol when correctness on complex, high-value tasks matters more than cost — production coding agents, research and analysis, and multi-step automation that must not drift.
GPT-5.6 Sol is an AI language model developed by OpenAI. It features a context window of 1,050,000 tokens, allowing it to process extremely long sequences of text, images, and files in a single…
GPT-5.6 Sol is optimized for tasks requiring extensive context and multimodal input. It can analyze entire books, lengthy contracts, or research papers in a single prompt, answering questions at any point. With image support, it can process hundreds of photographs, diagrams, or screenshots simultaneously. File input allows direct handling of PDFs, Word documents, and other formats, extracting information without manual preprocessing. In code, it can review large repositories, understand dependencies, and generate summaries across multiple files. These capabilities make it suitable for deep analysis, cross-document reasoning, and long-form generation. For example, a legal team could input an entire contract suite and ask targeted questions. A researcher could load a book and request chapter-by-chapter analysis. The model maintains coherence across the entire context.
For short prompts, simple Q&A, or tasks that fit within a smaller context window, a cheaper model like GPT-4o or GPT-4o-mini may be more efficient. These models are faster and less expensive per token. GPT-5.6 Sol's large context window comes with higher computational cost. If your task does not require processing tens of thousands of tokens or multimodal inputs, you may see better performance and lower latency with a smaller model. OrcaRouter allows you to switch between models easily, so you can use GPT-5.6 Sol only when the context depth is necessary. Evaluate your average prompt size. If most interactions are under 10,000 tokens, a smaller model will likely suffice. Reserve GPT-5.6 Sol for tasks that truly benefit from its context capacity.
Yes. With a context window of 1,050,000 tokens, GPT-5.6 Sol can ingest large portions of a codebase, including multiple files and dependencies, in a single prompt. This enables tasks like code review, bug detection across files, architectural analysis, and generation of comprehensive documentation. Developers can provide the entire project directory as text or file inputs. The model understands programming languages and can follow complex logic across files. However, very large codebases exceeding the context window may require careful selection of the most relevant files. OrcaRouter's API supports streaming responses for real-time feedback. For example, a team could input their entire application code and ask for a security audit or refactoring suggestions. The model's output can be up to 128,000 tokens, sufficient for detailed reports.
GPT-5.6 Sol accepts file inputs as part of a conversation. Users can upload documents, images, and other file types through the API. The model processes the file content directly, extracting text from PDFs, analyzing images, or reading structured data. This eliminates the need to manually convert files into text before sending. The large context window allows multiple files to be included in the same prompt, enabling cross-file analysis. OrcaRouter's API supports file attachments in the request, following the OpenAI format. File input modalities are particularly useful for auditing, research, and data extraction tasks. Supported file types include commonly used formats such as PDF, PNG, JPEG, and others. The exact list is available in OrcaRouter's documentation.
A context window of 1.05 million tokens allows GPT-5.6 Sol to hold a huge amount of information in its working memory. For comparison, this is roughly equivalent to 700-800 pages of text or several hundred images. This capacity enables the model to reference information from the start of a long document when generating a response at the end, without loss of context. It reduces the need for chunking strategies that are common with smaller models. However, processing such large contexts can be computationally intensive and may result in longer latencies. The maximum output of 128,000 tokens allows for lengthy generated responses, like full reports or code files.
Due to its large context window, GPT-5.6 Sol generally has higher latency than smaller models like GPT-4o or GPT-4o-mini. The time to generate the first token and overall response time scales with the size of the input and output. For short prompts, the difference may be negligible, but for prompts processing hundreds of thousands of tokens, the model may take seconds to respond. OrcaRouter's API provides streaming responses to mitigate perceived latency. Users should benchmark for their specific use case. If speed is critical and context is small, a faster model is recommended. The model's architecture is optimized for throughput on large inputs, but fundamental physics of large models make it slower than smaller alternatives.
The primary strength is its enormous context window, which allows it to process and reason over very long sequences without forgetting earlier information. This is critical for tasks like narrative analysis, long document comprehension, and multi-image reasoning. The multimodal capabilities (text, image, file) make it versatile across different data types. The high output limit (128,000 tokens) enables generation of extensive content. Additionally, being an OpenAI model, it benefits from robust training and alignment. For users who require these capacities, GPT-5.6 Sol offers capabilities not available in smaller or less context-rich models. Its ability to maintain coherence over tens of thousands of tokens is a distinguishing feature that can significantly improve performance on tasks like legal brief analysis or scientific literature review.
The large context window comes with trade-offs. Inference is slower and more expensive per token compared to smaller models. The model may also be overkill for short, simple tasks. Additionally, the model's performance on benchmarks specific to long-context recall isn't publicly available for this version, so users should evaluate empirically. File input quality depends on the file format and structure; images are limited in resolution. As with all large language models, GPT-5.6 Sol can produce incorrect or hallucinated information. Users should verify critical outputs. OrcaRouter's gateway does not change the model's inherent limitations. Context window does not guarantee perfect performance; the model may still lose fine details. For precise numerical tasks, careful prompting is needed.
Pricing for GPT-5.6 Sol is based on token usage, separately for input and output tokens. Exact per-token rates are not publicly listed for this specific model; they are available through OrcaRouter's pricing page or API documentation. Generally, models with larger context windows command a premium due to increased computational resources. OrcaRouter may offer tiered pricing or discounts for high-volume usage. Users are charged for the number of tokens sent in the request (including the context) and the number of tokens generated in the response. To get precise current pricing, refer to OrcaRouter's official sources. Note that file inputs are converted to tokens, so uploading a large image or PDF will increase the input token count accordingly.
Because GPT-5.6 Sol charges per token, a single request with a large context can be significantly more expensive than using a smaller model for the same task. For example, sending 500,000 tokens of input would cost more than sending 10,000 tokens. Therefore, it is essential to estimate the token count of your typical prompts. If your task can be accomplished with a smaller context, you may save costs by using a cheaper model. OrcaRouter allows you to choose between models, so you can route simple queries to smaller, faster models and complex ones to GPT-5.6 Sol. Also consider caching: if you frequently send overlapping prefix tokens, repeated costs can add up.
Caching mechanisms for GPT-5.6 Sol are not explicitly detailed in the provided information. However, OrcaRouter may implement prompt caching or request deduplication to reduce costs for repeated or similar inputs. Developers should consult OrcaRouter's documentation for any available caching features. If caching is available, sending identical prompts multiple times could avoid recomputation costs. Without official confirmation, users should plan for full token billing for each unique request. For predictable costs, consider setting max_tokens limits and monitoring your token usage via OrcaRouter's dashboard.
To use GPT-5.6 Sol, send a POST request to OrcaRouter's OpenAI-compatible endpoint: https://api.orcarouter.ai/v1/chat/completions. Set the model parameter to 'openai/gpt-5.6-sol'. Include your API key in the Authorization header. The request body should follow the standard OpenAI chat completions format: a list of messages with role and content. You can include text, image URLs (data URIs), and file attachments. Example: { "model": "openai/gpt-5.6-sol", "messages": [{"role": "user", "content": "Analyze this document."}], "max_tokens": 1000 }. OrcaRouter handles routing and returns the response in the same format.
The API supports most parameters available in OpenAI's chat completions API. These include: 'temperature' (controls randomness), 'top_p' (nucleus sampling), 'max_tokens' (maximum output length), 'stop' (sequences to stop generation), 'frequency_penalty' and 'presence_penalty', 'stream' (for streaming), and 'user' (for end-user identification). The 'max_tokens' parameter cannot exceed the 128,000 token output limit. For file inputs, you can include file URLs or attachments in the content array. OrcaRouter may also support additional parameters like 'seed' for deterministic outputs; refer to their API documentation for full details. Note that the model's output length is constrained by both max_tokens and the remaining context capacity. Always set max_tokens within the output limit.
Migrating is straightforward because OrcaRouter's API is fully OpenAI-compatible. Simply change the base URL from https://api.openai.com to https://api.orcarouter.ai/v1, and update the model ID from 'gpt-5.6-sol' to 'openai/gpt-5.6-sol'. If you were using an OpenAI SDK (e.g., Python openai package), you can override the base URL and model in your client configuration. For example, in Python: client = OpenAI(base_url='https://api.orcarouter.ai/v1', api_key='your_orcarouter_key'). Then use client.chat.completions.create(model='openai/gpt-5.6-sol', ...). This minimizes code changes. Authentication is handled via an API key provided by OrcaRouter. Ensure your key has the necessary permissions.
OrcaRouter requires an API key for authentication. This key should be included in the HTTP request header as 'Authorization: Bearer YOUR_API_KEY'. The key is obtained by signing up for an OrcaRouter account and creating an API key in the dashboard. OrcaRouter may offer different tiers with rate limits and model access. The same key can be used for all models accessible through OrcaRouter, including GPT-5.6 Sol. Keep your key secure and rotate it periodically. For high-security environments, OrcaRouter may support additional authentication methods; check their documentation. If you encounter authentication errors, verify that the key is correct and has not expired. Contact support for account issues.
GPT-5.6 Sol offers a significantly larger context window (1,050,000 tokens vs. GPT-4o's much smaller typical context). This makes GPT-5.6 Sol better suited for long documents and complex multi-step reasoning. However, GPT-4o is generally faster and cheaper per token. GPT-4o also supports multimodal inputs (text, image) with lower latency. For most day-to-day tasks where context is under 10,000 tokens, GPT-4o may be more cost-effective. GPT-5.6 Sol should be reserved for tasks that truly need its extensive context. Both models are available via OrcaRouter, so you can switch based on need. GPT-4o's maximum output is also lower, so GPT-5.6 Sol wins on output length as well.
Compared to models like Anthropic's Claude with 200K context or Google's Gemini with 1M context, GPT-5.6 Sol's 1.05M is competitive in capacity. Each provider's implementation differs in how they utilize the context—some may be more efficient at retrieval within the window. Benchmark comparisons are not provided here, so users should test empirically. GPT-5.6 Sol benefits from OpenAI's ecosystem and fine-tuning. However, other models may offer different strengths (e.g., code specialization). OrcaRouter aggregates multiple providers, so users can compare by testing different model IDs in the same API format.
Choose GPT-5.6 Sol when your task requires processing a very large amount of information in a single turn—for example, analyzing an entire book, reviewing a huge codebase, or reasoning across hundreds of images. If you need to generate very long outputs (up to 128,000 tokens), this model is suitable. If your task fits within a smaller context and you prioritize speed and cost, consider a cheaper model. OrcaRouter makes it easy to evaluate: start with GPT-5.6 Sol for complex tasks and fall back to smaller models for simpler ones. For applications like legal document review, scientific literature survey, or multi-file code analysis, the large context is a decisive advantage.
Exact pricing details are not disclosed in the provided information, but generally, models with larger context windows command higher per-token rates. GPT-5.6 Sol is likely more expensive per token than smaller models like GPT-4o or GPT-4o-mini. For large input sizes, the total cost per request can be substantial. However, for tasks that would otherwise require multiple API calls and manual chunking, GPT-5.6 Sol might reduce overall cost and complexity. OrcaRouter's pricing page should have a comparison for available models. Users should estimate their monthly token consumption to make an informed choice. If your workload is heavily context-dependent, the potential savings from avoiding chunking and multiple calls may offset the higher per-token cost.
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-5.6-sol",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningreasoning_effortresponse_formatseedstructured_outputstool_choicetools| Tier | Input / 1M tokens | Output / 1M tokens | Cache read / 1M | Cache write / 1M |
|---|---|---|---|---|
| ≤ 32K | $5.00 | $30.00 | $0.500 | $6.25 |
| ≤ ∞ | $10.00 | $45.00 | $1.00 | $12.50 |
| 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-5.6-solOpen @misc{orcarouter_gpt_5_6_sol,
title = {GPT-5.6 Sol API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.6-sol}
}OpenAI. (2026). GPT-5.6 Sol API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.6-sol