GPT-5.5 vs. Claude Opus 4.7: Which Benchmarks Actually Matter?

LLM Stats summarizes the matchup this way: across 10 benchmarks that it says both providers report, Claude Opus 4.7 leads on six and GPT-5.5 leads on four. It also says Claude’s advantages cluster around reasoning-heavy and review-grade tests, while GPT-5.5’s advantages cluster around long-running tool use and shell-driven tasks . That is a useful pattern, but it does not resolve rows where public data conflicts, such as Humanity’s Last Exam .

Coding: Terminal-Bench and SWE-Bench are not measuring the same thing

If your product is an agentic coding tool that lives in the terminal, GPT-5.5 is the stronger first candidate based on the public numbers. It scores 82.7% on Terminal-Bench 2.0, compared with 69.4% for Claude Opus 4.7 in the side-by-side tables . OpenAI describes Terminal-Bench 2.0 as a benchmark for complex command-line workflows requiring planning, iteration and tool coordination .

That matters for CLI copilots, DevOps assistants, test-running agents and systems that must read errors, edit files, run commands and loop until a task is done. For that class of workload, Terminal-Bench 2.0 is more relevant than a broad reasoning score.

For real-world software issue fixing, however, Claude Opus 4.7 has the public edge on SWE-Bench Pro: 64.3% versus 58.6% for GPT-5.5 . OpenAI describes SWE-Bench Pro as an evaluation of real GitHub issue resolution . If your workload looks more like bug fixing, codebase changes or review-grade software engineering than long terminal sessions, Claude Opus 4.7 belongs in the first test round.

SWE-Bench Verified is less clean in the provided source set. MindStudio reports Claude Opus 4.7 at 82.4%, while APIyi and DataCamp report 87.6%; the provided sources do not give a stable GPT-5.5-versus-Claude Opus 4.7 pair for that exact row .

Agents and workflow automation: GPT-5.5 leads several tests, but Claude still has strong zones

For agentic workflows, GPT-5.5 has several positive signals. In Vellum’s table, it leads BrowseComp at 84.4% versus 79.3%, GDPval at 84.9% versus 80.3%, and OSWorld-Verified at 78.7% versus 78.0% . Mashable also reports the same BrowseComp pair: 84.4% for GPT-5.5 and 79.3% for Claude Opus 4.7 . LLM Stats adds that GPT-5.5 leads CyberGym, although the provided snippet does not show the percentage scores .

Claude Opus 4.7 still has important territory. In Vellum’s table, Claude leads MCP Atlas at 79.1% versus 75.3% for GPT-5.5 . LLM Stats says Claude leads FinanceAgent v1.1, and DataCamp reports Claude Opus 4.7 at 64.4% on FinanceAgent v1.1 . Anthropic also describes Claude Opus 4.7 as stronger across coding, agents, vision and multi-step tasks .

The practical takeaway: if your workflow is heavy on shell use, browsing or OS-style automation, GPT-5.5 has the better starting signal. If it depends on structured orchestration, MCP-style tool use or finance-agent tasks, Claude Opus 4.7 should be benchmarked directly rather than screened out early.

Reasoning: GPQA is close, HLE is messy

On GPQA Diamond, Claude Opus 4.7 scores 94.2% and GPT-5.5 scores 93.6% in the comparison tables . That is a Claude lead, but the margin is only 0.6 percentage points. For scientific QA, expert analysis or long-form reasoning, the better decision is to run both models on your own question set.

Humanity’s Last Exam is the benchmark to treat most carefully in this comparison. LLM Stats says Claude Opus 4.7 leads both HLE without tools and HLE with tools . Mashable, however, reports GPT-5.5 at 40.6% versus 31.2% for Opus 4.7 on HLE without tools, while Claude leads 54.7% to 52.2% on HLE with tools . o-mega gives another set of HLE numbers again . When public sources disagree like this, HLE should not be the deciding benchmark unless you rerun it under a controlled setup.

So which model should you try first?

Start with GPT-5.5 if your priority is terminal-based agents, shell workflows, test loops or multi-step automation. Terminal-Bench 2.0 leans strongly toward GPT-5.5 . GPT-5.5 is also a strong early candidate for browsing/search-style tasks, GDPval, OSWorld-Verified and FrontierMath T1–3 based on Vellum and Mashable’s tables .

Start with Claude Opus 4.7 if your priority is software issue resolution in the style of SWE-Bench Pro, where Claude leads GPT-5.5 . Claude should also be on the shortlist for GPQA-style scientific reasoning, MCP/tool orchestration and finance-agent workflows, based on GPQA Diamond, MCP Atlas, FinanceAgent v1.1 and LLM Stats’ summary .

The safest approach is not to choose from a leaderboard alone. Split your evaluation into four buckets: repository coding, terminal/agent automation, no-tool reasoning and tool-using workflows. For each bucket, run the same prompts, tool permissions, sampling settings, reasoning effort and scoring rubric. Public benchmarks tell you where to begin; your own eval tells you which model belongs in production, especially when some public scores may be self-reported or not independently verified .