Seven days separated the releases of the two most ambitious language models of this year. Anthropic released Claude Opus 4.7 on April 16. OpenAI responded a week later, on April 23, with GPT-5.5. Both companies claim the same thing: their model is the best for demanding, multi-step tasks. Both offer a context window of 1 million tokens. Both have the same input price of $5 per million tokens. And yet they are two very different tools.
OpenAI describes GPT-5.5 as the first full retraining of the base model since GPT-4.5. Internally, it was called "Spud." It natively processes text, images, audio, and video through a single architecture rather than a composite system of separate models. OpenAI designed it specifically in conjunction with NVIDIA's most powerful GB200 and GB300 NVL72 chips, resulting in a model that achieves the same latency as GPT-5.4 when deployed while operating at a significantly higher level.
With Claude Opus 4.7, Anthropic is betting on accuracy and reliability in long-running tasks. The model added a new thinking level called xhigh, which sits between the high and max settings. More precisely, it gives the model more room than high but does not create as much latency as max. In Claude Code, xhigh is now the default setting for all plans. This is reflected in the fact that Opus 4.7 actually verifies its work. It runs tests. It stops before submitting incorrect work.
Benchmarks: where each model leads
Reading the headlines gives the impression of a clear winner. But when you look at the data, the picture is different.
GPT-5.5 leads on most official comparison tables. On Terminal-Bench 2.0, it scored 82.7% compared with 69.4% for Claude Opus 4.7. On BrowseComp, a test of the ability to browse the web and find information, GPT-5.5 scored 84.4% and Opus 4.7 scored 79.3%. In difficult mathematics (FrontierMath Tier 4), the difference is even more pronounced: 35.4% vs. 22.9%. On the ARC-AGI-2 abstract reasoning benchmark, GPT-5.5 leads with 85% versus 75.8%.
On SWE-Bench Pro, which tests real code fixes in production repositories, Opus 4.7 leads with 64.3% compared with 58.6% for GPT-5.5. On MCP-Atlas, a tool-use benchmark, Opus 4.7 scored 79.1% and GPT-5.5 scored 75.3%. For financial analysis (FinanceAgent v1.1), Opus leads 64.4% vs. 60%. And on GPQA Diamond, which consists of deep expert-level questions, the result is reversed as well: Opus 4.7 scores 94.2% and GPT-5.5 scores 93.6%.
Of the 10 benchmarks for which both companies published figures, Claude Opus 4.7 leads in 6. GPT-5.5 leads in 4. Yet most media coverage declared GPT-5.5 the winner. This discrepancy between the headlines and the data is worth mentioning.
Coding and technical tasks: complexity matters
For developers, this is probably the most practical question. And the answer depends on exactly what you are doing.
On short and well-defined tasks, GPT-5.5 moves quickly and accurately. It generates code constructs, writes tests, creates scripts, and fills in specifications. It responds well to an iterative approach, quickly correcting whatever you send back to it.
For complex, multi-step coding, Opus 4.7 has a clear advantage. It maintains coherence across large codebases. It is less likely to invent nonexistent libraries. When faced with an unclear specification, it is more likely to stop and ask than to confidently generate an incorrect answer. On Anthropic's internal benchmark with 93 tasks, Opus 4.7 improved by 13% over version 4.6. On CursorBench, it jumped from 58% to 70%.
Security testing (pentesting) follows a different logic. A pentest is not a single question for a model. It is a chain of tasks: reconnaissance, attack-surface mapping, hypothesis generation, tool execution, evidence collection, reporting, and retesting. GPT-5.5 is well suited to structured outputs, scripts, and automation. Claude Opus 4.7 is stronger where deep reasoning about a system's business logic and the relationships between user roles is required.
Long context and agent workflows
Both models have a context window of 1 million tokens. That alone says nothing about how well a model works with that context.
GPT-5.5 scored 74% on the MRCR v2 test (8 needles in a haystack), compared with 32.2% for Opus 4.7. So if you work with very long documents and need a model to search accurately across extensive text, GPT-5.5 has a significant advantage in this regard.
For agentic coding chains, where the model progressively controls tools and manages complex dependencies, Opus 4.7 is the more reliable choice. It uses fewer sub-agents. It plans ahead. It does not stop in the middle of a complex refactoring task.
Alongside GPT-5.5, OpenAI released a platform for workflow agents. These are not chat tools. They are sequences of actions that the model manages for you. GPT-5.5 handles this approach better if you configure the thinking mode correctly. Extended thinking should be the default setting on Plus, Business, and Enterprise plans, not a fallback option for difficult cases.
What are you actually paying for?
For input, both models are priced the same: $5 per million tokens. The difference is in the output. Claude Opus 4.7 costs $25 per million output tokens. GPT-5.5 costs $30 per million. That is about 17% more.
OpenAI claims that GPT-5.5 can handle the same tasks with roughly 40% fewer tokens than the previous version, so the actual bill increases by only about 20%. This figure is based on its own calculation. When deploying at scale, each team will need to verify whether this holds true for its specific tasks.
Another detail is noteworthy: GPT-5.5 doubled the price compared with GPT-5.4. If you used the previous version extensively, the difference in cost will be noticeable.
Safety, accuracy, and sensitive fields
Anthropic is building a reputation as a company that prioritizes safety and interpretability over the speed of releasing new versions. For fields such as healthcare, law, or compliance, this may be a deciding factor. Claude Opus 4.7 tends to produce more cautious outputs and, in situations involving ambiguous instructions, is more likely to point out limitations than to generate confident-sounding but inaccurate answers.
For security testing of AI features such as chatbots, RAG systems, or agents with access to tools, Claude Opus 4.7 reasons better about the boundaries of permissions, memory, and data access. GPT-5.5, in turn, is well suited to generating structured testing frameworks and automating verification processes.
In both cases, however, the same principle applies: the model proposes. The tool measures, and the evidence decides. A human is accountable for the result.
Where each model truly wins
Here is a brief summary. If you need a model for:
- Quick scripting, code scaffolding, and clearly defined coding tasks → GPT-5.5
- Production code fixes, refactoring, and complex projects → Claude Opus 4.7
- Web browsing and information research → GPT-5.5
- Controlling tools via the MCP protocol → Claude Opus 4.7
- Difficult mathematics and abstract reasoning → GPT-5.5
- Financial analysis → Claude Opus 4.7
- Terminal and command-line agents → GPT-5.5
- Analysis of business logic and complex systems → Claude Opus 4.7
- Creating reports, templates, and structured outputs → GPT-5.5
- Deeper reasoning about ambiguous tasks → Claude Opus 4.7
The strongest workflow is usually one that combines both models. Claude Opus 4.7 for deep reasoning and GPT-5.5 for structured outputs, scripts, and automation.
Sources: penligent.ai, mindstudio.ai and reddit.com
