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Models/Language/GPT-5.5 Instant (May 2026)
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OpenAI

GPT-5.5 Instant (May 2026)

Released May 2026

View Rankings
openai.com
Intelligence
#102
Coding
#77
Arena AI
#20
Context400K

GPT-5.5 Instant is an optimized, high-speed large language model in the GPT-5 series, released on May 5, 2026, as a successor to GPT-5.3 Instant. It serves as a default conversational model for general-purpose interactions, balancing advanced reasoning capabilities with significantly lower latency and lower inference costs than higher-tier variants like GPT-5.5 Pro. The model is engineered for rapid response times, making it the primary choice for standard dialogues and high-volume text-based workloads.\n\n## Performance and Accuracy\nThe model introduces substantial improvements in factuality over its predecessors. Internal evaluations demonstrate a 52.5% reduction in hallucinated claims in high-stakes fields such as medicine, law, and finance. Additionally, GPT-5.5 Instant features real-time self-correction, which allows the model to detect and rectify logical errors mid-generation. It also prioritizes conciseness, generating responses that are approximately 30% shorter by word count while maintaining high factual density.\n\n## Multimodal Capabilities and Personalization\nAs a natively multimodal model, it processes both text and image inputs simultaneously. It includes an enhanced personalization and memory system that leverages context from past conversations and connected data sources. This architectural shift focuses on reducing repetitive follow-up questions by referencing user-authorized "memory" more effectively, while providing transparency into which sources were used to shape a given response.\n\n## Prompting and Steerability\nOptimization for GPT-5.5 Instant favors outcome-focused prompting over process-oriented instructions. The model performs most effectively when user prompts clearly define the desired goal, specific constraints, and verification criteria. This improved steerability allows the model to handle complex instructions with less manual scaffolding, as its training reduces the necessity for explicit length or formatting rules in every request.

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