How much does GPT-5.6 Cyber cost per million tokens?
As of August 20, 2026, GPT-5.6 Cyber OpenAI API pricing is $12.50 per million input tokens and $75.00 per million output tokens. Input tokens are the prompt you send; output tokens are the completion. Long answers therefore cost more than short ones even when the prompt stays the same. Source: https://developers.openai.com/api/docs/pricing
What is the blended API cost for GPT-5.6 Cyber?
On this page the blended GPT-5.6 Cyber API cost is $87.50 for 1 million input tokens plus 1 million output tokens. That 1:1 mix is the comparison scale used across the LLM pricing table. A 3:1 mix (three million input tokens per one million output tokens), which some calculators use, would be about $112.50.
How much does a typical GPT-5.6 Cyber request cost?
A typical GPT-5.6 Cyber API request with 100K input tokens and 20K output tokens costs about $2.75 at current rates as of August 20, 2026. Multiply that by daily or monthly volume to estimate spend. Caching, batch APIs, and volume discounts from OpenAI can bring the real invoice lower than the public list rate at https://developers.openai.com/api/docs/pricing.
What is the context window for GPT-5.6 Cyber?
GPT-5.6 Cyber supports a 400K token context window and can generate up to 128K output tokens per request. The context window is the total prompt plus conversation history the model can see in one call. Larger windows cost more when you fill them, because every input token is billed per million tokens.
Is GPT-5.6 Cyber open source?
No. GPT-5.6 Cyber is a proprietary model by OpenAI. The only public way to call it is OpenAI's API, billed per million input and output tokens. There is no self-host option for the weights.
What benchmarks does GPT-5.6 Cyber perform well on?
GPT-5.6 Cyber currently reports Advanced Cybersecurity Completion Rate 95%. See the performance table for the full eval set. Use those scores with the token prices above when you are choosing between a cheaper LLM and a frontier model. GPQA, SWE-bench, and HLE are the evals people usually compare first.
What is the knowledge cutoff for GPT-5.6 Cyber?
GPT-5.6 Cyber has a published knowledge cutoff of 2026-02-16. Facts after that date are not guaranteed to be in the weights. For current events you still need retrieval or tools, which adds input tokens and therefore API cost per million tokens.
How do I compare GPT-5.6 Cyber with another LLM?
Use the Compare picker on this page to start an LLM comparison. Search by model or provider, pick a second model, and AnotherWrapper opens a dedicated GPT-5.6 Cyber vs page with per-million-token prices, blended 1M-in / 1M-out cost, context window, throughput, and benchmarks. You can also open any model from the sidebar, or go back to the LLM pricing table to sort the full index.
What are the cheapest alternatives to GPT-5.6 Cyber?
The cheapest LLM alternative depends on whether you optimize for blended token cost, coding evals, or context window. Start with the popular matchups on this page, then sort the LLM pricing table by blended cost to see which hosted APIs undercut GPT-5.6 Cyber. Open-weight models can be cheaper still if you self-host, but that is not the same as OpenAI API pricing on this page.
How do I use GPT-5.6 Cyber in my application?
GPT-5.6 Cyber is called through OpenAI's API with an API key. Bill against input and output tokens separately; cache or shorten prompts if spend is high. If you are shipping a full product, AnotherWrapper includes pre-built API routes and templates for OpenAI and 5+ other providers with auth, payments, and deployment already configured.
How accurate is this GPT-5.6 Cyber pricing data?
This GPT-5.6 Cyber page tracks the latest published OpenAI API pricing from OpenAI, quoted per million tokens. List rates can differ from invoices because of prompt caching, batch discounts, committed-use tiers, and private contracts. Official rate sheet: https://developers.openai.com/api/docs/pricing. Benchmark scores come from official publications and independent evals, not from a private lab run by AnotherWrapper.