GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6: 2026 benchmarks and model-picking guide

The short version: do not crown one overall winner

Ranking GPT-5.5, Claude Opus 4.7, DeepSeek V4 and Kimi K2.6 with a single combined score would be misleading. The public evidence does not put all four models through the same benchmark suite under the same evaluator. GPT-5.5 and Claude Opus 4.7 have several direct comparisons from Vellum and OpenAI, while DeepSeek V4 and Kimi K2.6 are better documented in the available sources for long context, open-weight or multimodal characteristics, and reliability signals from DeepSeek’s technical report and Artificial Analysis .

The safest reading is workload-by-workload. In direct comparisons, GPT-5.5 leads Claude Opus 4.7 on Terminal-Bench 2.0, 82.7% to 69.4%, and GDPval, 84.9% to 80.3%. Claude Opus 4.7 leads GPT-5.5 on SWE-Bench Pro, 64.3% to 58.6%, and GPQA Diamond, 94.2% to 93.6%, according to Vellum’s table .

For computer-use and tool-use evaluations, OpenAI reports GPT-5.5 at 78.7% on OSWorld-Verified versus 78.0% for Claude Opus 4.7, and 84.4% on BrowseComp versus 79.3%. Claude Opus 4.7 leads on MCP Atlas, 79.1% to GPT-5.5’s 75.3% .

For DeepSeek V4 and Kimi K2.6, the public evidence used here does not cover the same full benchmark set as GPT-5.5 and Claude Opus 4.7. That means missing scores should be read as missing evidence, not as poor performance .

Benchmark results that can be compared from the available sources

Benchmark or metric	GPT-5.5	Claude Opus 4.7	DeepSeek V4	Kimi K2.6	What it tells us
Terminal-Bench 2.0	82.7%	69.4%	No direct score found in the same source	No direct score found in the same source	GPT-5.5 leads Claude Opus 4.7 in Vellum’s table .
SWE-Bench Pro	58.6%	64.3%	No direct score found in the same source	No direct score found in the same source	Claude Opus 4.7 leads GPT-5.5 on this software-engineering benchmark .
GDPval	84.9%	80.3%	No direct score found in the same source	No direct score found in the same source	GPT-5.5 leads Claude Opus 4.7 in this set .
OSWorld-Verified	78.7%	78.0%	No direct score found in the same source	No direct score found in the same source	GPT-5.5 is slightly ahead in OpenAI’s table .
BrowseComp	84.4%	79.3%	No direct score found in the same source	No direct score found in the same source	GPT-5.5 leads in OpenAI’s tool-use data .
MCP Atlas	75.3%	79.1%	No direct score found in the same source	No direct score found in the same source	Claude Opus 4.7 leads GPT-5.5 in OpenAI’s table .
GPQA Diamond	93.6%	94.2%	No direct score found in the same source	No direct score found in the same source	Claude Opus 4.7 is slightly ahead in Vellum’s table .
FrontierMath T1–3	51.7%	43.8%	No direct score found in the same source	No direct score found in the same source	GPT-5.5 leads Claude Opus 4.7 in Vellum’s table .
Context window	Not in this Artificial Analysis comparison table	Not in this Artificial Analysis comparison table	DeepSeek V4 Pro: 1,000k tokens	256k tokens	DeepSeek V4 Pro has the larger context window in the same Artificial Analysis comparison .
AA-Omniscience / hallucination	No direct score found in the same source	No direct score found in the same source	V4 Pro Max scores -10; V4 Pro hallucination rate reported at 94%	No direct score found in the same source	A warning sign that DeepSeek V4 outputs need careful verification .
Artificial Analysis Intelligence Index	Not found in the sources used here	Not found in the sources used here	Not found in the sources used here	54	Useful as a Kimi K2.6-specific signal, not a shared leaderboard with Vellum or OpenAI .

“No direct score found” does not mean a model is worse. It means the sources used here did not provide that model’s score on the same benchmark under the same evaluator.

GPT-5.5: strongest evidence for agentic, terminal and tool workflows

GPT-5.5 has the broadest set of public numbers against Claude Opus 4.7 in this evidence set. Vellum reports Terminal-Bench 2.0, SWE-Bench Pro, GDPval, GPQA Diamond and FrontierMath results, while OpenAI reports OSWorld-Verified, BrowseComp and MCP Atlas results .

The clearest GPT-5.5 strength is in terminal-heavy and tool-heavy workflows. It leads Claude Opus 4.7 on Terminal-Bench 2.0, 82.7% to 69.4%; BrowseComp, 84.4% to 79.3%; and OSWorld-Verified, 78.7% to 78.0% . That makes it a strong candidate for agentic systems that need to operate tools, browse, run command-line tasks or coordinate multi-step work.

But GPT-5.5 does not win every category. Claude Opus 4.7 leads on SWE-Bench Pro, MCP Atlas and GPQA Diamond in the cited sources .

On safety and evaluation, OpenAI says GPT-5.5 was assessed with CoT-Control, an evaluation suite containing more than 13,000 tasks built from benchmarks such as GPQA, MMLU-Pro, HLE, BFCL and SWE-Bench Verified . That is relevant for understanding model controllability, but it should not be treated as a direct substitute for performance benchmark scores.

Claude Opus 4.7: the clearest signal is software engineering

Anthropic’s Claude API documentation lists Claude Opus 4.7 with a date of 16 April 2026 . In the direct benchmark figures available here, its strongest signal is software engineering: Claude Opus 4.7 scores 64.3% on SWE-Bench Pro, compared with 58.6% for GPT-5.5 .

Claude Opus 4.7 also leads GPT-5.5 on MCP Atlas, 79.1% to 75.3%, according to OpenAI’s table . At the same time, GPT-5.5 leads on OSWorld-Verified and BrowseComp in OpenAI’s data, and Vellum reports GPT-5.5 ahead on Terminal-Bench 2.0, GDPval and FrontierMath T1–3 .

For safety context, Anthropic’s Petri 2.0 report says two combined interventions produced a 47.3% median relative drop in eval-awareness on Claude models . That should be read as behavioral and safety evidence for the Claude family, not as a direct performance score for Claude Opus 4.7.

DeepSeek V4: huge context, but reliability needs controls

DeepSeek’s technical report says the V4 series retains the DeepSeekMoE framework and Multi-Token Prediction strategy from DeepSeek-V3, while adding hybrid attention to improve long-context efficiency . In Artificial Analysis data, DeepSeek V4 Pro has a 1,000k-token context window, compared with 256k tokens for Kimi K2.6 .

The major caution is reliability. Artificial Analysis reports that DeepSeek V4 Pro Max scores -10 on AA-Omniscience, an improvement from DeepSeek V3.2 Reasoning at -21, but also reports a 94% hallucination rate for DeepSeek V4 Pro and 96% for DeepSeek V4 Flash .

That makes DeepSeek V4 Pro worth considering when the task genuinely needs very long context, such as working across large document sets or workflows that must keep a great deal of information in view. For high-stakes or factual work, it should be paired with retrieval grounding, source checks, automated validation or human review .

Kimi K2.6: open-weight multimodal, but still needs more direct comparisons

Artificial Analysis describes Kimi K2.6 as an open-weights model released in April 2026 and gives it an Artificial Analysis Intelligence Index score of 54 . A separate Artificial Analysis write-up says Kimi K2.6 supports native image and video input with text output, and that its maximum context length remains 256k .

Against DeepSeek V4 Pro, the context-window comparison is straightforward: 256k tokens for Kimi K2.6 versus 1,000k tokens for DeepSeek V4 Pro in the same Artificial Analysis table . But the available sources used here do not provide Kimi K2.6 scores directly comparable with GPT-5.5 and Claude Opus 4.7 on Terminal-Bench 2.0, SWE-Bench Pro, GDPval, OSWorld-Verified or MCP Atlas .

The practical read: Kimi K2.6 belongs on the shortlist if you need an open-weight multimodal model, especially where native image or video input matters. It should not be declared better or worse than GPT-5.5, Claude Opus 4.7 or DeepSeek V4 in categories where the direct benchmark evidence is missing .

Which model should you choose?

Workload	Models to consider first	Evidence-based reason
Terminal automation and agentic command-line workflows	GPT-5.5	It leads Claude Opus 4.7 on Terminal-Bench 2.0, 82.7% to 69.4% .
Software engineering and issue solving	Claude Opus 4.7	It leads GPT-5.5 on SWE-Bench Pro, 64.3% to 58.6% .
Browser and tool workflows	GPT-5.5 or Claude Opus 4.7, depending on the tool stack	GPT-5.5 leads on BrowseComp, while Claude Opus 4.7 leads on MCP Atlas .
Computer-use workflows	GPT-5.5, narrowly	OSWorld-Verified is 78.7% for GPT-5.5 and 78.0% for Claude Opus 4.7 .
Very large long-context tasks	DeepSeek V4 Pro	Artificial Analysis lists a 1,000k-token context window, but its reported 94% hallucination rate means answers need extra checking .
Open-weight multimodal applications	Kimi K2.6	Artificial Analysis says it is open weights and supports native image/video input with text output .
Lowest-hallucination production choice	No overall winner can be named from this evidence set	DeepSeek V4 has a clear risk signal, but the sources do not provide one shared reliability comparison across all four models .

Benchmark caveats before you buy or build

Scores from different sources should not be merged into a single definitive ranking. Vellum, OpenAI and Artificial Analysis report different tests, different model settings and different evaluation contexts .

Coding benchmarks need special care. Academic work on real-world issue-solving evaluation notes that older benchmarks such as HumanEval have limitations, and that more realistic evaluations should consider issue-solving benchmarks such as SWE-Bench . That is one reason Claude Opus 4.7’s SWE-Bench Pro lead matters for engineering work, even though GPT-5.5 is stronger on other tests.

A large context window is also not the same thing as accuracy. DeepSeek V4 Pro’s 1,000k-token context window is impressive, but Artificial Analysis also reports a 94% hallucination rate for DeepSeek V4 Pro . In production, the best model is usually the one that passes your own internal evaluation suite for your data, your tools, your latency budget and your risk tolerance.

Bottom line

If you are choosing from the evidence available here, GPT-5.5 looks strongest for agentic, terminal and several tool-use workflows, with leads over Claude Opus 4.7 on Terminal-Bench 2.0, BrowseComp and OSWorld-Verified . Claude Opus 4.7 is the more compelling first pick for software-engineering issue solving where SWE-Bench Pro is the guiding benchmark, because it scores 64.3% versus GPT-5.5’s 58.6% .

DeepSeek V4 Pro is the standout for very long context at 1,000k tokens, but its reported 94% hallucination rate means it needs strong verification around it . Kimi K2.6 is an interesting open-weight multimodal candidate, with a 256k-token context window, native image/video input and an Intelligence Index score of 54, but it needs more direct benchmark coverage before a confident production ranking against GPT-5.5 and Claude Opus 4.7 is possible .