Claude Opus 4.7 vs GPT-5.5: what the 2026 benchmarks really show

The cleanest head-to-head numbers

The table below only pairs Claude Opus 4.7 and GPT-5.5 on the same named benchmark. GPT-5.5 Pro is included only where the source lists it as a separate variant ^[2].

How to read the benchmarks without overclaiming

1. Do not mix SWE-Bench Pro with SWE-bench Verified

OpenAI uses SWE-Bench Pro Public in its GPT-5.5 vs Claude Opus 4.7 table ^[5]. That is not the same thing as SWE-bench Verified. BenchLM describes SWE-bench Verified as a curated, human-verified subset of SWE-bench that tests models on real GitHub issues from popular Python repositories such as Django, Flask and scikit-learn ^[21].

That means Claude's 64.3% on SWE-Bench Pro Public should not be compared directly with Claude scores on SWE-bench Verified leaderboards unless the benchmark variant, harness, evaluation date and model configuration are aligned ^[5][21].

2. GPQA Diamond is no longer a sharp separator

Vellum reports Claude Opus 4.7 at 94.2% and GPT-5.5 at 93.6% on GPQA Diamond ^[2]. The Next Web also reported a tight cluster on the same benchmark, with Claude Opus 4.7 at 94.2%, GPT-5.4 Pro at 94.4% and Gemini 3.1 Pro at 94.3%, and described those differences as within noise ^[17].

For model selection, GPQA is still a useful reasoning signal. It just should not be the deciding metric on its own when frontier models are separated by fractions of a point.

3. Third-party leaderboards can disagree

SWE-bench Verified numbers for Claude Opus 4.7 vary by source. BenchLM lists Claude Opus 4.7 Adaptive at 87.6% as of April 24, 2026 ^[21]. LLM Stats also reports 87.6% ^[18]. LM Council shows Claude Opus 4.7 max at 83.5% ± 1.7 ^[10], while MindStudio gives 82.4% ^[14].

That spread does not automatically mean one table is wrong. It usually reflects differences in model settings, evaluation harnesses, dates, retry handling, reasoning modes or leaderboard rules. For engineering teams, public leaderboards are best treated as a shortlist, not a substitute for testing on your own repositories, tools and workflows.

Where Claude Opus 4.7 looks strongest

The strongest signals for Claude Opus 4.7 are code repair and tool orchestration. In OpenAI's table, Claude beats GPT-5.5 on SWE-Bench Pro Public, 64.3% to 58.6%, and on FinanceAgent v1.1, 64.4% to 60.0% ^[5]. Vellum also reports Claude ahead on MCP Atlas, 79.1% to GPT-5.5's 75.3% ^[2].

Anthropic's own launch note points to partner evidence around agentic workflows. It cites Hebbia seeing a double-digit jump in tool-call accuracy and planning in orchestrator agents, and Rakuten-SWE-Bench reporting that Opus 4.7 resolved three times as many production tasks as Opus 4.6, with double-digit gains in Code Quality and Test Quality ^[19]. Those are useful product signals, but they are not the same as an independent evaluation on your own production stack.

The practical read: if your priority is autonomous repo repair, MCP-style tool orchestration or long multi-tool workflows, Claude Opus 4.7 deserves an early slot in your evaluation. Still, validate it against your test suites, permission model and real tool-calling patterns.

Where GPT-5.5 looks strongest

GPT-5.5's clearest lead is Terminal-Bench 2.0. OpenAI reports GPT-5.5 at 82.7%, compared with Claude Opus 4.7 at 69.4% and Gemini 3.1 Pro at 68.5% ^[5]. In the same OpenAI table, GPT-5.5 also leads Claude on GDPval wins/ties, 84.9% to 80.3%, and OfficeQA Pro, 54.1% to 43.6% ^[5].

Vellum adds more context for computer use, browser/search and reasoning. It reports GPT-5.5 slightly ahead of Claude on OSWorld-Verified, 78.7% to 78.0%; ahead on BrowseComp, 84.4% to 79.3%; and ahead on FrontierMath T1–3, 51.7% to 43.8% ^[2]. For BrowseComp, Vellum also lists GPT-5.5 Pro at 90.1% ^[2].

The coding picture is mixed rather than one-sided. GPT-5.5 is very strong on Terminal-Bench 2.0, but trails Claude Opus 4.7 on SWE-Bench Pro Public in OpenAI's head-to-head table ^[5]. OpenAI's system card separately describes CoT-Control, a controllability suite with more than 13,000 tasks drawn from GPQA, MMLU-Pro, HLE, BFCL and SWE-Bench Verified, but that source does not provide a direct comparison with DeepSeek V4 or Kimi K2.6 ^[26].

DeepSeek V4 and Kimi K2.6: the evidence gap

For DeepSeek V4, the cited sources do not provide direct benchmark scores. The closest data point is for DeepSeek V3.2: MangoMind places DeepSeek V3.2 in an April 2026 coding recommendation list with 89.2% on SWE-bench, below Claude Opus 4.6 at 93.2% and GPT-5.4 Pro at 91.1% ^[1]. That cannot be used to infer DeepSeek V4's performance.

Kimi K2.6 has the same problem. Stanford HAI says KimiK2.5 was among models grouped between 70% and 76% on SWE-bench Verified as of February 2026 ^[13]. Siliconflow lists Kimi K2 Thinking with GPQA 84.5 and SWE Bench 71.3 ^[6]. Those figures are useful context for the Kimi ecosystem, but they are not benchmark evidence for Kimi K2.6.

What to test first

Bottom line

If you only use the direct head-to-head evidence in these sources, GPT-5.5 is the stronger first candidate for terminal/CLI agents, browser/search agents, office-style professional tasks and FrontierMath-style math evaluations ^[5][2]. Claude Opus 4.7 is the stronger first candidate for SWE-Bench Pro Public, MCP Atlas tool orchestration and FinanceAgent v1.1 ^[5][2].

DeepSeek V4 and Kimi K2.6 should not be forced into the same ranking yet. The available data refers to DeepSeek V3.2, KimiK2.5 and Kimi K2 Thinking, so claims that DeepSeek V4 or Kimi K2.6 beat Claude Opus 4.7 or GPT-5.5 are not supported by direct benchmark numbers in the cited sources ^[1][13][6].