OpenAI has released GPT-5.5, its newest frontier model for ChatGPT, Codex, and the OpenAI API. The name may sound like a small version bump, but the technical direction is much bigger. GPT-5.5 is designed for agentic work: tasks where an AI model plans, calls tools, reads files, writes code, checks output, and continues through multi-step workflows with less hand-holding.
The timing also matters. GPT-5.5 arrived on April 23, 2026, only a week after Anthropic introduced Claude Opus 4.7 on April 16, 2026. Both models are competing for the same serious users: developers, researchers, automation builders, enterprise teams, and creators who need more than a polite chatbot.
So what changed from GPT-5.4? Who gets GPT-5.5? Is it worth the higher API price? And how does it compare with Claude Opus 4.7? Here is the technical breakdown.
What Is GPT-5.5?
GPT-5.5 is OpenAI’s latest flagship model for complex reasoning, coding, research, tool use, computer use, and long-context professional work. It is available in ChatGPT, Codex, and the API, with a higher-end gpt-5.5-pro variant for harder tasks.
The key phrase is agentic AI. A normal chatbot answers prompts. An agentic model can break a problem into steps, inspect intermediate results, decide what to do next, and use external tools when needed. In a coding environment, that might mean reading a repository, finding the right file, editing code, running tests, interpreting the failure, and making a second fix. In research, it might mean searching, comparing sources, extracting claims, and building a structured answer.
This makes GPT-5.5 especially relevant for workflows such as large-codebase debugging, technical research, data analysis, document review, spreadsheet work, browser tasks, customer-support automation, and enterprise knowledge-base search.
GPT-5.5 Availability: Who Gets It?
OpenAI says GPT-5.5 Thinking is available for ChatGPT Plus, Pro, Business, and Enterprise users. GPT-5.5 Pro is available for Pro, Business, and Enterprise users. In Codex, GPT-5.5 is available across Plus, Pro, Business, Enterprise, Edu, and Go plans.
For developers, both gpt-5.5 and gpt-5.5-pro are available through the API. GPT-5.5 supports the Responses API and Chat Completions API. GPT-5.5 Pro is intended for more difficult jobs and may be better suited to background execution where latency is less important than deeper reasoning.
The practical takeaway is simple: casual free-tier users may not see GPT-5.5 immediately, but paid ChatGPT users, Codex users, and API developers can start building with it now.
GPT-5.5 API Specs
For technical users, the API profile is where GPT-5.5 becomes clearer. OpenAI lists gpt-5.5 at $5 per 1 million input tokens and $30 per 1 million output tokens. It supports a 1 million token context window and up to 128K output tokens. Its knowledge cutoff is listed as December 1, 2025.
GPT-5.5 supports text and image input, text output, function calling, structured outputs, web search, file search, computer use, and code-oriented workflows depending on the API setup. It also supports reasoning effort controls such as none, low, medium, high, and xhigh.
The Pro version is more expensive: $30 per 1 million input tokens and $180 per 1 million output tokens. It lists a context window around 1,050,000 tokens and the same 128K max output. Function calling and structured outputs are supported, but streaming is not listed for GPT-5.5 Pro.
| Model | Input Price | Output Price | Context | Max Output |
|---|---|---|---|---|
| GPT-5.4 | $2.50 / 1M | $15 / 1M | Large context | 128K |
| GPT-5.5 | $5 / 1M | $30 / 1M | 1M | 128K |
| GPT-5.5 Pro | $30 / 1M | $180 / 1M | ~1.05M | 128K |
What Changed From GPT-5.4?
The move from GPT-5.4 to GPT-5.5 is mainly about reliability across longer, messier tasks. GPT-5.4 is already strong, but GPT-5.5 improves the areas that matter for agents: tool coordination, coding workflows, computer-use tasks, long-context retrieval, and complex reasoning.
OpenAI’s positioning suggests GPT-5.5 is better at understanding user intent earlier, deciding when a tool is needed, managing intermediate steps, and completing work without excessive prompting. That is more important than it sounds. Many AI failures do not happen because the model cannot produce one good paragraph. They happen because the model loses track of the task, calls the wrong tool, misses a file, ignores a test failure, or stops before the job is complete.
GPT-5.5 also has a newer knowledge cutoff than GPT-5.4: December 2025 instead of August 2025. That does not replace web browsing for current facts, but it does make the model more useful for technical topics, libraries, tools, and business context released in late 2025.
Coding Performance: GPT-5.5 vs GPT-5.4
Coding is one of GPT-5.5’s strongest upgrade areas. OpenAI reports that GPT-5.5 scores 82.7% on Terminal-Bench 2.0, compared with 75.1% for GPT-5.4. On Expert-SWE, GPT-5.5 reaches 73.1%, compared with 68.5% for GPT-5.4. On SWE-Bench Pro, GPT-5.5 reaches 58.6%, slightly above GPT-5.4’s 57.7%.
The Terminal-Bench result is especially important. Terminal-Bench tests command-line workflows where a model has to reason, use tools, and iterate. That is closer to how coding agents work in practice. A model that can write a function but cannot run tests, inspect logs, or correct its own patch is not enough for real software engineering.
For developers, GPT-5.5 should be more useful in repo-level work: tracing bugs, refactoring modules, modifying tests, migrating APIs, diagnosing build errors, and understanding how changes affect a broader codebase.
Computer Use and Tool Use
GPT-5.5 also improves in computer-use and tool-use benchmarks. OpenAI reports 78.7% on OSWorld-Verified, compared with 75.0% for GPT-5.4. On BrowseComp, GPT-5.5 scores 84.4%, compared with 82.7%. On Tau2-bench Telecom, GPT-5.5 reaches 98.0%, compared with 92.8% for GPT-5.4.
These benchmarks matter because the next generation of AI apps will depend on tools. A useful model may need to search the web, open a file, call a database, operate a browser, run code, or produce structured JSON. The model has to know when to use each tool and how to interpret the result.
GPT-5.5’s improvement here suggests stronger orchestration: fewer dead ends, better tool selection, and more stable completion of multi-step workflows.
Long Context: Where GPT-5.5 Pulls Ahead
A 1 million token context window sounds impressive, but the real question is whether the model can use it. Long context is not just input capacity. It is retrieval, attention, prioritization, and reasoning across distant information.
OpenAI reports major gains on long-context evaluations. On Graphwalks BFS at 1M context, GPT-5.5 scores 45.4%, compared with only 9.4% for GPT-5.4. On MRCR v2 8-needle at 512K to 1M context, GPT-5.5 scores 74.0%, compared with 36.6% for GPT-5.4.
That is one of the clearest technical reasons to use GPT-5.5. It is not merely accepting more tokens; it appears better at finding and reasoning over information buried inside huge inputs. This helps with large repositories, legal contracts, research libraries, meeting archives, support logs, and enterprise documentation.
Cost Tradeoff: Is GPT-5.5 Worth It?
GPT-5.5 costs twice as much as GPT-5.4 in the API. That sounds expensive, but token price alone is not the full story. The real metric is cost per completed task.
If GPT-5.4 can summarize 10,000 product reviews accurately, GPT-5.5 may be overkill. But if GPT-5.4 fails twice on a repository migration and GPT-5.5 succeeds once, GPT-5.5 may be cheaper in practice. Failed retries, human review, broken code, and incorrect tool calls all have hidden costs.
Use GPT-5.4 for high-volume routine work such as classification, short summaries, simple rewriting, light extraction, and basic chat. Use GPT-5.5 when failure is expensive: production code, complex research, long-context analysis, tool-heavy automations, and tasks requiring multiple steps.
GPT-5.5 vs Claude Opus 4.7
Claude Opus 4.7 is Anthropic’s top general model released one week before GPT-5.5. Anthropic positions it around advanced software engineering, long-running coding tasks, stronger visual reasoning, polished professional output, and better self-verification.
The comparison is not one-sided. GPT-5.5 appears stronger on many OpenAI-reported agentic benchmarks, while Claude Opus 4.7 remains extremely competitive for software engineering and design-heavy work. Developers should think in terms of workload fit rather than a single universal winner.
GPT-5.5 looks especially attractive for terminal workflows, browser tasks, tool orchestration, long-context retrieval, math, cybersecurity evaluations, and broad professional agent tasks. Claude Opus 4.7 may be preferable for front-end implementation, interface polish, visual reasoning, long-form writing, and tasks where Anthropic’s style and self-checking behavior are useful.
Pricing: GPT-5.5 vs Claude Opus 4.7
On pricing, Claude Opus 4.7 is slightly cheaper than GPT-5.5 for output-heavy workloads. Both models are listed at $5 per 1 million input tokens. GPT-5.5 costs $30 per 1 million output tokens, while Claude Opus 4.7 costs $25 per 1 million output tokens.
| Model | Input Price | Output Price | Best Fit |
|---|---|---|---|
| GPT-5.4 | $2.50 / 1M | $15 / 1M | Cheaper high-volume tasks |
| GPT-5.5 | $5 / 1M | $30 / 1M | Agentic work and long context |
| Claude Opus 4.7 | $5 / 1M | $25 / 1M | Coding, writing, visual work |
If your workload produces very long outputs, Claude may have a cost advantage. If GPT-5.5 needs fewer retries or handles tools more reliably, it may still win on total cost.
Benchmarks: GPT-5.5 vs Claude Opus 4.7
OpenAI’s published comparisons show GPT-5.5 ahead on several technical evaluations. GPT-5.5 scores 82.7% on Terminal-Bench 2.0, compared with 69.4% for Claude Opus 4.7. On GDPval, GPT-5.5 scores 84.9% versus 80.3%. On BrowseComp, GPT-5.5 scores 84.4% versus 79.3%. On CyberGym, GPT-5.5 scores 81.8% versus 73.1%.
Claude Opus 4.7, however, leads on SWE-Bench Pro in OpenAI’s comparison, with 64.3% versus GPT-5.5’s 58.6%. That is important because SWE-Bench-style tasks are among the closest public benchmarks to real GitHub issue resolution.
The honest conclusion is nuanced: GPT-5.5 seems stronger on broad agentic and tool-based work, while Claude Opus 4.7 remains a serious coding competitor and may be better on some real-world software repair tasks.
Coding Style Difference
The models also feel different in likely usage. GPT-5.5 is best described as a technical operator. It is built for reading large contexts, choosing tools, navigating terminals, and completing workflows. That makes it a strong candidate for coding agents, research agents, automation platforms, and technical assistants embedded in developer tools.
Claude Opus 4.7 is better framed as a senior creative engineer. It is often strong at interface decisions, clean writing, design-sensitive code, and long implementation passes where taste matters. For front-end polish, UX copy, visual reasoning, and product-oriented prototypes, Claude’s strengths may be valuable.
The best developer stack may use both: GPT-5.5 for investigation, tooling, long-context analysis, and backend-heavy workflows; Claude Opus 4.7 for front-end refinement, product feel, documentation, and design-aware implementation.
Safety and Cybersecurity
GPT-5.5 also ships with serious safety framing. OpenAI says the model went through targeted testing for advanced cybersecurity and biology-related risks. Its system card treats GPT-5.5 as a high-capability model in biological/chemical and cybersecurity domains, though below OpenAI’s critical cybersecurity threshold.
OpenAI also launched a GPT-5.5 Bio Bug Bounty, offering $25,000 for the first valid universal jailbreak that passes its bio-safety challenge. The testing period runs from April 28 to July 27, 2026.
Anthropic’s Claude Opus 4.7 also arrived with strong cybersecurity discussion. Anthropic says it experimented with reducing certain cyber capabilities during training and includes safeguards for prohibited or high-risk cybersecurity requests. This is now a major frontier-model battleground: stronger models must also prove they can be deployed responsibly.
Final Verdict
GPT-5.5 is a meaningful upgrade over GPT-5.4, especially for technical users. It is more expensive, but the improvements are concentrated where serious AI workflows need them most: coding agents, terminal tasks, tool use, computer use, long-context reasoning, and research synthesis.
For casual users writing short emails or simple summaries, GPT-5.4 may still be enough. For developers, analysts, researchers, and businesses, GPT-5.5 is much more interesting because it can reduce failures across complicated workflows.
Compared with Claude Opus 4.7, GPT-5.5 is not an automatic winner everywhere. GPT-5.5 appears stronger on many technical and agentic benchmarks, while Claude Opus 4.7 remains powerful for software engineering, design-heavy coding, visual reasoning, and polished professional output.
The simplest recommendation is this: use GPT-5.5 when you need a model to operate across tools, codebases, documents, browsers, and long context. Use Claude Opus 4.7 when you want polished coding, strong writing, visual reasoning, and front-end taste. Use GPT-5.4 when cost matters more than frontier capability.
GPT-5.5 is not just another chatbot update. It is OpenAI’s clearest step toward AI as an execution layer for real computer work.
