GPT-5.1

GPT-5.1 API Overview

GPT-5.1 is an advanced iteration of the GPT-5 family by OpenAI. It improves on conversational quality, adaptive reasoning, and instruction-following capability, positioning itself as a more intelligent and user-friendly large language model suited for a variety of practical AI applications.

Technical Specifications

• Architecture: Transformer-based neural network building on GPT-5 foundations, with presumed parameter count increases and optimized layer efficiency.
• Context Window: Supports very long context input up to 128K tokens, enhancing long-form reasoning and document handling.
• Inference: Runs distributed across GPU clusters, leveraging adaptive reasoning via a meta-controller that evaluates prompt complexity to dynamically allocate computational resources.
• Safety & Robustness: Improved safeguard measures across harassment, hate speech, and image input evaluations, ensuring responsible AI usage.

Performance Benchmarks

• Outperforms GPT-5 on key math and coding benchmarks such as AIME 2025 and Codeforces.
• Demonstrates significant improvements in instruction adherence, response clarity, and conversational tone.

Output Quality & Reasoning Performance

Users report GPT-5.1 excels in maintaining coherent narratives over extended conversations with fewer contradictions. Its outputs balance creativity and reliability, yielding precise, contextually-aware responses with strong adherence to factual information.

Key Features

• Adaptive Reasoning: Dynamically adjusts reasoning depth based on query complexity to balance speed and answer quality.
• Improved Instruction Following: More reliable, clearer responses that align closely with user requests.
• Personalization: Users can tailor ChatGPT’s tone and style with options including Default, Friendly, Efficient, Professional, Candid, Quirky, Nerdy, and Cynical, with further fine-tuning capabilities forthcoming.
• Multimodal Support: Enhanced handling of various data types, including images and complex documents.
• Long-Form Document Analysis: Supports extensive document contexts for deep analysis and summarization.

Practical Impact

GPT-5.1 is ideally suited for professional and research applications requiring deep reasoning, complex coding assistance, and multimodal content analysis. Its adaptability makes it valuable for interactive AI agents, automated research assistants, and creative collaborations.

GPT-5.1 API Pricing

• Input: $1.3125 per 1M tokens
• Cached Input: $0.13125 per 1M tokens
• Output: $10.50 per 1M tokens

Use Cases

• Everyday Chat and Assistance: Quick, natural conversational interactions using GPT-5.1 Instant.
• Research and Data Analysis: Complex problem solving, coding help, and detailed textual and scientific analysis.
• Content Creation: Drafting, summarizing, and brainstorming for marketing, presentations, or technical writing.
• Multimodal Applications: Handling images, documents, and structured data for integrated workflows.
• Personalized AI Experience: Custom tone and style settings for diverse user and organizational needs.

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Code Sample

Comparison with Other Models

vs GPT-5: GPT-5.1 brings significant improvements in conversational warmth, adaptive reasoning, and instruction following compared to GPT-5. It features two specialized variants, Instant for fast, natural chat and Thinking for deep reasoning, while GPT-5 uses a single balanced model approach.

vs GPT-5 Pro: While GPT-5 Pro remains the highest-capability variant (for ultra-deep reasoning tasks), GPT-5.1 puts many of those advances into a more broadly accessible consumer model, offering near-top performance with more usability for everyday tasks.

vs Claude Sonnet 4.5: Claude 4.5 leads in coding reliability and lower hallucination rates, making it ideal for safety-critical and detailed technical tasks. GPT-5.1 outperforms Claude in conversational warmth and adaptive reasoning flexibility, delivering faster responses for everyday use and more personalized interactions.

Community Option

Highlights include its smoother handling of long conversations and improved factual consistency. Some developers admire the balance it strikes between reasoning complexity and responsiveness, making it preferred for multi-domain AI agent development.