GPT-5.5

OpenAI's frontier flagship: top-tier coding, reasoning, and vision with a 1.05-million-tokenThe basic unit of text a model reads and writes. Tokens are roughly three-quarters of a word in English — so 100 tokens is about 75 words. Models don't see letters or words directly; they see tokens. Pricing is almost always quoted per million tokens, and context windows are measured in tokens rather than words. context windowThe maximum amount of text the model can "see" at once — prompt plus prior conversation plus any documents you give it. Measured in tokens (which are roughly three-quarters of a word each). A 128K context window is about 96,000 words of input — roughly a 400-page book. Larger context windows let the model work with bigger documents but cost more to run. — the best of the GPT line, at premium prices.

GPT-5.5, released in April 2026, is OpenAI's current top model — the one positioned for the hardest coding, research, and high-value professional work. It pairs strong general capability with a very large context windowThe maximum amount of text the model can "see" at once — prompt plus prior conversation plus any documents you give it. Measured in tokens (which are roughly three-quarters of a word each). A 128K context window is about 96,000 words of input — roughly a 400-page book. Larger context windows let the model work with bigger documents but cost more to run. (1.05 million tokens, enough for a big codebase or a stack of documents at once) and the deepest developer tooling in the industry: function calling, structured outputs, agentic workflows, and broad third-party support.

It's the headline of OpenAI's enormous lineup, which is both the appeal and the catch. The appeal is that there's a model for every budget; the catch is that the flagship itself is expensive — at $5 in / $30 out per million tokens it's among the priciest frontier options, sitting above Claude Opus on output cost, with newer competitors like DeepSeek and Gemini undercutting it. The practical advice OpenAI itself gives is to route simpler tasks to cheaper mini/nano models and reserve GPT-5.5 for work that genuinely needs the top tier.

Being closed, there's nothing to download — everything runs through OpenAI's API, ChatGPT, or Azure. For most teams that's fine; for those with strict data-residency needs, OpenAI offers enterprise and zero-retention options, and Azure hosting for regulated industries.

• The hardest coding and agentic tasks where top-tier capability justifies the cost.
• Research-grade reasoning and complex professional work.
• Large-context jobs — analyzing big codebases or document sets in one pass.
• Teams already invested in the OpenAI/ChatGPT/Azure ecosystem and its tooling.

• Cost-sensitive or high-volume workloads — route those to GPT-5.4 or the mini/nano tiers; the flagship is expensive.
• Self-hosting or fully in-house data — it's closed; use gpt-oss-120b instead.
• Buyers optimizing purely on price — DeepSeek and Gemini undercut it at the frontier.
• The very highest-stakes reasoning where cost is no object — GPT-5.5 Pro goes further.

Proprietary and API-only. GPT-5.5 is accessed through the OpenAI API (or Azure OpenAI) under OpenAI's terms — you get commercial rights to what you build with the outputs, but no rights to the model itself: no weightsThe numerical values inside a trained model that encode everything it has learned. A model is, functionally, a giant list of weights — tens of billions of numbers for a mid-sized model, hundreds of billions for a frontier model. "Open-weight" means those numbers are published. "Downloading the weights" means getting the actual file you'd need to run the model yourself., no modification, no redistribution. Your diligence is OpenAI's usage policies and the data-handling terms of the API (with enterprise and zero-retention options available). If owning or self-hosting matters, gpt-oss is OpenAI's open line.

Against GPT-5.4, GPT-5.5 is more capable with a far larger context windowThe maximum amount of text the model can "see" at once — prompt plus prior conversation plus any documents you give it. Measured in tokens (which are roughly three-quarters of a word each). A 128K context window is about 96,000 words of input — roughly a 400-page book. Larger context windows let the model work with bigger documents but cost more to run., but double the price — many production workloads are better served by 5.4's cost-quality balance. Against GPT-5.5 Pro, the Pro tier reaches higher on research-grade reasoning at six times the price. Against the other frontier flagships — Google's Gemini 3.5 Flash and Anthropic's Claude — GPT-5.5's strengths are ecosystem depth and agentic tooling, while Gemini leads on multimodality and Claude on coding reliability, and both can be cheaper.