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

What is OpenAI GPT-5.5? OpenAI GPT-5.5 (model ID: ) is the company's flagship large language model released in April 2026, designed for complex, agentic tasks in coding, research, and knowledge-intensive workflows. As per the official announcement on , it excels in planning, tool usage, and self-verification, making it ideal for B2B operations like production coding agents and enterprise RAG systems. Positioned as an upgrade over GPT-5.4, GPT-5.5 introduces enhanced reasoning capabilities via the parameter, support for text and image inputs, and a significantly expanded context window. For enterprise leaders evaluating AI integration—especially in multi-agent platforms like LUMOS—GPT-5.5 offers efficiency gains in token usage and output quality. All specs here are drawn from OpenAI's model card and API docs as of May 3, 2026 (UTC), available at . Context Window and Input Modalities GPT-5

.5 supports a maximum context window of 1,050,000 tokens, enabling long-context applications such as full codebase analysis or extended RAG retrievals in enterprise knowledge bases. This is a leap from earlier models, per the (as of May 3, 2026). Key Modality Details: - Text Input : Standard tokenization up to 1M tokens total. - Image Input : Multimodal support for vision tasks; images are tokenized dynamically (e.g., a 1024x1024 image ≈ 1,700 tokens, scaling with resolution). Combine text + images seamlessly for tasks like diagram-to-code generation. - Total Context Limit : Input + output cannot exceed 1,050,000 tokens; cached prompts via API reduce recompute costs. For B2B use in coding agents, this allows feeding entire repositories or visual specs without truncation, critical for LUMOS-style multi-agent RAG where agents chain long documents. Reasoning Effort Parameter Explained The p

arameter is a new API control for GPT-5.5, defaulting to . Options include: - : Fastest, minimal internal thinking—ideal for simple queries. - (default): Balanced for most knowledge work. - : Deeper chain-of-thought, better for complex reasoning. - : Maximum effort for edge-case puzzles or verification. Per (May 3, 2026), higher settings increase latency (e.g., 3-5x ) and output tokens (up to 2x more reasoning traces), but improve accuracy in tool-heavy tasks. Billed costs rise indirectly via extra tokens generated during reasoning. In practice, tune via API: . For enterprise ops, start with in LUMOS agents and A/B test latency vs. success rates. Per-1M-Token Pricing and Surcharges Pricing for GPT-5.5 is usage-based, tiered by volume. Always verify live rates at OpenAI's (as of May 3, 2026, UTC), as they update frequently with discounts for batch API or high-volume commitments. Standard

Tier 1 rates for : - Input Tokens : $18 per 1M tokens - Output Tokens : $54 per 1M tokens Additional factors: - Batch API : Up to 50% discount for async jobs. - Fine-Tuning/Provisioned Throughput : Custom quotes for enterprises. No invented comparisons—use OpenAI's calculator tool for your workload estimates. For knowledge work, expect 20-30% higher costs than lighter models due to reasoning overhead. Long-Context Rules ( 272K Tokens) OpenAI applies surcharges for extended contexts to reflect compute intensity. As of May 3, 2026, per : - Base (≤272K tokens total) : Standard rates above. - 272K tokens : 1.75x multiplier on excess input/output tokens only. Example: A 500K input prompt bills 272K at $18/1M + 228K at $18/1M 1.75 = effective $21.65/1M blended. This incentivizes efficient prompting. In RAG pipelines, compress docs to stay under 272K for cost savings, or embrace surcharges for

full-book analysis in LUMOS agents. GPT-5.5 vs GPT-5.4: Key Differences GPT-5.4 (max 400K context) suits lighter loads, but GPT-5.5 shines in scale. Verified differences per model cards (May 3, 2026): Feature GPT-5.4 GPT-5.5 --------- --------- --------- Context Window 400K 1.05M Reasoning Effort No low/medium/high/xhigh Pricing (Input/1M) $12 $18 (+50%) Strengths General chat Agentic coding/research Choose GPT-5.5 over 5.4 when: - Needing 400K context for RAG/codebases. - Tool-calling accuracy 95% in benchmarks (e.g., agentic coding evals). - Knowledge work with images/docs. Migration: Update param in API calls; retrain prompts for reasoning effort. Best Use Cases for Coding and Knowledge Work GPT-5.5 targets enterprise pain points: - Coding Agents : Full-repo refactoring; outperforms GPT-5.4 by 25% in HumanEval-like tests (per OpenAI evals). - Knowledge Workflows : Long-doc summarizati

on, multi-hop QA. - RAG Optimization : 1M context reduces chunking errors. For B2B leaders: Deploy in production agents where latency <10s and accuracy trumps cost. Integration in Multi-Agent Platforms like LUMOS LUMOS multi-agent frameworks leverage GPT-5.5 via simple API swaps: 1. Set and for plan