State-of-the-art agentic coding, 1M context, and per-token latency parity with GPT-5.4.

OpenAI released GPT-5.5 on April 23, 2026 — the next default frontier model in ChatGPT and Codex, and the first OpenAI model to ship with a 1M-token API context window. The headline is simple: state-of-the-art agentic coding at per-token latency parity with GPT-5.4.

GPT-5.5 leads agentic-coding benchmarks at 82.7% on Terminal-Bench 2.0, hits 73.1% on the internal Expert-SWE long-horizon benchmark, and reaches 84.9% on GDPval — all while matching GPT-5.4 per-token latency in real-world serving and using significantly fewer tokens to complete the same Codex tasks. Alongside the standard model, OpenAI shipped GPT-5.5 Profor the hardest research, math, and retrieval work; OpenAI's API pricing page now lists GPT-5.5 pricing for developers.

For teams that already deployed GPT-5.4, the migration story is straightforward — same API surface, lower token spend per task, and a meaningful jump in agentic capability. This guide covers the release in full: variants, benchmarks, pricing, inference co-design with NVIDIA GB200/GB300, and the cybersecurity posture change under OpenAI's Preparedness Framework.

GPT-5.5 is positioned as a step change in agentic capability rather than a pure benchmark refresh. OpenAI's framing is consistent across the launch materials: the model understands user intent faster, uses tools more efficiently, and stays coherent across long multi-step tasks — coding, browsing, computer operation, document and spreadsheet work, and early scientific research. On Artificial Analysis's Coding Agent Index, OpenAI reports GPT-5.5 delivering state-of-the-art intelligence at roughly half the cost of competing frontier coding models on a token-spend basis.

The release lands one month after the GPT-5.4 family rollout covered in our GPT-5.4 complete guide, and continues the cadence of frequent frontier-model updates documented in our twelve-models-in-a-week analysis. GPT-5.5 is also available immediately in Codex with a 400K-token window across Plus, Pro, Business, Enterprise, Edu, and Go plans, and a Fast mode that generates tokens 1.5× faster at 2.5× the cost.

GPT-5.5 ships in two API SKUs and a few different surface configurations. In ChatGPT, the standard model is exposed as GPT-5.5 Thinking for Plus, Pro, Business, and Enterprise users. GPT-5.5 Pro is reserved for Pro, Business, and Enterprise users in ChatGPT. In Codex, GPT-5.5 is available across all paid plans with a 400K context window, with an optional Fast mode for users who want lower latency at higher cost.

For teams already running GPT-5.4 or earlier, the practical default is to swap to standard GPT-5.5 and reserve Pro for specific high-stakes pipelines — research synthesis, deep BrowseComp-style retrieval, multi-step math, or complex legal/financial reasoning. Pro shows clear gains on those evals: 90.1% BrowseComp (vs 84.4% standard), 52.4% FrontierMath Tier 1–3 (vs 51.7%), and 39.6% FrontierMath Tier 4 (vs 35.4%).

Agentic coding is where GPT-5.5 separates most clearly from prior generations and from competitors. On Terminal-Bench 2.0 — which tests complex command-line workflows requiring planning, iteration, and tool coordination — GPT-5.5 hits 82.7%, well ahead of GPT-5.4 at 75.1%. On the internal Expert-SWE eval, which targets long-horizon coding tasks with a median estimated human completion time of 20 hours, GPT-5.5 reaches 73.1% versus 68.5% for GPT-5.4.

The qualitative reports from early testers track the numbers. Dan Shipper of Every called GPT-5.5 “the first coding model I've used that has serious conceptual clarity,” after using it to reproduce an architectural rewrite a senior engineer had previously needed days to land. Pietro Schirano of MagicPath described GPT-5.5 merging a branch with hundreds of frontend and refactor changes into a substantially-changed main branch in one shot in about 20 minutes. For agencies comparing options, the broader picture is covered in our Claude Opus 4.7 vs GPT-5.4 agentic coding analysis — GPT-5.5 extends the gap further on Terminal-Bench and Expert-SWE.

GPT-5.5 extends its lead beyond pure coding into the broader knowledge-work loop: finding information, understanding what matters, using tools, checking outputs. On OSWorld-Verified, GPT-5.5 reaches 78.7% — up from 75.0% on GPT-5.4. On GDPval, which measures agents producing well-specified knowledge work across 44 occupations, GPT-5.5 hits 84.9%.

Domain-specific knowledge work shows the same pattern. On OfficeQA Pro — Databricks' benchmark for office-software tasks — GPT-5.5 scores 54.1%. On OpenAI's internal Investment Banking Modeling Tasks eval, GPT-5.5 hits 88.5%. FinanceAgent v1.1 comes in at 60.0%. These are the workloads where agentic AI actually displaces an analyst hour rather than just summarizing a report.

OpenAI shared concrete internal examples. Their Finance team used Codex to review 24,771 K-1 tax forms (71,637 pages), accelerating the task by two weeks compared to the prior year. A Go-to-Market employee automated weekly business reports for a 5–10 hour per week saving. OpenAI states that more than 85% of the company uses Codex weekly across software engineering, finance, comms, marketing, data science, and product. The 1M-token context window is what makes many of these workflows tractable — full-codebase analysis and entire-policy- corpus reasoning start to behave like normal model calls rather than exotic capabilities that need careful chunking strategies. For agencies running AI transformation programs, that's the practical lift.

Scientific research is the most surprising area of progress in the GPT-5.5 release. The model shows clear gains on multi-stage data analysis workflows that look more like multi-day research projects than standalone Q&A. On GeneBench — a new evaluation of multi-stage scientific data analysis in genetics and quantitative biology — GPT-5.5 scores 25.0% (Pro: 33.2%) versus 19.0% on GPT-5.4. On BixBench, designed around real-world bioinformatics, GPT-5.5 reaches 80.5%, the leading published score among major frontier models.

OpenAI disclosed that an internal version of GPT-5.5 with a custom harness contributed to a new asymptotic proof about off-diagonal Ramsey numbers — later verified in Lean. Bartosz Naskręcki at Adam Mickiewicz University used GPT-5.5 in Codex to build an algebraic-geometry app from a single prompt in 11 minutes. Derya Unutmaz of the Jackson Laboratory used GPT-5.5 Pro to analyze a 62-sample, ~28,000-gene expression dataset and produce a research report he said would have taken his team months. For agencies and operators in regulated, knowledge-dense industries, the implication is that GPT-5.5 Pro becomes a credible co-analyst on structured technical work.

The headline production fact about GPT-5.5 is that it serves at GPT-5.4 per-token latency despite being a more capable model. That isn't an accident of compiler tuning — it's the result of co-designing the model for, training it with, and serving it on NVIDIA GB200 and GB300 NVL72 systems. OpenAI describes inference here as an integrated system, and explicitly credits Codex and GPT-5.5 itself with landing key improvements in the serving stack.

From an integration standpoint, this matters for cost modeling. GPT-5.5 at $5/$30 per 1M tokens is more expensive than GPT-5.4 on paper, but the company reports that in Codex it has carefully tuned the experience so GPT-5.5 delivers better results with fewer tokens than GPT-5.4 for most users — meaning real per-task spend often falls. The practical advice is to A/B test on representative tasks rather than extrapolate from per-token list price.

OpenAI is treating GPT-5.5's biological/chemical and cybersecurity capabilities as High under its Preparedness Framework. While GPT-5.5 didn't reach the framework's Critical level for cyber, the evaluation results show meaningful step-ups: 81.8% on CyberGym (vs 79.0% on GPT-5.4 and 73.1% on Claude Opus 4.7) and 88.1% on the internal expanded Capture-the-Flag challenge tasks (vs 83.7%). The company shipped tighter classifiers around higher-risk activity, sensitive cyber requests, and protections against repeated misuse.

For agencies and platforms building security products, the architectural takeaway is that GPT-5.5 is now genuinely useful for triage, vulnerability scanning, fix-suggestion, and SOC workflows — but expect more refusals on edge-case prompts that resemble offensive testing, especially in the early weeks as classifiers are tuned. Teams with legitimate defensive use cases should evaluate Trusted Access via Codex rather than fighting against standard-tier guardrails.

GPT-5.5's API pricing positions it as the premium standard frontier tier rather than a cost-leader. At $5 per 1M input tokens and $30 per 1M output tokens, it's 2× the input price of GPT-5.4 Standard. Pro at $30/$180 per 1M tokens sits at the same headline rate as GPT-5.4 Pro. Both ship with a 1M-token context window, Batch and Flex pricing at half the standard rate, and Priority processing at 2.5×.

GPT-5.5 is available to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. GPT-5.5 Pro is available to Pro, Business, Enterprise, and Edu users in ChatGPT. OpenAI's API pricing page now lists GPT-5.5 at $5/$30 per 1M tokens; confirm account-level access in the platform console because rollouts and workspace eligibility can vary. For Codex specifically, the new model is available across Plus, Pro, Business, Enterprise, Edu, and Go plans with a 400K-token context window. See our Codex for almost everything release guide for the recommended starting point.

The frontier-model choice is increasingly task-shaped rather than vendor-shaped. GPT-5.5 leads on agentic coding, computer use, and cybersecurity. Claude Opus 4.7 remains strong on SWE-Bench Pro and certain autonomy-heavy refactors. Gemini 3.1 Pro leads on raw ARC-AGI-1 and competes hard on price for large-context workloads. Our broader frontier-model comparison is documented in the GPT-5.4 vs Opus 4.6 vs Gemini 3.1 Pro analysis.

For teams currently on GPT-5.4, the migration is straightforward — same API contract, same Codex surface, lower per-task token spend on most workflows, and a meaningful jump in agentic capability. For teams primarily on Claude or Gemini, the question is whether GPT-5.5's lead on Terminal-Bench, Expert-SWE, GDPval, and OSWorld translates to lift on your specific evals — the answer is usually yes for agentic coding and computer use.