GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6: benchmark guide by task

AI benchmarks are best read as a map, not a league table. The practical question is not which model wins everything, but which model wins the kind of work you actually need done.

Based on the available source-backed data, the safest conclusion is task-specific: Claude Opus 4.7 looks strongest for hard no-tool reasoning and SWE-Bench Pro; GPT-5.5 Pro stands out for tool use and browsing; GPT-5.5 has the clearest terminal-work advantage; DeepSeek V4 is compelling on cost/performance but needs hallucination controls; and Kimi K2.6 has useful individual signals but not a full like-for-like comparison matrix against all rivals .

Benchmark comparison at a glance

A dash means the cited source did not report a like-for-like score for that model on that benchmark. It does not mean the model scored zero.

Benchmark	GPT-5.5	GPT-5.5 Pro	Claude Opus 4.7	DeepSeek-V4-Pro-Max	Kimi K2.6	Best-supported signal
GPQA Diamond	93.6%	—	94.2%	90.1%	No direct VentureBeat score; LLM Stats lists GPQA at 0.91	Claude Opus 4.7 in the direct VentureBeat table
Humanity’s Last Exam, no tools	41.4%	43.1%	46.9%	37.7%	—	Claude Opus 4.7
Humanity’s Last Exam, with tools	52.2%	57.2%	54.7%	48.2%	—	GPT-5.5 Pro
Terminal-Bench 2.0	82.7%	—	69.4%	67.9%	—	GPT-5.5
SWE-Bench Pro / SWE Pro	58.6%	—	64.3%	55.4%	LLM Stats lists 0.59	Claude Opus 4.7
BrowseComp	84.4%	90.1%	79.3%	83.4%	DocsBot lists 83.2%	GPT-5.5 Pro in the VentureBeat table
MCP Atlas / MCPAtlas Public	75.3%	—	79.1%	73.6%	—	Claude Opus 4.7

The pattern is clear: the top model changes as soon as the task changes. That is why a single overall ranking would be misleading.

Hard reasoning: Claude Opus 4.7 has the edge

For difficult no-tool reasoning, Claude Opus 4.7 is the strongest model in the direct VentureBeat comparison. It scores 94.2% on GPQA Diamond, ahead of GPT-5.5 at 93.6% and DeepSeek-V4-Pro-Max at 90.1% . The margin over GPT-5.5 is small, but Claude is still the top entry in that table .

Claude Opus 4.7 also leads Humanity’s Last Exam without tools, scoring 46.9% versus GPT-5.5 Pro at 43.1%, GPT-5.5 at 41.4% and DeepSeek-V4-Pro-Max at 37.7% . If your workload is mainly hard scientific reasoning, expert-level Q&A or tests where the model cannot lean on external tools, the available evidence points first to Claude Opus 4.7 .

Kimi K2.6 has a separate GPQA signal: LLM Stats lists it at 0.91, while Claude Opus 4.7 and GPT-5.5 are both listed at a rounded 0.94 on that leaderboard . That is useful context, but it should not be treated as the same thing as the VentureBeat GPQA Diamond comparison .

Tools, web and browsing: GPT-5.5 Pro moves ahead

Once tools are allowed, the ranking shifts. On Humanity’s Last Exam with tools, GPT-5.5 Pro scores 57.2%, ahead of Claude Opus 4.7 at 54.7%, GPT-5.5 at 52.2% and DeepSeek-V4-Pro-Max at 48.2% .

The same pattern appears in BrowseComp. In VentureBeat’s table, GPT-5.5 Pro reaches 90.1%, compared with GPT-5.5 at 84.4%, DeepSeek-V4-Pro-Max at 83.4% and Claude Opus 4.7 at 79.3% . DocsBot separately lists Kimi K2.6 at 83.2% on BrowseComp, but that figure comes from a Kimi-versus-DeepSeek comparison page rather than the same full VentureBeat matrix .

For web research, browsing-heavy agents and workflows that depend on tool orchestration, GPT-5.5 Pro is the standout choice in the cited data .

Terminal and agentic CLI: GPT-5.5 is the clearest winner

Terminal-Bench 2.0 matters when the model has to do work in a shell, not just answer questions. It is described as measuring real CLI workflows, including file manipulation, script execution, debugging and tool coordination .

Here, GPT-5.5 has the strongest signal: 82.7% on Terminal-Bench 2.0, well ahead of Claude Opus 4.7 at 69.4% and DeepSeek-V4-Pro-Max at 67.9% . If the use case is repo automation, shell-based debugging, command-line agents or multi-step developer workflows, GPT-5.5 has the clearest advantage in the available data .

Software engineering: Claude Opus 4.7 leads SWE-Bench Pro

SWE-Bench Pro is especially relevant for complex software engineering. LLM Stats describes it as an advanced version of SWE-Bench that evaluates real-world software engineering tasks requiring extended reasoning and multi-step problem solving .

In VentureBeat’s comparison, Claude Opus 4.7 scores 64.3% on SWE-Bench Pro / SWE Pro, ahead of GPT-5.5 at 58.6% and DeepSeek-V4-Pro-Max at 55.4% . LLM Stats points in the same direction, listing Claude Opus 4.7 at 0.64, GPT-5.5 at 0.59, Kimi K2.6 at 0.59 and DeepSeek-V4-Pro-Max at 0.55 on SWE-Bench Pro .

The takeaway is straightforward: for the harder software-engineering benchmark in these sources, Claude Opus 4.7 leads; GPT-5.5 and Kimi K2.6 are close in the LLM Stats listing; and DeepSeek-V4-Pro-Max trails those entries .

DeepSeek V4: the case is price/performance, not raw wins

DeepSeek-V4-Pro-Max does not lead any row in the direct VentureBeat benchmark table. It scores 90.1% on GPQA Diamond, 37.7% on Humanity’s Last Exam without tools, 48.2% on Humanity’s Last Exam with tools, 67.9% on Terminal-Bench 2.0, 55.4% on SWE-Bench Pro, 83.4% on BrowseComp and 73.6% on MCP Atlas .

Its appeal is cost/performance. VentureBeat describes DeepSeek V4 as near state-of-the-art at about one-sixth the cost of Opus 4.7 and GPT-5.5 . For teams running large volumes of inference, that can be a serious consideration.

But reliability needs testing. Artificial Analysis reports that DeepSeek V4 Pro Max scores -10 on AA-Omniscience, improving 11 points over V3.2 Reasoning at -21, while also saying V4 Pro and V4 Flash have very high hallucination rates of 94% and 96% respectively . That does not prove DeepSeek is the least reliable model in this whole group, because the cited sources do not provide the same hallucination metric for GPT-5.5, Claude Opus 4.7 and Kimi K2.6 . A safer conclusion is that DeepSeek V4 may be attractive when cost is the priority, but it should be tested against your own data and failure modes before production use .

Kimi K2.6: promising, but not fully comparable here

Kimi K2.6 is the hardest model to rank in this comparison because it is not included in the same full benchmark matrix as GPT-5.5, GPT-5.5 Pro, Claude Opus 4.7 and DeepSeek-V4-Pro-Max .

The separate signals are still worth noting. LLM Stats lists Kimi K2.6 at 0.91 on GPQA and 0.59 on SWE-Bench Pro . DocsBot lists Kimi K2.6 at 96.4% on AIME 2026 in thinking mode, 27.9% on APEX Agents and 83.2% on BrowseComp; the same DocsBot page lists DeepSeek-V4 Pro at 83.4% on BrowseComp .

Those numbers make Kimi K2.6 a model worth testing, especially if its reported strengths match your workload. But they do not support a clean claim that Kimi wins or loses overall against the full group .

Which model should you choose?

• Hard scientific reasoning or expert Q&A without tools: start with Claude Opus 4.7, which leads GPQA Diamond and Humanity’s Last Exam without tools in the direct comparison .
• Tool use, browsing and web-heavy research: prioritize GPT-5.5 Pro, which leads Humanity’s Last Exam with tools and BrowseComp in the cited data .
• Terminal agents and shell workflows: choose GPT-5.5 first, because it posts the strongest Terminal-Bench 2.0 score among the directly compared models .
• Complex software engineering: Claude Opus 4.7 is the strongest supported choice on SWE-Bench Pro across the cited VentureBeat and LLM Stats data .
• Cost-sensitive deployments: consider DeepSeek V4, which is described as near state-of-the-art at roughly one-sixth the cost of Opus 4.7 and GPT-5.5, but validate hallucination behavior carefully .
• Kimi K2.6 evaluation: treat its GPQA, SWE-Bench Pro, AIME 2026, APEX Agents and BrowseComp figures as useful individual signals, then run your own workload-specific tests .

Benchmark caveats that matter

First, GPT-5.5 Pro is only reported on some rows in the VentureBeat table, so missing scores should not be interpreted as wins or losses . Second, Kimi K2.6 data mostly comes from separate LLM Stats and DocsBot pages, not from the same full direct comparison matrix .

Third, OpenAI’s GPT-5.5 system card describes CoT-Control, an evaluation suite with more than 13,000 tasks built from GPQA, MMLU-Pro, HLE, BFCL and SWE-Bench Verified . That is useful context for how GPT-5.5 is evaluated, but the cited sources do not provide equivalent CoT-Control results for Claude Opus 4.7, DeepSeek V4 and Kimi K2.6, so it cannot be used as a cross-model ranking .

Bottom line: Claude Opus 4.7 is the best-supported choice here for hard no-tool reasoning and SWE-Bench Pro; GPT-5.5 Pro is strongest for tool-enabled and browsing tasks; GPT-5.5 is the clearest pick for terminal workflows; DeepSeek V4 is the cost/performance candidate with hallucination caveats; and Kimi K2.6 deserves testing but lacks a unified comparison matrix in the cited evidence .