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Text Generation Model

GPT-5.1

The best model for coding and agentic tasks across domains

PublisherOpenAI
TypeText
Context Window400,000 tokens
Training DataNovember 2025
Input$1.25/MTok
Output$10.00/MTok
FLAGSHIPREASONING

Flagship model for coding and agentic tasks

GPT-5.1 is a text generation model developed by OpenAI, positioned as the flagship option for coding and agentic workflows. It supports a 400,000-token context window and features configurable reasoning effort, allowing users to toggle between reasoning and non-reasoning modes depending on the task at hand. Its training data extends through November 2025.

The model is designed with tool use and agent orchestration in mind, accepting inputs that include tool definitions and MCP server configurations alongside standard text prompts. This makes it well-suited for multi-step tasks, automated pipelines, and code generation scenarios where structured decision-making and external integrations are required.

What GPT-5.1 supports

Configurable Reasoning

Supports both reasoning and non-reasoning modes, letting users adjust the level of deliberative effort the model applies to a given task.

Large Context Window

Processes up to 400,000 tokens in a single context, enabling long documents, large codebases, or extended conversation histories.

Tool Use

Accepts tool definitions as inputs, allowing the model to call external functions and APIs as part of a response.

MCP Server Integration

Supports MCP server configurations as a native input type, enabling integration with Model Context Protocol-compatible services.

Code Generation

Optimized for coding tasks across domains, producing, reviewing, and debugging code as a primary use case per OpenAI's model positioning.

Agentic Task Execution

Designed for multi-step agentic workflows where the model must plan, use tools, and complete tasks with minimal human intervention.

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Benchmark scores

Scores represent accuracy — the percentage of questions answered correctly on each test.

BenchmarkWhat it testsScore
MMLU-ProExpert knowledge across 14 academic disciplines87.0%
GPQA DiamondPhD-level science questions (biology, physics, chemistry)87.3%
LiveCodeBenchReal-world coding tasks from recent competitions86.8%
HLEQuestions that challenge frontier models across many domains26.5%
SciCodeScientific research coding and numerical methods43.3%
SWE-bench VerifiedReal GitHub issues requiring multi-file code fixes76.3%
AIME 2025American math olympiad problems (2025)94.0%

Common questions about GPT-5.1

What is the context window for GPT-5.1?

GPT-5.1 supports a context window of 400,000 tokens, which accommodates large codebases, lengthy documents, and extended multi-turn conversations.

What is the knowledge cutoff date for GPT-5.1?

GPT-5.1's training data extends through November 2025, based on the training date listed in the model metadata.

Does GPT-5.1 support tool calling and external integrations?

Yes. GPT-5.1 accepts tool definitions and MCP server configurations as native input types, making it suitable for agentic pipelines that require external function calls or service integrations.

What does configurable reasoning mean for GPT-5.1?

GPT-5.1 allows users to configure the level of reasoning effort applied to a task. This means you can enable more deliberative, step-by-step reasoning for complex problems or disable it for faster, more direct responses.

What tasks is GPT-5.1 best suited for?

According to OpenAI's model overview, GPT-5.1 is the flagship model for coding and agentic tasks across domains, making it a strong choice for code generation, debugging, and automated multi-step workflows.

What people think about GPT-5.1

Community reception to GPT-5.1 has been generally positive, with early discussions noting its introduction as a significant release from OpenAI and interest in its reasoning capabilities spotted in source code ahead of launch.

Some threads highlight limitations in specific mathematical benchmarks, such as failing on IMO 2025 problems where other models succeeded, and broader conversations touch on OpenAI's business trajectory alongside the model's release.

View more discussions →

Parameters & options

Max Temperature1
Max Response Size128,000 tokens
Reasoning EffortSelect

Used to give the model guidance on how many reasoning tokens it should generate before creating a response to the prompt. Low will favor speed and economical token usage, and high will favor more complete reasoning at the cost of more tokens generated and slower responses. The default value is medium, which is a balance between speed and reasoning accuracy.

Default: medium
NoneLowMediumHigh

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