That makes the practical question less about which model is better and more about what kind of work you are asking it to do. A coding assistant that fixes GitHub-style issues is not the same product as a terminal agent, a web research agent or a math solver.

There is also a methodology caveat. Artificial Analysis compares GPT-5.5 in an xhigh configuration with Claude Opus 4.7 in a Non-reasoning, High Effort configuration, and LLM Stats says the benchmark numbers identify workloads rather than a single winner . Even when two sources use the same benchmark name, model mode, harness, tool stack and retry policy can change the result .

The benchmark picture at a glance

Coding: Claude wins SWE-bench Pro, GPT-5.5 wins the terminal

Coding is where a single headline score can be most misleading. On SWE-bench Pro, Claude Opus 4.7 is reported at 64.3%, compared with GPT-5.5 at 58.6% . Vellum describes that as a 5.7-point gap on real GitHub issue resolution, keeping the coding crown with Anthropic for that particular benchmark .

But Terminal-Bench 2.0 flips the story. The benchmark is described as measuring real command-line workflows, including multi-step tasks with file manipulation and script execution . Here, GPT-5.5 is reported at 82.7%, while Claude Opus 4.7 is at 69.4% . If your coding agent spends most of its time navigating a repo, running shell commands, editing files and recovering from tool output, GPT-5.5 deserves an early test.

Qualitative comparisons point in the same direction. Mindstudio says GPT-5.5 has a slight edge on tasks requiring precise tool use and file navigation, while Claude Opus 4.7 performs better when the work depends on broad architectural reasoning across large codebases . In other words, ask whether your coding workload is mainly about deciding what needs to change, or about operating reliably inside the development environment.

One coding benchmark deserves extra caution: SWE-bench Verified. APIYI and LLM Stats list Claude Opus 4.7 at 87.6%, but the provided material does not establish GPT-5.5’s score under the same conditions . Treat that as a Claude data point, not a clean head-to-head comparison.

Agents and tools: GPT-5.5 is broad, but not universal

For computer-use agents, the reported numbers are almost a tie. OpenAI’s GPT-5.5 launch material lists OSWorld-Verified at 78.7% for GPT-5.5 and 78.0% for Claude Opus 4.7 . The difference is small, but the public figure gives GPT-5.5 the narrow edge.

The gap is clearer on BrowseComp, a benchmark used for search and browsing-style agent work. OpenAI reports GPT-5.5 at 84.4%, GPT-5.5 Pro at 90.1% and Claude Opus 4.7 at 79.3% . If your product depends on gathering information from the web, following browser-like workflows or handling research tasks, GPT-5.5 is the more obvious first candidate.

But MCP Atlas shows why agent benchmarks should be separated by tool type. On that tool-use benchmark, Claude Opus 4.7 is reported at 79.1%, ahead of GPT-5.5 at 75.3% . The safer evaluation plan is to test browser search, GUI computer use, MCP-style tool calls and terminal automation separately rather than collapsing them into one agent score.

Reasoning: Claude for GPQA, GPT-5.5 for FrontierMath

On GPQA Diamond, a difficult science and expert-knowledge benchmark, Claude Opus 4.7 is reported at 94.2–94.3%, while GPT-5.5 is reported at 93.6% . The margin is small, but on the supplied evidence Claude has the edge.

Math tells a different story. On FrontierMath T1-3, GPT-5.5 is reported at 51.7%, compared with Claude Opus 4.7 at 43.8%; on the harder FrontierMath T4, GPT-5.5 is at 35.4% while Claude is at 22.9% . For formal math, verification-heavy problem solving or workloads where calculation accuracy is the main failure mode, GPT-5.5 should be tested first.

Humanity’s Last Exam is not a clean tiebreaker

Humanity’s Last Exam, often shortened to HLE, is the messiest part of this comparison. Mashable reports a no-tools result of 40.6% for GPT-5.5 and 31.2% for Claude Opus 4.7, which would favor GPT-5.5 . But o-mega and RDWorld report no-tools figures of 41.4% for GPT-5.5 and 46.9% for Claude Opus 4.7, which would favor Claude .

With tools enabled, Mashable and RDWorld both report GPT-5.5 at 52.2% and Claude Opus 4.7 at 54.7%, giving Claude a small lead . Because the no-tools numbers diverge so much by source, HLE should not be used as the deciding benchmark unless you can verify the exact setup.

Context windows, cost and leaderboards are useful, not decisive

The context-window story is also source-dependent. Artificial Analysis lists GPT-5.5 at 922k tokens and Claude Opus 4.7 at 1,000k tokens . LLM Stats, however, describes both as 1M-token context models released at the same input price . For planning purposes, it is fair to treat both as long-context models, but production teams should recheck the exact limits, pricing and behavior for their API tier, model mode and tool setup.

Leaderboards reinforce the same point: both models are near the top. BenchLM ranks Claude Opus 4.7 second out of 110 models on its provisional leaderboard and second out of 14 on its verified leaderboard . BenchLM ranks GPT-5.5 fifth out of 112 models on its provisional leaderboard and second out of 16 on its verified leaderboard . That is useful signal, but it does not replace testing for latency, cost, tool-call reliability and the kinds of mistakes your users actually care about.

Which model should you test first?

Start with Claude Opus 4.7 if your workload looks like this:

• Real issue resolution and code-fixing tasks similar to SWE-bench Pro
• Large-codebase architecture, refactoring or code review where broad structural reasoning matters
• Specialist science or expert-knowledge questions similar to GPQA Diamond
• Tool-use workloads where MCP Atlas-style behavior is a priority

Start with GPT-5.5 if your workload looks like this:

• Terminal or CLI automation with file operations, scripts and multi-step shell workflows
• Computer-use tasks similar to OSWorld-Verified
• Search, browsing or web research agents similar to BrowseComp
• Hard math tasks similar to FrontierMath

Bottom line

Claude Opus 4.7 looks strongest on SWE-bench Pro, GPQA Diamond and MCP Atlas . GPT-5.5 looks strongest on Terminal-Bench 2.0, OSWorld-Verified, BrowseComp and FrontierMath .

So the decision is not Claude Opus 4.7 versus GPT-5.5 in the abstract. It is code repair versus terminal control, scientific Q&A versus math, MCP-style tool use versus browsing agents. If the stakes are high, run a small bake-off with the same prompts, tools, retry budget and acceptance criteria before standardizing on either model.

That makes the practical question less about which model is better and more about what kind of work you are asking it to do. A coding assistant that fixes GitHub-style issues is not the same product as a terminal agent, a web research agent or a math solver.

There is also a methodology caveat. Artificial Analysis compares GPT-5.5 in an xhigh configuration with Claude Opus 4.7 in a Non-reasoning, High Effort configuration, and LLM Stats says the benchmark numbers identify workloads rather than a single winner . Even when two sources use the same benchmark name, model mode, harness, tool stack and retry policy can change the result .

The benchmark picture at a glance

Coding: Claude wins SWE-bench Pro, GPT-5.5 wins the terminal

Coding is where a single headline score can be most misleading. On SWE-bench Pro, Claude Opus 4.7 is reported at 64.3%, compared with GPT-5.5 at 58.6% . Vellum describes that as a 5.7-point gap on real GitHub issue resolution, keeping the coding crown with Anthropic for that particular benchmark .

But Terminal-Bench 2.0 flips the story. The benchmark is described as measuring real command-line workflows, including multi-step tasks with file manipulation and script execution . Here, GPT-5.5 is reported at 82.7%, while Claude Opus 4.7 is at 69.4% . If your coding agent spends most of its time navigating a repo, running shell commands, editing files and recovering from tool output, GPT-5.5 deserves an early test.

Qualitative comparisons point in the same direction. Mindstudio says GPT-5.5 has a slight edge on tasks requiring precise tool use and file navigation, while Claude Opus 4.7 performs better when the work depends on broad architectural reasoning across large codebases . In other words, ask whether your coding workload is mainly about deciding what needs to change, or about operating reliably inside the development environment.

One coding benchmark deserves extra caution: SWE-bench Verified. APIYI and LLM Stats list Claude Opus 4.7 at 87.6%, but the provided material does not establish GPT-5.5’s score under the same conditions . Treat that as a Claude data point, not a clean head-to-head comparison.

Agents and tools: GPT-5.5 is broad, but not universal

For computer-use agents, the reported numbers are almost a tie. OpenAI’s GPT-5.5 launch material lists OSWorld-Verified at 78.7% for GPT-5.5 and 78.0% for Claude Opus 4.7 . The difference is small, but the public figure gives GPT-5.5 the narrow edge.

The gap is clearer on BrowseComp, a benchmark used for search and browsing-style agent work. OpenAI reports GPT-5.5 at 84.4%, GPT-5.5 Pro at 90.1% and Claude Opus 4.7 at 79.3% . If your product depends on gathering information from the web, following browser-like workflows or handling research tasks, GPT-5.5 is the more obvious first candidate.

But MCP Atlas shows why agent benchmarks should be separated by tool type. On that tool-use benchmark, Claude Opus 4.7 is reported at 79.1%, ahead of GPT-5.5 at 75.3% . The safer evaluation plan is to test browser search, GUI computer use, MCP-style tool calls and terminal automation separately rather than collapsing them into one agent score.

Reasoning: Claude for GPQA, GPT-5.5 for FrontierMath

On GPQA Diamond, a difficult science and expert-knowledge benchmark, Claude Opus 4.7 is reported at 94.2–94.3%, while GPT-5.5 is reported at 93.6% . The margin is small, but on the supplied evidence Claude has the edge.

Math tells a different story. On FrontierMath T1-3, GPT-5.5 is reported at 51.7%, compared with Claude Opus 4.7 at 43.8%; on the harder FrontierMath T4, GPT-5.5 is at 35.4% while Claude is at 22.9% . For formal math, verification-heavy problem solving or workloads where calculation accuracy is the main failure mode, GPT-5.5 should be tested first.

Humanity’s Last Exam is not a clean tiebreaker

Humanity’s Last Exam, often shortened to HLE, is the messiest part of this comparison. Mashable reports a no-tools result of 40.6% for GPT-5.5 and 31.2% for Claude Opus 4.7, which would favor GPT-5.5 . But o-mega and RDWorld report no-tools figures of 41.4% for GPT-5.5 and 46.9% for Claude Opus 4.7, which would favor Claude .

With tools enabled, Mashable and RDWorld both report GPT-5.5 at 52.2% and Claude Opus 4.7 at 54.7%, giving Claude a small lead . Because the no-tools numbers diverge so much by source, HLE should not be used as the deciding benchmark unless you can verify the exact setup.

Context windows, cost and leaderboards are useful, not decisive

The context-window story is also source-dependent. Artificial Analysis lists GPT-5.5 at 922k tokens and Claude Opus 4.7 at 1,000k tokens . LLM Stats, however, describes both as 1M-token context models released at the same input price . For planning purposes, it is fair to treat both as long-context models, but production teams should recheck the exact limits, pricing and behavior for their API tier, model mode and tool setup.

Leaderboards reinforce the same point: both models are near the top. BenchLM ranks Claude Opus 4.7 second out of 110 models on its provisional leaderboard and second out of 14 on its verified leaderboard . BenchLM ranks GPT-5.5 fifth out of 112 models on its provisional leaderboard and second out of 16 on its verified leaderboard . That is useful signal, but it does not replace testing for latency, cost, tool-call reliability and the kinds of mistakes your users actually care about.

Which model should you test first?

Start with Claude Opus 4.7 if your workload looks like this:

• Real issue resolution and code-fixing tasks similar to SWE-bench Pro
• Large-codebase architecture, refactoring or code review where broad structural reasoning matters
• Specialist science or expert-knowledge questions similar to GPQA Diamond
• Tool-use workloads where MCP Atlas-style behavior is a priority

Start with GPT-5.5 if your workload looks like this:

• Terminal or CLI automation with file operations, scripts and multi-step shell workflows
• Computer-use tasks similar to OSWorld-Verified
• Search, browsing or web research agents similar to BrowseComp
• Hard math tasks similar to FrontierMath

Bottom line

Claude Opus 4.7 looks strongest on SWE-bench Pro, GPQA Diamond and MCP Atlas . GPT-5.5 looks strongest on Terminal-Bench 2.0, OSWorld-Verified, BrowseComp and FrontierMath .

So the decision is not Claude Opus 4.7 versus GPT-5.5 in the abstract. It is code repair versus terminal control, scientific Q&A versus math, MCP-style tool use versus browsing agents. If the stakes are high, run a small bake-off with the same prompts, tools, retry budget and acceptance criteria before standardizing on either model.