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

“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?

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 .

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

“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?

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 .