Long Context Window
Processes up to 200,000 tokens in a single request, allowing it to handle full codebases, lengthy documents, or extended multi-turn conversations without truncation.
GPT-4.1 is a text generation model developed by OpenAI and released in April 2025. It is positioned as OpenAI's flagship model for handling complex, multi-domain tasks and is available to developers via the OpenAI API. The model supports a 200,000-token context window, enabling it to process and reason over long documents, codebases, and extended conversations in a single request. Its training data has a knowledge cutoff of May 31, 2024. GPT-4.1 is designed for problem solving across a wide range of domains, including coding, analysis, instruction following, and structured output generation. It is an API-only model, meaning it is accessible through the OpenAI platform rather than through ChatGPT's consumer interface. Developers building agents, pipelines, or applications that require handling large amounts of context or complex multi-step instructions are the primary intended audience for this model.
High-signal model metadata in a structured two-column overview table.
The entity that provides this model.
The routed model identifier exposed by upstream providers.
The number of tokens supported by the input context window.
The number of tokens that can be generated by the model in a single request.
Whether the model's code is available for public use.
When the model was first released.
When the model's knowledge was last updated.
The providers that offer this model. This is not an exhaustive list.
Types of data this model can process.
A fuller summary of positioning, capabilities, and source-specific details for GPT-4.1.
GPT-4.1 is a text generation model developed by OpenAI and released in April 2025. It is positioned as OpenAI's flagship model for handling complex, multi-domain tasks and is available to developers via the OpenAI API. The model supports a 200,000-token context window, enabling it to process and reason over long documents, codebases, and extended conversations in a single request. Its training data has a knowledge cutoff of May 31, 2024.
GPT-4.1 is designed for problem solving across a wide range of domains, including coding, analysis, instruction following, and structured output generation. It is an API-only model, meaning it is accessible through the OpenAI platform rather than through ChatGPT's consumer interface. Developers building agents, pipelines, or applications that require handling large amounts of context or complex multi-step instructions are the primary intended audience for this model.
Processes up to 200,000 tokens in a single request, allowing it to handle full codebases, lengthy documents, or extended multi-turn conversations without truncation.
Generates coherent, detailed text responses across domains including analysis, summarization, and open-ended question answering.
Writes, explains, and debugs code across multiple programming languages, and is suited for agentic coding workflows that require multi-step reasoning.
Follows complex, multi-part instructions with high fidelity, making it well suited for structured pipelines and automated agent tasks.
Returns responses in structured formats such as JSON, enabling reliable integration with downstream systems and APIs.
Supports multi-step, tool-using agent workflows where the model must plan, execute, and iterate across a sequence of actions.
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Additional usage-cost dimensions synced into the project for this model.
Places where this model is available, based on the synced detail-page metadata.
Endpoint-level provider data currently available for this model.
Benchmark scores synced from the current model source and normalized into the local catalog.
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AIME 2024
American math olympiad problems
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
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HLE
Questions that challenge frontier models across many domains
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LiveCodeBench
Real-world coding tasks from recent competitions
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MATH-500
Undergraduate and competition-level math problems
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MMLU-Pro
Expert knowledge across 14 academic disciplines
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SciCode
Scientific research coding and numerical methods
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Official model cards, release notes, docs, and other references synced from the source page.
GPT-4.1 discussions are most active in r/OpenAI, r/singularity, r/ChatGPT. Top Reddit threads cluster around benchmark and model-comparison threads, mixed hands-on reactions.
The strongest match in this snapshot has 6348 upvotes and 1277 comments.
[Upcoming deprecation of GPT-4.1](https://github.blog/changelog/2026-05-07-upcoming-deprecation-of-gpt-4-1/)
> We will deprecate the following model across all GitHub Copilot experiences (including Copilot Chat, inline edits, ask and agent modes, and code completions) on 6/1/2026
What does this mean for code completions? AFAIK GPT-4.1 is the only model that can be used for code completions at the moment.
Github's announcement on switching to [usage based billing](https://github.blog/news-insights/company-news/github-copilot-is-moving-to-usage-based-billing/) states:
> Code completions and Next Edit suggestions remain included in all plans and do not consume AI Credits.
So the feature isn't going away.
Does anyone know what model will be used for code completion after 6/1/2026?
Source: [Retiring GPT-4o, GPT-4.1, GPT-4.1 mini, o4-mini | OpenAI](https://openai.com/index/retiring-gpt-4o-and-older-models/)
GPT-4.1 supports a context window of 200,000 tokens, which allows it to process large documents, long conversation histories, or extensive codebases in a single request.
GPT-4.1 has a training data cutoff of May 31, 2024, meaning it does not have knowledge of events or information published after that date.
GPT-4.1 is an API-only model and is not available through the ChatGPT consumer interface. It is accessible via the OpenAI API and platforms like MindStudio.
GPT-4.1 is published by OpenAI and was announced and made available in April 2025.
GPT-4.1 is designed for complex, multi-domain tasks including coding, long-document analysis, instruction following, structured output generation, and agentic workflows that require multi-step reasoning.
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