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OpenAI released GPT-4.1, GPT-4.1 mini and GPT-4.1 nano through its API on April 14, 2025. The family emphasized coding, instruction following and a context window of up to about one million tokens. GPT-4.1 later appeared in ChatGPT for some users, but OpenAI retired it from ChatGPT on February 13, 2026. The API is a separate product: OpenAI’s developer documentation still lists GPT-4.1, though developers should check current access and retirement notices before relying on it.
What OpenAI released
GPT-4.1 is a family of three non-reasoning models aimed at different cost and capability needs:
- GPT-4.1: the family’s highest-capability option, intended for demanding coding, instruction-following and long-context work.
- GPT-4.1 mini: a less expensive, lower-latency choice for routine workloads and higher request volumes.
- GPT-4.1 nano: the fastest and cheapest variant, suited to simple, repetitive tasks where its lower capability is sufficient.
OpenAI introduced the family on April 14, 2025. It initially described the models as API-only, while saying that many GPT-4.1 improvements would be incorporated into GPT-4o in ChatGPT. That launch-era status changed later; API availability and ChatGPT availability should not be treated as the same thing.
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OpenAI later made GPT-4.1 selectable in ChatGPT for certain paid and organizational users. It subsequently announced that GPT-4.1 and GPT-4.1 mini would leave ChatGPT on February 13, 2026. That retirement was for ChatGPT; OpenAI said there were no API changes at the time. Its current developer documentation still lists the API model. Access may depend on the account, endpoint and current OpenAI terms, so verify these before building or migrating a production system.
A ChatGPT subscription does not automatically provide API access or pay API usage charges. ChatGPT and the API are separate products with separate billing and access arrangements. For historical plan availability, OpenAI’s ChatGPT release notes document changes over time.
What GPT-4.1 was designed to improve
Coding
OpenAI reported a 54.6% result on SWE-bench Verified, which it described as 21.4 percentage points above GPT-4o and 26.6 points above GPT-4.5. These are OpenAI-reported benchmark comparisons, not independent measurements or a guarantee that the model will solve a particular repository’s issues.
The intended practical gains included repository-level changes, following implementation requirements, web development and multi-step or multi-file coding tasks. Results in real projects still depend on the codebase, task definition, tools, tests and human review. A benchmark score should not be read as proof of dependable, unsupervised software development.
Instruction following and tools
OpenAI positioned GPT-4.1 as better than GPT-4o at carrying out detailed instructions, including prompts with multiple constraints and required formats. That can help an application produce structured outputs or follow a defined sequence of actions. It does not eliminate ambiguity: contradictory directions, underspecified requests or lengthy prompts can still cause errors.
GPT-4.1 can be used with API tool-calling and orchestration features, but it is not an autonomous-agent product by itself. A production system still needs well-defined tool schemas, application-side control flow, permission limits, state management, evaluation and monitoring. A model making a tool call does not make that call safe or correct.
Long context and image input
At launch, OpenAI advertised a one-million-token context window. Current developer documentation specifies 1,047,576 tokens of context and a 32,768-token maximum output. A large context makes it possible to supply extensive material, such as code or multiple documents, but it does not ensure that every detail will be noticed or recalled accurately. Carefully selecting, retrieving and structuring relevant information can still improve results; sending more text also increases input-token use.
The current model page lists text and image input with text output. It does not list audio or video support. It also gives a June 1, 2024 knowledge cutoff, so applications that need current facts should supply up-to-date material through retrieval or another appropriate system rather than assuming the model knows later events.
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GPT-4.1 compared with GPT-4o and GPT-4.5
GPT-4.1 was not simply a replacement for either model. OpenAI’s launch framing emphasized coding and instruction following, with lower cost and latency than GPT-4.5 for many workloads. It said GPT-4.1 performed better than GPT-4o on several coding and instruction-following tasks, and that median queries were 26% less expensive than GPT-4o. These are company comparisons and depend on the measured task and usage pattern.
| Model | How to think about it | Important qualification |
|---|---|---|
| GPT-4.1 | Coding, strict instructions, tool use and large text/image inputs where a non-reasoning model is appropriate. | Not designed as a reasoning model; its strengths do not make it best for every task. |
| GPT-4o | A separate model that OpenAI said would receive many GPT-4.1 improvements in ChatGPT. | That statement did not mean GPT-4.1 and GPT-4o were the same model or that one universally replaced the other. |
| GPT-4.5 | OpenAI associated it with qualities such as creativity, writing, humor and nuance. | GPT-4.1 was positioned as a more cost- and latency-efficient production choice for many tasks, not as an across-the-board improvement. |
For difficult mathematics, deliberate multi-step analysis or complex research synthesis, a reasoning-oriented model may be a better fit. OpenAI’s current GPT-4.1 documentation recommends starting with GPT-5 for complex tasks. Model choice should be tested against the actual prompts, tools, latency limits and costs of the application.
Original API pricing and cost considerations
At launch, the listed standard rates per one million tokens were:
| Model | Input | Cached input | Output |
|---|---|---|---|
| GPT-4.1 | $2.00 | $0.50 | $8.00 |
| GPT-4.1 mini | $0.40 | $0.10 | $1.60 |
| GPT-4.1 nano | $0.10 | $0.025 | $0.40 |
OpenAI said Batch API use received an additional 50% discount. Batch is intended for asynchronous jobs, not work that needs an immediate response. The current GPT-4.1 model page lists standard rates of $2 per million input tokens and $8 per million output tokens; check the live model documentation for current pricing and terms.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →These are API token rates, not ChatGPT subscription prices. Actual API spend also depends on input and output volume, repeated or cached context, batch eligibility, tool charges where applicable, retries and evaluation traffic. A large context window is a capability ceiling, not a promise of low cost: sending a million tokens can be expensive even when each token is relatively inexpensive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Using GPT-4.1 in an API application
The documented dated model identifier is gpt-4.1-2025-04-14. OpenAI lists support for the Chat Completions, Responses and Realtime API endpoints. Confirm the identifier and endpoint supported by your account before deployment; do not assume an alias or a different model snapshot behaves identically.
Developers can prototype prompts and tool flows in the OpenAI Playground, then integrate through the OpenAI API platform. For a mature application, test outputs on representative inputs, track prompt and model versions, set usage controls and keep a fallback or migration path. GPT-4.1’s ChatGPT retirement does not establish an API retirement, but it is a reminder that model availability can change.
Who should consider it now?
- GPT-4.1: consider it when coding quality, instruction adherence, tool use or long context matters, and a non-reasoning model meets the task’s needs.
- GPT-4.1 mini: consider it for routine extraction, classification, routing, rewriting or code assistance where throughput and cost matter more than maximum capability.
- GPT-4.1 nano: consider it for simple, repetitive operations where low cost and latency are priorities and the application can tolerate reduced capability.
- A newer reasoning model: compare one when the work involves difficult analysis, complex planning or a new long-lived system for which model longevity matters.
Before committing, compare representative tasks rather than relying only on benchmark claims. Include failure rates, latency, full request cost, tool behavior and the effort required to migrate if the model changes. For long-running production systems, regression tests, monitoring and a documented fallback are as important as the initial model choice.
Bottom line
GPT-4.1 was OpenAI’s April 2025 API family for coding, instruction-following and large-context workloads, offered in full, mini and nano versions. It later reached some ChatGPT users but is no longer available there following its February 2026 retirement. The API remains documented as of August 2026, making it a possible specialized or legacy choice—not an automatic default for every new project. Check the live API documentation and compare newer models for the workload before deciding.
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