Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6: What the 2026 Benchmarks Really Say

That separation matters because some older headline tests are running out of room at the top. Nanonets explains that MMLU is calculated with 5-shot prompting and that, by 2026, top models are clustered above roughly 88%, which makes the benchmark less useful for separating frontier models . In other words, a one-point edge on a saturated test may matter less than a clear lead on the benchmark closest to your workflow.

Public benchmark snapshot

Overall leaderboards: Claude has the cleanest BenchLM lead

Among the three models with BenchLM pages in this source set, Claude Opus 4.7 posts the strongest overall score. BenchLM lists it at 97/100, #2 of 110 on the provisional leaderboard and #2 of 14 on the verified leaderboard .

GPT-5.5 is also in the top tier on BenchLM, but its displayed overall score is lower: 89/100, #5 of 112 on the provisional leaderboard and #2 of 16 on the verified leaderboard . Kimi 2.6 appears lower still on the same site, at 85/100, #12 of 115, with 27 published benchmark scores shown .

That does not make BenchLM a universal ranking of all four models. The comparison pools are not identical, and the DeepSeek numbers available in this source set come primarily from separate DeepSeek V4-Pro-Max reporting rather than an equivalent BenchLM entry .

Coding: Claude Opus 4.7 has the clearest published signal

For software engineering, Claude Opus 4.7 has the most direct public evidence here. MindStudio reports Claude Opus 4.7 at 82.4% on SWE-bench Verified, roughly 11 points higher than Opus 4.6, and presents SWE-bench Verified as the most meaningful coding benchmark in that breakdown . The same source reports FinanceBench at 82.7% and a 9.5-point improvement on MathVista for visual math reasoning .

GPT-5.5 may still be relevant to engineering teams, but the OpenAI figures included here emphasize professional work and computer-use agents rather than a headline SWE-bench score: GDPval 84.9%, OSWorld-Verified 78.7% and Tau2-bench Telecom 98.0% .

For Kimi K2.6, GMI Cloud says the model tops SWE-Bench Pro and can run 300 parallel sub-agents on 4x H100S, but the provided snippet does not give a precise score or a same-conditions comparison against the other three models . For DeepSeek V4, the most concrete numbers in this source set are stronger on reasoning and math than on coding .

Work agents and computer use: GPT-5.5 has the most specific official metrics

If your use case is knowledge work, desktop or browser control, or customer-service automation, GPT-5.5’s official benchmark set is especially relevant. OpenAI says GPT-5.5 scores 84.9% on GDPval, a benchmark for producing well-specified knowledge-work outputs across 44 occupations . It also reports 78.7% on OSWorld-Verified, which tests whether a model can operate real computer environments, and 98.0% on Tau2-bench Telecom, which measures complex customer-service workflows without prompt tuning .

Claude Opus 4.7 has agentic evidence too, but it is in a different format. Anthropic says its internal research-agent benchmark gives Claude Opus 4.7 a tied top overall score of 0.715 across six modules, with a General Finance score of 0.813 versus 0.767 for Opus 4.6 .

Those are useful signals, not directly comparable scores. GPT-5.5’s 84.9% on GDPval and Claude Opus 4.7’s 0.715 on Anthropic’s internal research-agent benchmark are not the same scale, not the same task set and not evidence for a simple head-to-head ranking .

Reasoning and knowledge: DeepSeek and Kimi can be compared on some shared rows

The clearest DeepSeek V4 numbers here are for the V4-Pro-Max setting. DataCamp, citing DeepSeek internal results, reports 87.5% on MMLU-Pro, 90.1% on GPQA Diamond and 92.6% on GSM8K for math . These are strong reference points, but the internal-results label matters when comparing them with third-party leaderboards .

A Hugging Face table for DeepSeek-V4-Pro puts DeepSeek V4-Pro-Max and Kimi K2.6 Thinking side by side on several knowledge and reasoning rows .

On this table alone, DeepSeek V4-Pro-Max leads Kimi K2.6 Thinking on MMLU-Pro, SimpleQA-Verified, Chinese-SimpleQA and HLE, while Kimi is slightly higher on GPQA Diamond . The table does not settle the four-way race, however, because its other comparison columns are Opus-4.6 Max and GPT-5.4 xHigh rather than Claude Opus 4.7 and GPT-5.5 .

Cost and operating profile: Kimi K2.6 is the one to watch

For teams that care about open weights and operating economics, Kimi K2.6 stands out for a different reason. Artificial Analysis describes Moonshot’s Kimi K2.6 as the leading open weights model and places it #4 overall on the Artificial Analysis Intelligence Index with a score of 54 .

Vals AI also gives Kimi K2.6 useful operating data: Accuracy 63.94% ± 1.97, Latency 373.57s and Cost/Test $0.21 . GPT-5.5’s Vals entry shows higher Accuracy at 67.76% ± 1.79 and Latency of 409.09s, with a 1M context window . On those Vals entries alone, GPT-5.5 has the higher displayed accuracy, while Kimi K2.6 has the lower displayed latency and an explicit cost-per-test figure .

Do not average these into one master number. Artificial Analysis, Vals AI and BenchLM use different scoring systems, so Kimi’s Intelligence Index 54, Vals Accuracy 63.94% and BenchLM 85/100 should not be merged as if they were three versions of the same metric .

Practical buying guide

• If autonomous code repair and software engineering are the priority, start by testing Claude Opus 4.7. Its SWE-bench Verified 82.4% and BenchLM 97/100 are the sharpest public signals in this source set .
• If your workload is professional knowledge output, computer use or customer-support automation, GPT-5.5 has the most specific official evidence through GDPval 84.9%, OSWorld-Verified 78.7% and Tau2-bench Telecom 98.0% .
• If your main concern is scientific QA, math or hard reasoning, compare DeepSeek V4-Pro-Max and Kimi K2.6 Thinking carefully. The shared Hugging Face rows favor DeepSeek on several knowledge tests, while Kimi is slightly ahead on GPQA Diamond in that table .
• If open weights, visible per-test cost or operational flexibility matter most, Kimi K2.6 deserves a pilot because of its Artificial Analysis open-weight positioning and Vals Cost/Test of $0.21 .
• If someone offers you a single ranking based on MMLU alone, be skeptical. By 2026, top models are tightly bunched on MMLU, which reduces its usefulness as a frontier-model separator .

Bottom line

The public evidence points to different leaders for different jobs. Claude Opus 4.7 has the cleanest coding and BenchLM case; GPT-5.5 has the most concrete official metrics for knowledge-work agents and computer use; DeepSeek V4-Pro-Max has strong reasoning and math figures; and Kimi K2.6 is notable for open weights, cost and latency signals .

What the evidence does not support is a confident 1st-through-4th ranking across all four models. Use the benchmark table as a shortlist, then run your own evaluation on the actual work: codebases, finance documents, browser tasks, support workflows, long-running agents and budget limits. That approach aligns better with how 2026 benchmarks are organized and with the known limits of high-level scores .