GPT-5.5 Complete Guide: Thinking, Pro & 1M Context

GPT-5.5 Release Overview

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 1.5x faster at 2.5x the cost.

Variants: Thinking, Pro, and Fast Mode

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 — the general-purpose tier optimized for everyday work that benefits from reasoning. GPT-5.5 Pro is reserved for Pro, Business, and Enterprise users in ChatGPT and is targeted at the hardest questions and highest-accuracy outputs. 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%). For everything else, the standard model already leads on agentic coding, GDPval, OSWorld, and CyberGym.

Agentic Coding: 82.7% Terminal-Bench Lead

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%, Claude Opus 4.7 at 69.4%, and Gemini 3.1 Pro at 68.5%. 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. Across all three coding evals OpenAI publishes, GPT-5.5 improves on GPT-5.4 while using fewer tokens.

OpenAI's leadership framed the agentic-coding gains as the headline story on the launch call.

The qualitative reports from early testers are consistent with 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 the kind of 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. Senior engineers who tested the model said GPT-5.5 was noticeably stronger than GPT-5.4 and Claude Opus 4.7 at reasoning, autonomy, and predicting testing and review needs without explicit prompting.

For agencies and product teams comparing options, the broader agentic-coding picture is well covered in our Claude Opus 4.7 vs GPT-5.4 agentic coding analysis — the GPT-5.5 numbers extend that gap further on Terminal-Bench and Expert-SWE while Anthropic's SWE-Bench Pro lead remains the main counterpoint, with the memorization caveat noted in Anthropic's own release.

Computer Use and Knowledge Work

GPT-5.5 extends its lead beyond pure coding into the broader knowledge-work loop: finding information, understanding what matters, using tools, checking outputs, and turning raw material into something useful. On OSWorld-Verified, the standard evaluation for computer-use agents, GPT-5.5 reaches 78.7% — up from 75.0% on GPT-5.4 and ahead of Claude Opus 4.7 at 78.0%. On GDPval, which measures agents producing well-specified knowledge work across 44 occupations, GPT-5.5 hits 84.9% (vs 83.0% GPT-5.4, 80.3% Opus 4.7, 67.3% Gemini 3.1 Pro).

Domain-specific knowledge work shows the same pattern. On OfficeQA Pro — Databricks' benchmark for office-software tasks — GPT-5.5 scores 54.1% versus 43.6% for Opus 4.7 and 18.1% for Gemini 3.1 Pro. 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 — the kind of work agencies and consulting teams are now operationalizing through Codex-driven internal tools.

OpenAI shared concrete internal usage examples that frame the knowledge-work upside. Their Comms team built an automated Slack agent for low-risk speaking-request triage after using GPT-5.5 in Codex to analyze six months of historical data and design a scoring framework. Finance used Codex to review 24,771 K-1 tax forms (71,637 pages) — excluding personal information from the workflow — 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. On long-context retrieval evals, GPT-5.5 jumps to 74.0% on OpenAI MRCR v2 8-needle 512K-1M (up from 36.6% on GPT-5.4 and 32.2% on Claude Opus 4.7), 81.5% at 256K-512K, and 87.5% at 128K-256K. For agencies running AI transformation programs, the implication is direct: full-codebase analysis, entire-policy-corpus reasoning, and multi-document research start to behave like normal model calls rather than exotic capabilities that need careful chunking strategies.

Scientific Research Capabilities

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 and data analysis, GPT-5.5 reaches 80.5% (vs 74.0%), the leading published score among major frontier models.

OpenAI also disclosed that an internal version of GPT-5.5 with a custom harness contributed to a new asymptotic proof about off-diagonal Ramsey numbers — a longstanding combinatorics result later verified in Lean. Outside the lab, Bartosz Naskręcki, an assistant professor of mathematics at Adam Mickiewicz University, used GPT-5.5 in Codex to build an algebraic-geometry app from a single prompt in 11 minutes, visualizing the intersection of quadratic surfaces and converting the resulting curve into a Weierstrass model. 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.

Brandon White, co-founder of Axiom Bio, summarized the shift: "It's incredibly energizing to use OpenAI's new GPT-5.5 model in our harness, have it reason over massive biochemical datasets to predict human drug outcomes, and then see it deliver significant accuracy gains on our hardest drug discovery evals." 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 — not just a copywriter or chatbot.

Inference Efficiency: NVIDIA GB200/GB300 Co-Design

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 rather than a set of point optimizations, and explicitly credits Codex and GPT-5.5 itself with helping land a number of the 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. For teams building on top of the API, the practical advice is to A/B test on representative tasks rather than extrapolate from per-token list price.

Cybersecurity Capabilities and Safeguards

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, with the explicit acknowledgement that some users will find the stricter posture noticeable as it's tuned over time.

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.

Pricing, 1M Context, and Availability

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 2x the input price of GPT-5.4 Standard and 2x the output price. 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 2.5x.

Today, GPT-5.5 is rolling out to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. GPT-5.5 Pro is rolling out to Pro, Business, and Enterprise users in ChatGPT. API access on the Responses and Chat Completions endpoints is coming shortly — OpenAI cited additional safety and security work needed before serving the model at API scale, especially for partners integrating it into agent platforms. For Codex specifically, the new model is available across Plus, Pro, Business, Enterprise, Edu, and Go plans with a 400K-token context window.

For developers already integrated with Codex, the recommended starting point is documented in our Codex for almost everything release guide — same surface, lower token spend per task with GPT-5.5, and the option to flip Fast mode on for tight feedback loops.

Choosing GPT-5.5 vs. Alternatives

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 — most of those decisions hold, with GPT-5.5 strengthening OpenAI's position on the agentic and computer-use axes.

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, often more nuanced for code generation and long-context retrieval where individual model strengths and price points still matter.

Frequently Asked Questions