Start by separating official positioning from benchmark tables

OpenAI’s API documentation describes GPT-5.5 as its newest frontier model for the most complex professional work, and shows that it supports a reasoning.effort control.

Anthropic’s Claude Opus 4.7 launch page emphasizes tool use, planning and software-engineering improvements. The page cites Hebbia’s report of a double-digit jump in tool-call and planning accuracy, and Rakuten-SWE-Bench results where Claude Opus 4.7 resolved three times as many production tasks as Opus 4.6.

That tells us how the companies position their models. For direct GPT-5.5 vs Claude Opus 4.7 numbers, this article relies mainly on third-party side-by-side summaries from Vellum, Kingy AI and Mashable.

Benchmark snapshot: where each model leads

The core comparison below comes primarily from Vellum’s GPT-5.5 benchmark summary. The GPQA Diamond ordering is also visible in Vellum’s leaderboard.

For software-engineering fixes, test Claude Opus 4.7 first

The clearest Claude win is SWE-Bench Pro: Claude Opus 4.7 scores 64.3%, compared with GPT-5.5 at 58.6%, a 5.7-point lead.

That supports a practical recommendation: if your main use case is fixing real issues, generating patches, understanding cross-file dependencies or reviewing complex pull requests, Claude Opus 4.7 belongs in the first round of testing.

SWE-bench Verified is another relevant signal. BenchLM describes it as a human-verified subset of SWE-bench designed to test whether models can resolve real GitHub issues, and lists Claude Opus 4.7 Adaptive at 87.6%. That source does not provide a directly comparable GPT-5.5 score, so it should not be used by itself to claim Claude definitely beats GPT-5.5 on SWE-bench Verified. The safer conclusion is that Claude Opus 4.7 is a very strong candidate for real software-engineering repair tasks.

For terminal, browsing and agent execution, GPT-5.5 has more public wins

GPT-5.5’s largest published advantage in this comparison is on Terminal-Bench 2.0: 82.7% versus 69.4%, a 13.3-point lead over Claude Opus 4.7.

It also leads on BrowseComp, GDPval and OSWorld-Verified, with scores of 84.4% versus 79.3%, 84.9% versus 80.3%, and 78.7% versus 78.0%, respectively.

For products that depend heavily on shell commands, browser navigation, file-system work, OS operations or multi-step automation, GPT-5.5 is therefore a natural first candidate.

There is an important caveat: this does not mean “pick GPT-5.5 for every agent task.” On MCP Atlas, Claude Opus 4.7 scores 79.1%, above GPT-5.5’s 75.3%; Anthropic’s own launch material also emphasizes improvements in tool calling and planning.

Professional work, reasoning and maths are split

Business and professional benchmarks do not point in just one direction. Vellum’s side-by-side table shows GPT-5.5 ahead on GDPval, 84.9% to 80.3%. Kingy AI’s summary, however, shows Claude Opus 4.7 ahead on FinanceAgent v1.1, 64.4% to 60.0%, while GPT-5.5 leads on OfficeQA Pro, 54.1% to 43.6%.

Reasoning and maths also depend on the task type. On GPQA Diamond, Claude Opus 4.7 scores 94.2% and GPT-5.5 scores 93.6%, a narrow 0.6-point Claude lead. On FrontierMath T1–3, GPT-5.5 scores 51.7% versus Claude Opus 4.7 at 43.8%, a 7.9-point GPT-5.5 lead.

Humanity’s Last Exam shows why caution matters when using third-party summaries. Kingy AI reports the no-tools result as GPT-5.5 at 41.4% and Claude Opus 4.7 at 46.9%; Mashable reports the no-tools result as GPT-5.5 at 40.6% and Claude Opus 4.7 at 31.2%. Because those public summaries disagree so sharply, this article does not treat that benchmark as core evidence for model selection.

How to choose: pick by workflow, not by “champion”

If your main workload is codebase-level repair, real GitHub issues, complex pull requests or patch generation, test Claude Opus 4.7 first. SWE-Bench Pro and SWE-bench Verified both support the view that it is strong in software-engineering repair.

If your main workload is terminal execution, web browsing, OS operations, automation agents or professional tasks represented by GDPval, test GPT-5.5 first. It has public side-by-side leads on Terminal-Bench 2.0, BrowseComp, OSWorld-Verified and GDPval.

If your workflow mixes coding, tool calls, long-horizon planning, document analysis and report generation, do not rely on one headline winner. GPT-5.5 leads several execution-oriented benchmarks, while Claude Opus 4.7 is stronger on SWE-Bench Pro, MCP Atlas and parts of Anthropic’s tool-planning narrative. Both should be on the shortlist.

What to test before putting either model into production

Public benchmarks are useful for narrowing the field. They should not be your final procurement or deployment decision.

Before going live, prepare a set of real tasks from your own workflow. Hide the model names, standardize the prompts, give each model the same tool permissions, context budget, time budget and scoring rubric. If you test GPT-5.5, also fix settings such as reasoning.effort, because OpenAI’s API documentation shows that the model supports that control.

Do not score only by average success rate. At minimum, track four outcomes: whether the task was completed, whether the answer was verifiable, how much human repair was needed, and the latency and call cost. In production, a model that makes fewer serious mistakes on your critical tasks can be more valuable than one that wins a few extra points on an unrelated benchmark.

The practical conclusion is straightforward: GPT-5.5 and Claude Opus 4.7 do not have an absolute winner. They have different strengths, and the better model is the one that fits your workflow.

Start by separating official positioning from benchmark tables

OpenAI’s API documentation describes GPT-5.5 as its newest frontier model for the most complex professional work, and shows that it supports a reasoning.effort control.

Anthropic’s Claude Opus 4.7 launch page emphasizes tool use, planning and software-engineering improvements. The page cites Hebbia’s report of a double-digit jump in tool-call and planning accuracy, and Rakuten-SWE-Bench results where Claude Opus 4.7 resolved three times as many production tasks as Opus 4.6.

That tells us how the companies position their models. For direct GPT-5.5 vs Claude Opus 4.7 numbers, this article relies mainly on third-party side-by-side summaries from Vellum, Kingy AI and Mashable.

Benchmark snapshot: where each model leads

The core comparison below comes primarily from Vellum’s GPT-5.5 benchmark summary. The GPQA Diamond ordering is also visible in Vellum’s leaderboard.

For software-engineering fixes, test Claude Opus 4.7 first

The clearest Claude win is SWE-Bench Pro: Claude Opus 4.7 scores 64.3%, compared with GPT-5.5 at 58.6%, a 5.7-point lead.

That supports a practical recommendation: if your main use case is fixing real issues, generating patches, understanding cross-file dependencies or reviewing complex pull requests, Claude Opus 4.7 belongs in the first round of testing.

SWE-bench Verified is another relevant signal. BenchLM describes it as a human-verified subset of SWE-bench designed to test whether models can resolve real GitHub issues, and lists Claude Opus 4.7 Adaptive at 87.6%. That source does not provide a directly comparable GPT-5.5 score, so it should not be used by itself to claim Claude definitely beats GPT-5.5 on SWE-bench Verified. The safer conclusion is that Claude Opus 4.7 is a very strong candidate for real software-engineering repair tasks.

For terminal, browsing and agent execution, GPT-5.5 has more public wins

GPT-5.5’s largest published advantage in this comparison is on Terminal-Bench 2.0: 82.7% versus 69.4%, a 13.3-point lead over Claude Opus 4.7.

It also leads on BrowseComp, GDPval and OSWorld-Verified, with scores of 84.4% versus 79.3%, 84.9% versus 80.3%, and 78.7% versus 78.0%, respectively.

For products that depend heavily on shell commands, browser navigation, file-system work, OS operations or multi-step automation, GPT-5.5 is therefore a natural first candidate.

There is an important caveat: this does not mean “pick GPT-5.5 for every agent task.” On MCP Atlas, Claude Opus 4.7 scores 79.1%, above GPT-5.5’s 75.3%; Anthropic’s own launch material also emphasizes improvements in tool calling and planning.

Professional work, reasoning and maths are split

Business and professional benchmarks do not point in just one direction. Vellum’s side-by-side table shows GPT-5.5 ahead on GDPval, 84.9% to 80.3%. Kingy AI’s summary, however, shows Claude Opus 4.7 ahead on FinanceAgent v1.1, 64.4% to 60.0%, while GPT-5.5 leads on OfficeQA Pro, 54.1% to 43.6%.

Reasoning and maths also depend on the task type. On GPQA Diamond, Claude Opus 4.7 scores 94.2% and GPT-5.5 scores 93.6%, a narrow 0.6-point Claude lead. On FrontierMath T1–3, GPT-5.5 scores 51.7% versus Claude Opus 4.7 at 43.8%, a 7.9-point GPT-5.5 lead.

Humanity’s Last Exam shows why caution matters when using third-party summaries. Kingy AI reports the no-tools result as GPT-5.5 at 41.4% and Claude Opus 4.7 at 46.9%; Mashable reports the no-tools result as GPT-5.5 at 40.6% and Claude Opus 4.7 at 31.2%. Because those public summaries disagree so sharply, this article does not treat that benchmark as core evidence for model selection.

How to choose: pick by workflow, not by “champion”

If your main workload is codebase-level repair, real GitHub issues, complex pull requests or patch generation, test Claude Opus 4.7 first. SWE-Bench Pro and SWE-bench Verified both support the view that it is strong in software-engineering repair.

If your main workload is terminal execution, web browsing, OS operations, automation agents or professional tasks represented by GDPval, test GPT-5.5 first. It has public side-by-side leads on Terminal-Bench 2.0, BrowseComp, OSWorld-Verified and GDPval.

If your workflow mixes coding, tool calls, long-horizon planning, document analysis and report generation, do not rely on one headline winner. GPT-5.5 leads several execution-oriented benchmarks, while Claude Opus 4.7 is stronger on SWE-Bench Pro, MCP Atlas and parts of Anthropic’s tool-planning narrative. Both should be on the shortlist.

What to test before putting either model into production

Public benchmarks are useful for narrowing the field. They should not be your final procurement or deployment decision.

Before going live, prepare a set of real tasks from your own workflow. Hide the model names, standardize the prompts, give each model the same tool permissions, context budget, time budget and scoring rubric. If you test GPT-5.5, also fix settings such as reasoning.effort, because OpenAI’s API documentation shows that the model supports that control.

Do not score only by average success rate. At minimum, track four outcomes: whether the task was completed, whether the answer was verifiable, how much human repair was needed, and the latency and call cost. In production, a model that makes fewer serious mistakes on your critical tasks can be more valuable than one that wins a few extra points on an unrelated benchmark.

The practical conclusion is straightforward: GPT-5.5 and Claude Opus 4.7 do not have an absolute winner. They have different strengths, and the better model is the one that fits your workflow.