GPT-4 Turbo arrived at OpenAI DevDay on November 6, 2023. Its 128,000-token context announcement mattered for document-heavy apps, but the initial developer release was a preview. Retaining that preview designation prevents a common migration error: assuming the first Turbo ID, later stable Turbo IDs and current GPT-4 products are exactly the same model.
Historical research cutoff: 25 October 2026. Primary-source historical claims are date-scoped; cloud entitlements, region-specific availability, current pricing and rate limits are not implied.
Release identity at a glance
- Provider
- OpenAI
- Model family
- GPT-4 Turbo
- Public launch
- 2023-11-06
- API ID / checkpoint
- gpt-4-1106-preview
- API retirement status
- Not confirmed as retired at the research cutoff
The release and why it mattered
The DevDay announcement bundled a model revision with new platform tools. The preview introduced lower API pricing, longer context and upgraded knowledge relative to original GPT-4. Pricing was explicitly dated to that event; it is not restated here as current. The same article documents a correction about automatic GPT-3.5 Turbo upgrades, illustrating why edited release notes require careful source tracking.
Capabilities and genuine use cases
A long context can simplify retrieval for contracts, repositories or multi-document comparison, but capacity is not equivalent to complete recall. Retrieval quality declines when distractors, tables and source ambiguity fill a prompt. Developers should measure extraction recall at different document positions and retain page pointers before sending results to downstream analysts.
Integration and version identity
For an archived application, locate the exact `gpt-4-1106-preview` string and inspect stop conditions, JSON handling and any vision-specific route separately. The model name contains the 2023-11-06 date marker; this is a preview label, not a guaranteed immutable legal contract. The API changelog and deprecation schedule are needed before attempting any replay today.
Limits and evidence gaps
An API customer should not infer that the consumer ChatGPT interface exposed identical snapshot access. Neither a 128K context statement nor higher throughput guarantees low latency on a customer’s own workload. Pricing, geography, cloud partner rollouts and current retirement dates can change independently of the original launch blog.
How to choose or migrate
Use GPT-4 Turbo as a historical comparison point for how context windows and structured developer interfaces matured. To select a current long-context model, test relevant retrieval tasks with pinned IDs and record actual context truncation, answer completeness and per-request cost using dated AI 10 inputs.
Reproducible evaluation checklist
Build three distinct test sets: the regular path, an adversarially ambiguous path, and a regression set drawn from failures. Hold prompts and tool access constant while comparing snapshots. Never use a consumer product announcement to fill an API region or account-entitlement field.
If the historical route is retired, keep the example as an archived request only. For active migration, identify a current model from the provider catalogue and perform a staging replay before changing production configuration.
Sources & Further Reading
- openai.com — documentation/reference (supplier-provided reference; not independently checked in this pass)
- developers.openai.com — documentation/reference (supplier-provided reference; not independently checked in this pass)
- developers.openai.com — documentation/reference (supplier-provided reference; not independently checked in this pass)
Source documentation establishes vendor-published facts, not independently measured model quality, tenant access, contractual rights or current prices. Use a dated pricing ledger and an account-specific entitlement check for deployment decisions.