Technical Specifications of gpt-4-1106-preview
| Specification | Details |
|---|---|
| Model ID | gpt-4-1106-preview |
| Provider | OpenAI |
| Model family | GPT-4 Turbo Preview |
| Release context | Announced as part of OpenAI DevDay updates in November 2023. |
| Primary modality | Text input and text output |
| Context window | Up to 128K context. |
| API compatibility | Usable through chat/completions-style workflows and OpenAI API model routing for GPT family models. |
| Structured output support | Supports JSON mode; function calling features are supported in this model generation family. |
| Notable developer feature | Seed-based reproducible output support was documented for gpt-4-1106-preview in beta. |
| Pricing reference | OpenAI’s legacy pricing page lists gpt-4-1106-preview at $10.00 per 1M input tokens and $30.00 per 1M output tokens. |
| Lifecycle status | Listed by OpenAI among legacy/deprecated GPT model snapshots, so availability may depend on platform compatibility and migration policy. |
What is gpt-4-1106-preview?
gpt-4-1106-preview is OpenAI’s preview snapshot of GPT-4 Turbo, a faster and lower-cost GPT-4 generation introduced for developers during OpenAI DevDay in November 2023. It was positioned as a high-capability text model with a much larger context window than earlier GPT-4 variants, making it suitable for long prompts, document-heavy workflows, coding assistance, structured generation, and conversational AI.
Compared with older GPT-4 API snapshots, this model was notable for combining stronger efficiency with a 128K context window and developer-oriented capabilities such as JSON mode and improved function-calling workflows. Those features made it especially useful for building assistants, extraction pipelines, and tool-using applications that needed more reliable machine-readable output.
Today, gpt-4-1106-preview is best understood as a legacy OpenAI model snapshot that still appears in historical pricing and deprecation materials. In CometAPI, the identifier gpt-4-1106-preview serves as the platform model code to access this model route where supported.
Main features of gpt-4-1106-preview
- 128K long-context processing: Designed to handle very large prompts and multi-document inputs, which is useful for summarization, analysis, retrieval-augmented generation, and long-session conversations.
- GPT-4 Turbo efficiency: OpenAI introduced GPT-4 Turbo as a lower-cost, faster alternative to earlier GPT-4 variants, improving practicality for production workloads.
- JSON mode: Helps developers generate valid JSON-shaped outputs for workflows that depend on structured responses instead of free-form text.
- Function calling support: Well-suited for applications that connect the model to external tools, APIs, or internal business logic.
- Reproducibility controls: OpenAI documented beta support for seed-based output consistency on
gpt-4-1106-preview, which can be helpful in testing and evaluation workflows. - Strong general-purpose capability: Appropriate for advanced chat, coding help, content generation, classification, transformation, and instruction-following use cases associated with GPT-4-class models.
- Legacy snapshot availability: Because OpenAI now classifies this model line as older or legacy, teams integrating it should verify current support and plan for future migration if needed.
How to access and integrate gpt-4-1106-preview
Step 1: Sign Up for API Key
To use gpt-4-1106-preview, first create a CometAPI account and generate an API key from the dashboard. After signing in, store your API key securely in an environment variable such as COMETAPI_API_KEY. This allows your application to authenticate requests safely without hardcoding credentials in source files.
Step 2: Send Requests to gpt-4-1106-preview API
Use CometAPI’s OpenAI-compatible endpoint and set the model field to gpt-4-1106-preview.
curl https://api.cometapi.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_API_KEY" \
-d '{
"model": "gpt-4-1106-preview",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain the main benefits of long-context language models."}
],
"temperature": 0.7
}'
Step 3: Retrieve and Verify Results
The API returns a JSON response containing the model’s generated output, token usage, and other metadata. In production, you should verify the response format, validate structured fields when using JSON-style outputs, and log request IDs or usage statistics for monitoring and debugging. If your workflow depends on deterministic behavior or schema compliance, test prompts carefully and confirm that the returned content matches your application requirements.