OpenAI GPT-5.5 Specs and Pricing: Context Window, Reasoning Effort, and When to Upgrade from GPT-5.4

Introducing OpenAI GPT-5.5 Flagship Model OpenAI released GPT-5.5 on April 23, 2026, marking a significant leap in frontier AI capabilities for complex enterprise tasks. Available as GPT-5.5 Thinking and GPT-5.5 Pro to paid users via the API, this model excels in coding, online research, document synthesis, and multi-step reasoning with improved tool usage and self-correction mechanisms. Designed for B2B operations, GPT-5.5 addresses key pain points in knowledge workflows and agentic systems like LUMOS platforms. It defaults to 'medium' reasoning effort, striking a balance between output quality, latency, and cost. Enhanced image understanding preserves finer visual details, making it suitable for multimodal RAG applications. While benchmarks show strong coding performance, OpenAI notes potential hallucination risks in edge cases, per the official system card (https://openai.com/index/gp

t-5-5-system-card/, as of May 14, 2026). For enterprise leaders, GPT-5.5 represents a strategic upgrade path from GPT-5.4, particularly in long-context scenarios exceeding 272K tokens. This article draws from official model cards and pricing pages to help you evaluate specs, costs, and integration fit. Context Window Size and Input Modalities GPT-5.5 boasts a massive 1,048,576-token (1.05M) context window, enabling ingestion of entire codebases, lengthy legal documents, or extensive RAG corpora without truncation. This is a step up from prior models, ideal for knowledge-intensive B2B workflows where context retention directly impacts accuracy. Key Modality Details - Text Input : Standard tokenization up to 1.05M tokens total. - Image Input : Supports vision capabilities with enhanced detail preservation. Images are tokenized at approximately 85 tokens per 512x512 tile (per OpenAI's visio

n guide, https://platform.openai.com/docs/guides/vision, as of May 14, 2026). For example, a high-res document scan might consume 500-1,000 tokens, fitting comfortably within the expanded window. - Multimodal Limits : Text + image combinations are supported, but total context cannot exceed 1.05M tokens. No video input at launch. This setup shines in LUMOS-style multi-agent systems, where agents process visual diagrams alongside code for debugging or workflow automation. Always verify token counts via OpenAI's tokenizer tool to avoid surprises in production. Reasoning Effort Levels Explained A standout feature of GPT-5.5 is its configurable 'reasoning effort' parameter: low, medium (default), high, or xhigh. This controls the model's internal chain-of-thought processes, directly influencing reasoning tokens, latency, and cost. How Reasoning Effort Works - Low : Minimal internal reasoning;

fastest latency, lowest token burn. Best for simple queries. - Medium (Default) : Balanced planning and verification steps. Suitable for most coding and knowledge tasks. - High : Deeper multi-step deliberation, generating more reasoning tokens for complex problems. - Xhigh : Maximum effort for frontier challenges like novel algorithm design; highest latency and cost. Crucially, reasoning tokens—used for internal planning—are billed as output tokens in enterprise API calls (https://openai.com/docs/guides/reasoning, as of May 14, 2026). This can multiply effective costs by 2-5x depending on effort level. For instance, a high-effort coding task might append 10K+ reasoning tokens to a 50K input prompt. In LUMOS integrations, tune effort dynamically: use 'medium' for routine RAG retrievals and escalate for agentic decision loops. Monitor via API usage dashboards to optimize. Per-1M-Token Pri

cing and Long-Context Surcharges Pricing for GPT-5.5 (exact model id: 'gpt-5.5') is tiered by usage volume, with live rates at https://openai.com/api/pricing/. As of May 14, 2026: - Tier 1 (first 1M tokens/day) : Input $15.00 per 1M tokens; Output $60.00 per 1M tokens (includes reasoning tokens). - Tier 5 (100M+ tokens/day) : Input $10.50 per 1M; Output $42.00 per 1M. Long-Context Surcharges For contexts 272K tokens, OpenAI applies a multiplier: - 272K-500K: 1.2x input token rate. - 500K-1.05M: 1.5x input token rate. These rules prevent abuse of extended windows while enabling cost predictability (per pricing page FAQ). Multimodal image tokens follow base input rates—no extra vision surcharge. Cost Estimation Example : A 800K-token RAG query (text+images) at medium effort, Tier 1: - Input: 800K 1.5x = 1.2M effective → $18. - Output + reasoning: 50K → $3. Total: $21 per call. Batch API of

fers 50% discounts for non-urgent jobs. Always query the latest via API metadata endpoints, as SKUs evolve. GPT-5.5 vs GPT-5.4: Key Differences for Coding and Knowledge Work GPT-5.4, OpenAI's prior flagship, offered a 512K context and basic reasoning. GPT-5.5 doubles down: Feature GPT-5.4 GPT-5.5 --