ORBiS TREE · ENGLISH DOCUMENT
Organizations →OpenAI
An artificial intelligence research and deployment organization known for GPT models and ChatGPT.
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English
This English page is a curated translation layer linked to the Korean source article. Community changes are currently made on the Korean source, where the full revision history and anonymous edit trail are preserved.
Overview
OpenAI is an AI research and deployment organization. Its stated mission is to ensure that artificial general intelligence benefits humanity broadly.[1]
Research and products
Its portfolio includes the GPT model family and the conversational service ChatGPT. Its activities connect model research, practical use and safe deployment.[1]
Mission and reading context
The OpenAI Charter sets out broad benefit, long-term safety, technical leadership and cooperation.[2] Structure, leadership, products and available models can change, so inspect current first-party materials when assessing the present organization.
Separate the organization, model and product
OpenAI names the organization; GPT names a model family; ChatGPT names a service. The official description checked on September 26, 2026 stated that the OpenAI Foundation controls OpenAI Group, a Public Benefit Corporation.[1] This is a dated organizational record, separate from a product's access conditions.
The September 2026 model announcement
On September 22, 2026, OpenAI introduced GPT-6 Sol and Luna alongside Astra, discussing trade-offs in task difficulty, speed and cost.[3] Compare tools, inference settings, task completion, latency and cost rather than infer every result from a family name. Continue with inference, latency and throughput and agents.
Safety assessment and external verification
The September 22, 2026 third-party-assessment principles distinguish safety claims from their evidence. They discuss safety cases, safeguards, dangerous-capability/alignment evaluations and major alignment incidents, alongside independence, rigor, security and responsibilities.[4]
Inspect tested conditions, disclosed evidence and limits through safety evaluation and model cards. A first-party publication records the company's position and proposals; it does not establish that every claim has already been independently demonstrated.[4]
Record model names, snapshots and services together
Record OpenAI as the organization, GPT as a family, the actual request's model identifier and the service or API separately. Inspect input/output and feature support in the model directory ↗.[5] The API compatibility guidance ↗ recommends pinned model versions and application evaluations.[6] Pinning does not guarantee identical wording on every run.
For a fictional document-classification comparison, retain identifier, test date, prompt, documents, output format and tool configuration. When an alias target or generation changes, keep the runs distinguishable. Separate model families, conversational services and model information to make findings reviewable.
Worked example: cost per successful task
These are fictional rates and outcomes, not actual prices or model results. Method A handles 100 tasks: 90 succeed initially and five more after retries, using 110 calls. At 0.2 illustrative cost units per call, total cost is 22 and cost per successful task is 22 / 95, approximately 0.232. Method B has 92 initial and four additional successes with 108 calls. At 0.3 units each, total is 32.4 and cost per successful task is 32.4 / 96 = 0.3375.
This assumes equal call costs and the same success criterion. Real comparisons include failed calls, tools, review time and input/output/cache usage, with quality and completion deadlines. Task counts differ from call counts. A single inference price or speed does not establish workflow economics.
The boundary between a tool-call proposal and execution
The function-calling guide ↗ connects model call outputs with functions executed by an application.[7] Proposing to save a draft differs from saving it on a server. Generated paths or document identifiers do not confer access rights.
Separate read, draft-save and publication permissions; validate arguments, targets and results. Check duplicate execution during retries. This is an ORBiS Tree design example, not an assertion about a product's default protections. Record execution authority and checks through oversight and the system configuration.
Connect earlier announcements to present availability
The September 2026 announcement above is historical context. Check the model directory ↗ and deprecation notices ↗ for present use.[5][8] Distinguish notice dates, shutdown dates, replacements and feature differences. A model name in an old example does not establish current access.
Before changing versions, preserve representative legitimate inputs, unanswerable questions, format constraints and tool cases for reevaluation. Check risk and legitimate completion separately. Mark untested languages or tools unconfirmed. A company's published principles and evidence for your service configuration are distinct records requiring support.
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