OpenAI model
GPT-4.1
A high-capability non-reasoning OpenAI model built for precise instruction following, software development, tool-driven workflows, and long-context applications.
- Context window
- 1.05M
- tokens
- Max output
- 32.8K
- tokens
- Input
- $2.00
- per 1M tokens
- Cached input
- $0.50
- per 1M tokens
- Output
- $8.00
- per 1M tokens
01 / Overview
What GPT-4.1 Is
GPT-4.1 is a non-reasoning OpenAI model designed for developers who need reliable instruction following, strong coding performance, tool use, and unusually large input context without adding a separate reasoning phase to every request.
Built for Practical API Workloads
GPT-4.1 is particularly relevant when an application needs to follow detailed instructions consistently or work across a large amount of source material. Its context window reaches 1,047,576 tokens, making it possible to send substantial codebases, document collections, long conversation histories, or retrieved context in a single request.
Unlike OpenAI reasoning models, GPT-4.1 does not expose a reasoning-effort control. This can be useful for workloads where predictable latency and direct execution matter more than allocating additional inference time to an internal reasoning process.
The model accepts both text and images as input and produces text output. It also supports features commonly required in production APIs, including function calling, structured outputs, streaming, fine-tuning, and predicted outputs.
- Strong fit for coding and software-engineering assistance.
- Designed to follow detailed multi-part instructions accurately.
- Supports long-context applications with more than one million tokens of context.
- Works with structured and tool-oriented application flows.
- Provider
- OpenAI
- Family
- GPT-4.1
- Status
- Active
- Knowledge cutoff
- Jun 1, 2024
- Input modalities
- Text, Image
- Output modality
- Text
- Model type
- Non-reasoning
02 / Use cases
When GPT-4.1 Is a Good Fit
GPT-4.1 is most compelling when your workload benefits from precise instructions, large context, or software-development capability more than from a dedicated reasoning model.
Match the Model to the Shape of the Task
A one-million-token context window is valuable only when the application actually needs to provide substantial source material. For a small classification request, much of that capacity may never be used. For repository analysis, large-document processing, migration work, or support systems with extensive history, the same capacity can remove aggressive truncation and reduce the need to split data across many calls.
GPT-4.1 is also a practical candidate for applications that depend on tool selection or strict output structure. Function calling and structured outputs can turn a natural-language request into predictable data that downstream code can validate and process.
For production selection, the key question is not whether GPT-4.1 performs well in general. The useful question is whether it performs well enough on your exact prompt set, at an acceptable cost and latency.
- Useful for repository-level coding assistance and code transformation.
- Well suited to instruction-heavy API tasks with many constraints.
- Practical for document analysis when relevant information may appear far apart in the input.
- Appropriate for tool-driven workflows that require function calls or schema-shaped output.
- 01
Software development
Generate, review, edit, and transform code while keeping more project context in a single request.
- 02
Long-document analysis
Analyze large reports, policies, knowledge collections, or other text-heavy inputs without immediately splitting them into small chunks.
- 03
Instruction-heavy automation
Follow multi-step requirements where output format, constraints, and task boundaries need to remain consistent.
- 04
Tool-based applications
Use function calling and structured outputs to connect model decisions with deterministic application logic.
03 / Pricing
GPT-4.1 Pricing
GPT-4.1 API pricing is $2.00 per million input tokens, $0.50 per million cached input tokens, and $8.00 per million output tokens.
Long Context Makes Cost Measurement Important
The context window is large enough that input design can materially affect the cost of an individual request. Sending a full repository, a large document set, or a long conversation on every call can consume far more input tokens than a conventional chat request.
Prompt caching changes the economics when large prefixes are reused. If your application repeatedly sends the same instructions or stable source material, cached input can cost substantially less than standard input. That makes request structure an important part of model evaluation rather than a purely technical implementation detail.
Output tokens are priced higher than input tokens, so verbose responses can also become a meaningful cost driver. For code-generation workflows, ask whether the model needs to rewrite an entire file or whether a smaller patch, structured result, or concise answer is sufficient.
- $2.00 per 1M standard input tokens.
- $0.50 per 1M cached input tokens.
- $8.00 per 1M output tokens.
1M tokens · USD
- Input
- $2.00
- Cached input
- $0.50
- Output
- $8.00
Example: 10K input + 2K output
- Input cost
- $0.0200
- Output cost
- $0.0160
- Estimated total
- $0.0360
04 / Context
GPT-4.1 Has a 1,047,576-Token Context Window
GPT-4.1 can process up to 1,047,576 tokens of context and can generate up to 32,768 output tokens, giving developers substantially more input capacity than output capacity.
Large Context Is Most Valuable When the Model Can Find the Right Details
Context capacity alone does not guarantee better results. A production prompt may combine a system message, user input, previous turns, source files, retrieved passages, and tool output. As that input grows, the application still needs to preserve clear instructions and avoid filling the prompt with irrelevant material.
For coding workloads, the large window can reduce the need to select only a few files before asking a repository-level question. For document applications, it can support broader source coverage. In both cases, evaluation should test whether the model consistently uses the correct information when the relevant evidence is buried deep in the prompt.
EidoStack can help compare context strategies using the same workload instead of assuming that the largest possible prompt is automatically the best one.
- Context window: 1,047,576 tokens.
- Maximum output: 32,768 tokens.
- Large enough for substantial code and document inputs.
- Best results still depend on context selection and prompt structure.
Context window
1,047,576
Max output
32,768
The context window includes the instructions, conversation state, source material, and other tokens sent as part of a request.
05 / Coding & instructions
Coding and Instruction Following Are Core GPT-4.1 Strengths
GPT-4.1 was introduced with a strong focus on software engineering, reliable instruction following, and long-context comprehension, which makes these areas central to evaluating the model.
Useful for Tasks Where Small Instruction Errors Become Product Bugs
Many API tasks fail not because the model lacks general knowledge, but because it misses one requirement: a field must be omitted, a schema must be respected, only certain files may change, or a tool should be called before producing the final response.
GPT-4.1 is designed for these constraint-heavy scenarios. For coding, that includes generating changes from repository context, applying edits, explaining unfamiliar code, and producing implementation output that follows a requested format.
The model is still non-reasoning. There is no reasoning_effort setting to tune from low to high. For deeply deliberative tasks, it is worth comparing GPT-4.1 with a current reasoning model rather than assuming one architecture is best for every workload.
- 01
Instruction fidelity
Useful when prompts contain multiple requirements, formatting rules, or explicit boundaries.
- 02
Code generation and editing
A strong candidate for implementation, refactoring, code review, and diff-oriented workflows.
- 03
Long-context comprehension
Designed to work with relevant information distributed across large prompts rather than only short chat history.
- 04
Direct execution
Non-reasoning behavior can be attractive when an application values straightforward latency without a separate reasoning phase.
06 / Capabilities
GPT-4.1 API Capabilities
GPT-4.1 supports the API features needed for both conversational products and structured application workflows, including image input, streaming, function calling, structured outputs, fine-tuning, and predicted outputs.
More Than a Text Completion Model
Image input allows an application to include screenshots, diagrams, charts, and other visual information alongside text. Function calling lets the model select application-defined tools, while structured outputs help constrain responses to machine-readable formats.
Fine-tuning provides an additional optimization path for teams that need behavior specialized around a repeatable task or domain. Predicted outputs can be useful in editing-style workloads where much of the expected output is already known.
The model can be used through both Chat Completions and the Responses API, allowing teams to integrate it into existing OpenAI application architectures.
- Supported
Text
Text input and text output.
- Supported
Image input
Analyze visual content supplied with the request.
- Supported
Streaming
Receive generated output incrementally.
- Supported
Function calling
Connect model decisions to application-defined functions.
- Supported
Structured outputs
Return data that follows an expected response structure.
- Supported
Fine-tuning
Customize the model for supported fine-tuning workflows.
- Supported
Predicted outputs
Optimize supported generation tasks when much of the expected output is known.
07 / Evaluation
GPT-4.1 Strengths and Limitations
GPT-4.1 remains a useful model for coding, instruction following, tool calls, and long-context processing, but its value depends on whether those strengths match the production workload you are evaluating.
Strengths
Large context window
More than one million tokens of context can support repository-scale and document-heavy requests.
Developer-oriented performance
Coding and instruction-following behavior make GPT-4.1 a practical candidate for software workflows.
Strong integration features
Function calling, structured outputs, streaming, and image input support production application patterns.
Fine-tuning support
Teams can evaluate a customized GPT-4.1 variant when prompting alone is not enough for a repeatable workload.
What to consider
No adjustable reasoning effort
GPT-4.1 is a non-reasoning model, so deeply deliberative tasks should be compared with current reasoning-capable alternatives.
Output is much smaller than input capacity
The 32,768-token output limit is generous for many tasks but far below the model's 1M-token context window.
No native audio or video modality
The model accepts text and image input but does not natively accept audio or video through its model modalities.
Large prompts can become expensive
A huge context window is useful only when the added source material improves the result enough to justify the extra tokens.
Evaluate before production
Test GPT-4.1 with your real application prompts
Run the same prompts you expect to use in production, then compare response quality, token consumption, cost, and context usage inside EidoStack.
Start FreeConnect your own OpenAI API key and evaluate GPT-4.1 under conditions that match your application.
Common Questions
What is GPT-4.1?
GPT-4.1 is an OpenAI non-reasoning model focused on strong instruction following, coding, tool calling, and long-context workloads. It accepts text and image input and produces text output.
How much does GPT-4.1 cost?
GPT-4.1 costs $2.00 per 1M standard input tokens, $0.50 per 1M cached input tokens, and $8.00 per 1M output tokens.
What is the GPT-4.1 context window?
GPT-4.1 has a context window of 1,047,576 tokens and a maximum output of 32,768 tokens.
Is GPT-4.1 a reasoning model?
No. OpenAI describes GPT-4.1 as a non-reasoning model. It does not use the adjustable reasoning-effort controls available on reasoning-oriented model families.
Does GPT-4.1 support image input?
Yes. GPT-4.1 supports both text and image input, while its output modality is text.
Does GPT-4.1 support function calling and structured outputs?
Yes. GPT-4.1 supports function calling and structured outputs, making it suitable for tool-driven and machine-readable application workflows.
Can GPT-4.1 be fine-tuned?
Yes. OpenAI lists fine-tuning as a supported GPT-4.1 feature.
What is GPT-4.1 best used for?
GPT-4.1 is a strong candidate for coding, code editing, instruction-heavy automation, long-document processing, repository analysis, and applications that rely on function calling or structured outputs.
Model information
Last updated
Specifications, pricing, modalities, and supported features on this page are based on the official OpenAI documentation for GPT-4.1. Provider pricing, availability, and API capabilities can change, so production integrations should be checked against the latest OpenAI documentation.