OpenAI model snapshot
GPT-4.1 (2025-04-14)
The dated GPT-4.1 API snapshot for teams that want a fixed model version for reproducible evaluations, controlled production releases, and consistent prompt behavior.
- 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 (2025-04-14) Is
GPT-4.1 (2025-04-14) is a dated OpenAI model snapshot. Its API identifier, gpt-4.1-2025-04-14, lets an application target a specific GPT-4.1 version instead of relying only on the moving gpt-4.1 alias.
A Versioned Model for Repeatable Behavior
The distinction matters most when a team needs to reproduce a result later. If a prompt, evaluation suite, or production workflow is tested against a dated snapshot, the model version remains explicit in the configuration.
That makes the April 14 snapshot useful for controlled deployments, regression tests, benchmark runs, and any workflow where a silent change in model behavior would make comparisons harder to interpret.
The snapshot keeps the core GPT-4.1 characteristics: a 1,047,576-token context window, text and image input, text output, a 32,768-token maximum output, and support for function calling, structured outputs, streaming, fine-tuning, and predicted outputs.
- Uses the fixed model ID
gpt-4.1-2025-04-14. - Belongs to the GPT-4.1 family released on April 14, 2025.
- Preserves a known model version for repeatable testing.
- Retains GPT-4.1's long-context and developer-oriented API capabilities.
- Provider
- OpenAI
- Family
- GPT-4.1
- Snapshot
- 2025-04-14
- Model ID
- gpt-4.1-2025-04-14
- Knowledge cutoff
- Jun 1, 2024
- Input modalities
- Text, Image
- Output modality
- Text
02 / Why pin it
Why Use the GPT-4.1 April 2025 Snapshot?
A dated snapshot is valuable when model identity is part of the experiment. It gives developers a stable reference point for prompts, evaluations, and production releases.
Separate Model Changes From Application Changes
When an application uses an alias, a future provider update can introduce a new underlying revision. That may be desirable when you always want the latest model, but it can complicate debugging: a changed response might come from your prompt, your code, your data, or the model version itself.
Pinning gpt-4.1-2025-04-14 removes one of those variables. The application can change prompts, tools, retrieval logic, or schemas while the selected model snapshot stays constant.
This is particularly useful for evaluation systems. If two test runs use the same dataset and the same dated snapshot, differences are easier to attribute to the experiment rather than to an untracked model update.
- 01
Pin the model
Use gpt-4.1-2025-04-14 explicitly in the application or evaluation configuration.
- 02
Build a baseline
Run representative prompts and save quality, cost, latency, and token-usage results.
- 03
Change one variable
Iterate on prompts, context strategy, tools, or application logic while keeping the model snapshot fixed.
- 04
Upgrade deliberately
Compare the pinned baseline with another snapshot or model before changing production traffic.
03 / Use cases
Where a Fixed GPT-4.1 Snapshot Is Most Useful
The dated model is especially useful when consistency across runs is a product requirement, not just a convenience.
Production Stability and Evaluation Are the Main Reasons to Choose It
For an ordinary prototype, the generic GPT-4.1 alias may be sufficient. For a production system with acceptance tests, a formal QA process, or benchmark history, an explicit snapshot creates a clearer dependency.
Software teams can pin the snapshot while validating code-generation prompts. AI product teams can preserve benchmark baselines. Regulated or review-heavy workflows can record exactly which model version produced a result. Migration projects can compare a known GPT-4.1 baseline against a newer model before switching.
The snapshot also remains suitable for the workloads GPT-4.1 was built to handle: coding, instruction-heavy tasks, long documents, large repositories, function calling, and structured application output.
- 01
Regression testing
Keep the underlying model fixed while checking whether prompt, tool, or application changes alter expected behavior.
- 02
Production pinning
Deploy against an explicit model version when predictable behavior is more important than automatically following an alias.
- 03
Benchmark baselines
Preserve a historical GPT-4.1 reference point for future model comparisons and upgrade decisions.
- 04
Repository and document workflows
Use the 1M-token context capacity for large codebases, long source material, and context-heavy instructions.
04 / Pricing
GPT-4.1 (2025-04-14) Pricing
The GPT-4.1 snapshot uses GPT-4.1 token pricing: $2.00 per million input tokens, $0.50 per million cached input tokens, and $8.00 per million output tokens.
Stable Versioning Does Not Remove Usage Variability
Pinning a snapshot stabilizes the model version, but request cost still depends on how your application constructs each call. A long repository prompt can contain hundreds of thousands of tokens, while an extraction task may use only a small fraction of the available window.
Cached input can materially reduce cost when the same prompt prefix or source context is reused. Output length matters as well because generated tokens are priced separately from input tokens.
For repeatable evaluations, record both the model ID and the token profile of each run. That gives you a baseline that includes economics as well as response quality.
- $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: 25K input + 4K output
- Input cost
- $0.0500
- Output cost
- $0.0320
- Estimated total
- $0.0820
05 / Context
A 1,047,576-Token Context Window in a Fixed Snapshot
GPT-4.1 (2025-04-14) supports 1,047,576 tokens of context and up to 32,768 output tokens, allowing a pinned model version to work with large code and document inputs.
Long-Context Tests Become Easier to Reproduce
Long-context evaluation is sensitive to more than the nominal context limit. Where relevant information appears, how much distracting material surrounds it, and how instructions are structured can all change the result.
A dated snapshot helps isolate those variables. You can rerun the same retrieval set, repository snapshot, or document collection against the same GPT-4.1 version and examine whether changes to context selection actually improve the output.
This is useful for RAG experiments, repository analysis, document review, support history, and other systems where context strategy is part of the product design.
- 1,047,576-token context window.
- 32,768-token maximum output.
- Suitable for controlled long-context benchmarks.
- Useful for testing retrieval and context-selection changes against a stable model version.
Context window
1,047,576
Max output
32,768
The input context may include instructions, conversation history, retrieved documents, source code, tool results, and other data supplied with the request.
06 / Capabilities
API Capabilities of the April 2025 Snapshot
The dated snapshot exposes the GPT-4.1 feature set for application integration, including multimodal input, structured responses, tool calling, and model customization.
A Fixed Version Can Still Support Rich Application Workflows
gpt-4.1-2025-04-14 is not a reduced archival model. It is a selectable GPT-4.1 snapshot with the same core API-oriented capabilities documented for the family.
Text and image input make it usable for mixed-content analysis. Function calling connects model decisions to deterministic application actions. Structured outputs help enforce machine-readable response shapes. Streaming supports incremental delivery, while fine-tuning gives teams another path for specialized behavior.
Predicted outputs are also supported, which can be relevant to editing and rewrite workflows where much of the desired output is already known.
- Supported
Text
Accept text input and generate text output.
- Supported
Image input
Include visual information together with textual instructions.
- Supported
Streaming
Deliver generated output incrementally.
- Supported
Function calling
Connect the model to application-defined tools and actions.
- Supported
Structured outputs
Constrain responses to predictable machine-readable structures.
- Supported
Fine-tuning
Use supported fine-tuning workflows for specialized model behavior.
- Supported
Predicted outputs
Optimize supported generation tasks when much of the expected result is already known.
07 / Evaluation
Strengths and Limitations of Pinning This Snapshot
The main advantage of GPT-4.1 (2025-04-14) is not a different context window or a special price tier. It is the ability to make the exact GPT-4.1 version part of your test and deployment contract.
Strengths
Reproducible evaluations
A dated model ID gives benchmark runs and prompt tests a specific version that can be recorded and repeated.
Controlled production changes
Teams can decide when to evaluate and adopt a newer model instead of coupling upgrades to an alias.
Strong long-context capacity
The 1,047,576-token window supports large repositories, document sets, and extensive application context.
Full GPT-4.1 integration features
Function calling, structured outputs, image input, streaming, fine-tuning, and predicted outputs remain available.
What to consider
Pinned means intentionally static
A snapshot does not automatically inherit improvements that may appear in later model versions.
Upgrade testing becomes your responsibility
Teams should periodically compare the pinned baseline with newer models and decide when migration is justified.
Non-reasoning architecture
GPT-4.1 does not expose adjustable reasoning effort, so reasoning-focused models may perform better on some complex tasks.
Large context can raise spend
The ability to send very large prompts should be balanced against actual retrieval quality, cache behavior, latency, and token cost.
Evaluate a fixed model version
Test the GPT-4.1 April 2025 snapshot on your own prompts
Run repeatable tests against the exact gpt-4.1-2025-04-14 identifier and compare response quality, token usage, cost, and context consumption without changing the underlying model snapshot.
Start FreeConnect your own OpenAI API key and use the dated model ID when you need stable evaluations or controlled production behavior.
Common Questions
What is GPT-4.1-2025-04-14?
GPT-4.1-2025-04-14 is a dated snapshot of OpenAI's GPT-4.1 model. It lets developers target the April 14, 2025 model version explicitly instead of relying only on the GPT-4.1 alias.
Why use GPT-4.1 (2025-04-14) instead of the GPT-4.1 alias?
A dated snapshot is useful when you want the selected model version to remain explicit for reproducible evaluations, regression tests, controlled deployments, and deliberate upgrade decisions.
What is the context window of GPT-4.1-2025-04-14?
The snapshot supports a 1,047,576-token context window and a maximum output of 32,768 tokens.
How much does GPT-4.1 (2025-04-14) cost?
GPT-4.1 pricing is $2.00 per 1M standard input tokens, $0.50 per 1M cached input tokens, and $8.00 per 1M output tokens.
Does GPT-4.1-2025-04-14 support images?
Yes. The GPT-4.1 snapshot supports text and image input and produces text output.
Does this GPT-4.1 snapshot support function calling and structured outputs?
Yes. Function calling and structured outputs are supported, along with streaming, fine-tuning, and predicted outputs.
Is GPT-4.1-2025-04-14 a reasoning model?
No. GPT-4.1 is a non-reasoning model and does not provide an adjustable reasoning-effort control.
When should I pin this snapshot in production?
Pinning is most useful when application behavior must be reproducible, when you maintain regression tests or benchmark baselines, or when model upgrades need to pass an explicit evaluation before deployment.
Model information
Last updated
Specifications, pricing, modalities, supported features, and snapshot information on this page are based on the official OpenAI documentation for GPT-4.1. OpenAI lists gpt-4.1-2025-04-14 as a GPT-4.1 snapshot that can be used to lock a specific model version.