OpenAI GPT-5.5 Specs and Pricing: 1M Context Window, Reasoning Effort, and GPT-5.4 Upgrade Triggers

Overview of OpenAI GPT-5.5 Flagship Model Released on April 23, 2026, OpenAI's GPT-5.5—codenamed "Spud"—stands as the company's latest flagship large language model (LLM), model ID . Designed for advanced coding, knowledge-intensive tasks, and scientific research, it builds on GPT-5.4 with enhanced intelligence while maintaining comparable latency. According to the official as of May 15, 2026 (UTC), GPT-5.5 introduces adjustable controls, multimodal text+image inputs, and a massive context window, making it a strong fit for enterprise B2B operations like multi-agent platforms in LUMOS. For English-speaking B2B leaders evaluating AI for operations, GPT-5.5 targets RAG-heavy workflows, coding agents, and document analysis. Early access partners highlighted its efficiency in agentic tasks, with OpenAI addressing training data issues like fantasy creature fixations through robust safeguards.

Context Window Size and Multimodal Inputs GPT-5.5 offers a 1M token context window via the OpenAI API, enabling processing of extensive documents or conversation histories—far surpassing many competitors for enterprise RAG applications. Note that Codex variants cap at 400K tokens, per the (as of May 15, 2026). Multimodal Capabilities - Text + Image Inputs : Supports vision modalities for document analysis, charts, and screenshots. Images are tokenized similarly to prior GPT models (e.g., 85 tokens per 512x512 tile), billed as input tokens. - Tokenization Rules : Per OpenAI's tokenizer docs, text follows cl100k base; images add variable tokens based on resolution. This suits LUMOS platforms handling visual RAG queries. For contexts exceeding 272K tokens, OpenAI applies potential surcharges—verify the latest on the as rates can tier by usage volume. Reasoning Effort Parameter: Levels and

Usage A standout feature is the parameter, which generates internal "reasoning tokens" for planning, tool use, and multi-step reasoning. Levels include: Level Use Case Impact on Tokens & Latency --------- ----------------------------------- ---------------------------- Simple queries Minimal added tokens Basic tasks, efficiency-focused Low overhead Default; balanced coding/knowledge Moderate increase Complex agents Higher tokens/latency Frontier research/multi-step Significant compute Defaults to . Per the (May 15, 2026), higher efforts boost accuracy in LUMOS multi-agent setups but raise billed tokens (reasoning tokens count toward output) and latency. For production, test for cost-optimized RAG. Per-1M-Token Pricing and Long-Context Surcharges As of May 15, 2026, on OpenAI's : - GPT-5.5 : $5 per 1M input tokens; $30 per 1M output tokens (includes reasoning tokens). - GPT-5.5 Pro ( ): H

igher rates for peak accuracy, exact figures tiered by volume. Long-Context Rules ( 272K Tokens) OpenAI imposes surcharges for extended contexts, scaling with length (e.g., 2x multiplier beyond thresholds)—confirm live tiers, as they adjust for demand. Batch API offers up to 50% discounts. For LUMOS RAG workflows, calculate costs via OpenAI's ; reasoning.effort amplifies output billing. No invented comparisons: Always reference primary docs over aggregators like OpenRouter. GPT-5.5 vs GPT-5.4: Performance Differences GPT-5.5 matches GPT-5.4's per-token latency but delivers superior intelligence, using fewer tokens for equivalent tasks like coding ( , May 2026). Key diffs: - Context : 1M vs. GPT-5.4's 128K base (extendable). - Reasoning : Native controls absent in 5.4. - Efficiency : Better for knowledge work, per model card benchmarks. Upgrade if GPT-5.4 hits limits in multi-agent LUMOS

chains. When to Choose GPT-5.5 for Coding and Knowledge Work Opt for GPT-5.5 over GPT-5.4 in these enterprise scenarios: - Coding Agents : effort excels in multi-file edits, debugging—ideal for LUMOS dev ops. - Knowledge Workflows : 1M context handles full codebases/docs in RAG, reducing chunking errors. - Multimodal RAG : Analyze invoices/schematics without external vision models. - Cost Triggers : If effort on 5.4 exceeds budgets, test 5.5's token efficiency. For LUMOS platforms, route simple queries to GPT-5.4-mini, escalate complex to GPT-5.5. Benchmarks, Safety, and Enterprise Integration Tips Benchmarks (model card, May 15, 2026) show GPT-5.5 leading in coding/research vs. Claude Opus 4.7/Gemini 3.1 Pro, though some note hallucination risks—mitigate with grounding. Safety : Enhanced safeguards post-training fixes. LUMOS Tips : - Integrate via OpenAI API SDK; use in agent loops. - M

onitor costs: 20-50% token uplift on . - Scale with provisioned throughput for predictable latency. Disclaimer : This content is for educational and informational purposes only, reflecting data as of May 15, 2026 (UTC) from official OpenAI sources. It is not professional financial, legal, or technic