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

What is OpenAI GPT-5.5? OpenAI GPT-5.5, released in April 2026, represents the company's latest flagship reasoning model designed for complex, agentic tasks. According to the official model card on (updated April 2026), GPT-5.5 excels in coding, scientific research, and knowledge-intensive workflows, outperforming predecessors in tool usage, self-verification, and multi-step planning. The model ID for API access is , available via the OpenAI API shortly after launch. It's optimized for enterprise applications like multi-agent platforms (e.g., LUMOS), where long-context retrieval-augmented generation (RAG) and autonomous agents handle production operations. As B2B leaders evaluate AI for scaling operations, GPT-5.5 addresses key pain points in reliability and efficiency, per OpenAI's dated April 2026. Context Window and Input Modalities GPT-5.5 supports a massive 1,050,000 token context w

indow for input, as documented in the as of May 6, 2026 (UTC). This is a significant leap from GPT-5.4's 400,000 tokens, enabling deeper RAG pipelines with full documents, codebases, or multi-turn agent histories without truncation. Input modalities include text + image , making it a multimodal LLM. Images are tokenized similarly to text—typically 85+ tokens per low-res image, scaling with resolution and detail, per OpenAI's vision guidelines. Output remains text-only, but this supports workflows like diagram analysis in knowledge work or UI debugging in coding agents. For enterprise RAG, this long context reduces chunking overhead in LUMOS-style platforms, but evaluate token limits against your data volumes to avoid surcharges (detailed below). Reasoning Effort Parameter Explained A standout feature is the parameter, configurable from to . As explained in OpenAI's (updated April 2026),

this controls internal reasoning tokens used for planning, reflection, and tool calls. minimal : Fastest, lowest billed tokens—ideal for simple queries. low/medium : Balanced for most coding/knowledge tasks. high/xhigh : Deeper chain-of-thought for complex agents, increasing latency by 2-5x and billed tokens by up to 4x (internal tokens are charged as input/output). In practice, higher effort boosts accuracy in benchmarks like agentic coding (e.g., 25% better tool success vs GPT-5.4 at ), but monitor latency for real-time ops. For LUMOS integrations, start with and A/B test against latency budgets. Official Pricing per 1M Tokens Pricing for follows OpenAI's tiered structure, verified from the as of May 6, 2026 (UTC). Base rates for standard context (<272K tokens): Input tokens : $12.00 per 1M Output tokens : $36.00 per 1M These apply to text and image inputs alike, with images billed via

their tokenized representation. Batch API discounts (up to 50%) and provisioned throughput are available for enterprises—check your account tier for custom rates. Unlike GPT-5.4 ($8 input/$24 output at same date), GPT-5.5 reflects added reasoning capabilities. To calculate costs: Use OpenAI's tokenizer tool for precise estimates, factoring multipliers (e.g., adds 3x input tokens internally). Long-Context Surcharges ( 272K Tokens) For contexts exceeding 272K input tokens, OpenAI applies surcharges per the pricing page (May 6, 2026): 273K–500K : 1.25x input multiplier 501K–1M+ : 1.75x input multiplier (output unchanged) This incentivizes efficient prompting while supporting ultra-long RAG. Example: A 800K-token query at base $12 input becomes $21 per 1M effective. GPT-5.4 caps at 400K without surcharges, making GPT-5.5 costlier for massive contexts but viable for knowledge bases 500K toke

ns. Enterprise tip: Hybrid routing in LUMOS—use GPT-5.4 for <272K, escalate to 5.5 only for overflow. GPT-5.5 vs GPT-5.4: Performance Gains Per OpenAI benchmarks (April 2026 system card), GPT-5.5 outperforms GPT-5.4 ( ): Metric GPT-5.4 GPT-5.5 (medium effort) Gain :---------------------- :------ :---------------------- :---- Coding (HumanEval+) 92% 97% +5% Agentic Tasks (TAU-bench) 78% 89% +11% Knowledge QA (long-context) 85% 94% +9% Context: 1,050K vs 400K. GPT-5.5 shines in multi-tool agents and error-checking, reducing hallucinations by 20% in research workflows. Pricing edge for GPT-5.4 on short tasks, but 5.5 justifies premium for production reliability. When to Choose GPT-5.5 for Coding and Knowledge Work Upgrade from GPT-5.4 if: Coding agents : Need 400K context for full repos or reasoning for debugging (e.g., 15% fewer iterations in LUMOS simulations). Knowledge work : RAG over 5

00K-token docs (e.g., legal/financial analysis)—surcharges offset by 25% accuracy gains. Multimodal needs : Image+text for ops diagrams. Stick with 5.4 for high-volume, low-complexity (e.g., <272K chatbots). ROI trigger: If agent failure rate 10%, pilot GPT-5.5 at effort. Enterprise Integration Tips