GPT-5.5 vs Claude Opus 4.7 vs Kimi K2.6 vs DeepSeek V4: where each model leads

There is also a DeepSeek naming caveat. In the shared benchmark table, the DeepSeek entry is DeepSeek-V4-Pro-Max. A separate SWE-Bench Verified figure refers to DeepSeek V4-Pro, not Pro-Max . So the fair reading is that different DeepSeek V4 variants have different reported results, not that there is one universal DeepSeek V4 score.

Quick answer

• For hard reasoning without tools: start with Claude Opus 4.7. It leads the shared table on GPQA Diamond and Humanity’s Last Exam without tools .
• For terminal-based agentic work: GPT-5.5 has the clearest lead, scoring 82.7% on Terminal-Bench 2.0 versus 69.4% for Claude Opus 4.7 and 67.9% for DeepSeek-V4-Pro-Max .
• For reasoning with tools and browsing: GPT-5.5 Pro leads the rows where it is reported, with 57.2% on HLE with tools and 90.1% on BrowseComp .
• For coding experiments with available weights: Kimi K2.6 deserves a separate test. Its model card reports 80.2 on SWE-Bench Verified, 58.6 on SWE-Bench Pro and 66.7 on Terminal-Bench 2.0 . Another source says K2.6 weights are on Hugging Face and can run with vLLM, SGLang or KTransformers .
• For cost-sensitive API use: DeepSeek V4 does not top the shared benchmark rows, but Mashable and DataCamp list its API pricing at $1.74 per 1M input tokens and $3.48 per 1M output tokens, compared with $5/$30 for GPT-5.5 and $5/$25 for Claude Opus 4.7 .

Benchmark table

Here, n/a means the value was not provided in the cited source. It does not mean the model scored zero.

Reasoning: Claude leads without tools, GPT-5.5 Pro leads with tools

On the no-tool reasoning rows, Claude Opus 4.7 has the edge. In GPQA Diamond, it scores 94.2%, just ahead of GPT-5.5 at 93.6%, while DeepSeek-V4-Pro-Max is at 90.1% . In Humanity’s Last Exam without tools, Claude’s lead is larger: 46.9% versus 41.4% for GPT-5.5, 43.1% for GPT-5.5 Pro and 37.7% for DeepSeek-V4-Pro-Max .

The ranking changes when tools are allowed. In HLE with tools, GPT-5.5 Pro reaches 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 practical takeaway is straightforward: Claude looks strongest for pure reasoning in this table, while GPT-5.5 Pro looks strongest in the available tool-assisted reasoning row .

Coding and agentic tasks: GPT-5.5 has the big Terminal-Bench gap

The largest GPT-5.5 advantage in the shared table is Terminal-Bench 2.0: 82.7% for GPT-5.5, compared with 69.4% for Claude Opus 4.7 and 67.9% for DeepSeek-V4-Pro-Max . Kimi K2.6 is reported separately at 66.7 on Terminal-Bench 2.0 in its Hugging Face card, and an LLM Stats leaderboard also lists 0.667 for Kimi K2.6 and 0.694 for Claude Opus 4.7 . That puts Kimi near Claude and DeepSeek on that specific scale, but still well below GPT-5.5 in the shared table .

For SWE-Bench Pro / SWE Pro, Claude Opus 4.7 leads the shared comparison with 64.3%, ahead of GPT-5.5 at 58.6% and DeepSeek-V4-Pro-Max at 55.4% . Kimi K2.6 is also listed at 58.6 on SWE-Bench Pro in its Hugging Face card, but that number comes from a different source rather than the same shared comparison run .

SWE-Bench Verified should not be turned into a universal ranking of all four families. Kimi K2.6 has a reported 80.2 in its model card and eval file . A separate DeepSeek V4 overview reports 87.6% for Claude Opus 4.7 and 80.6% for DeepSeek V4-Pro, but that source does not provide the full GPT-5.5 row and it refers to V4-Pro rather than V4-Pro-Max .

Model-by-model takeaways

GPT-5.5 and GPT-5.5 Pro

GPT-5.5 stands out most clearly on Terminal-Bench 2.0, where its 82.7% score is the best in the shared table . GPT-5.5 Pro is not reported on every row, but where it appears, it wins two important categories: 57.2% on HLE with tools and 90.1% on BrowseComp .

If your workload involves terminal agents, long multi-step execution or tool-heavy browsing, GPT-5.5 and GPT-5.5 Pro are the first candidates to test from this comparison .

Claude Opus 4.7

Claude Opus 4.7 has the broadest set of wins in the shared table. It leads GPQA Diamond at 94.2%, HLE without tools at 46.9%, SWE-Bench Pro / SWE Pro at 64.3% and MCP Atlas / MCPAtlas Public at 79.1% . It does, however, trail GPT-5.5 on Terminal-Bench 2.0 and GPT-5.5 Pro on HLE with tools and BrowseComp .

For a first pass at hard no-tool reasoning or SWE-Bench Pro-style coding tasks, Claude Opus 4.7 looks like the strongest default candidate in the shared data .

Kimi K2.6

Kimi K2.6 cannot be ranked cleanly against every model in the main table, because its results here come from its Hugging Face model card and eval file rather than the same head-to-head comparison . Still, the coding profile is notable: 80.2 on SWE-Bench Verified, 58.6 on SWE-Bench Pro, 76.7 on SWE-Bench Multilingual, 66.7 on Terminal-Bench 2.0 and 73.1 on OSWorld-Verified .

Its operational appeal is also different. One source says K2.6 weights are available on Hugging Face and that it can run with vLLM, SGLang or KTransformers . That does not make Kimi the overall benchmark winner, but it does make it a serious candidate for teams that want to run their own coding and deployment experiments .

DeepSeek V4

In the shared table, DeepSeek is represented as DeepSeek-V4-Pro-Max . Across those rows, it does not take first place: 90.1% on GPQA Diamond, 37.7% on HLE without tools, 48.2% on HLE with tools, 67.9% on Terminal-Bench 2.0, 55.4% on SWE-Bench Pro / SWE Pro, 83.4% on BrowseComp and 73.6% on MCP Atlas / MCPAtlas Public .

DeepSeek’s strongest argument in this comparison is price rather than absolute benchmark leadership. Mashable and DataCamp list DeepSeek V4 API pricing at $1.74 per 1M input tokens and $3.48 per 1M output tokens. The same sources list GPT-5.5 at $5 per 1M input tokens and $30 per 1M output tokens, and Claude Opus 4.7 at $5 and $25 respectively . If cost is the limiting factor, DeepSeek V4 belongs in your own eval, even if it is not the leader in the shared benchmark table .

The main limitations

1. There is no single common run for all four model families across every benchmark. The shared table covers GPT-5.5, GPT-5.5 Pro, Claude Opus 4.7 and DeepSeek-V4-Pro-Max, while Kimi K2.6 is added from Hugging Face data .
2. DeepSeek V4 refers to different variants in different places. The shared table uses DeepSeek-V4-Pro-Max, while the separate SWE-Bench Verified figure refers to DeepSeek V4-Pro .
3. GPT-5.5 Pro is not reported everywhere. Where the Pro column is blank, its performance should not be inferred from nearby GPT-5.5 results .
4. Kimi K2.6 should be checked with your own evals. Its Hugging Face numbers are useful, but they are not from the same shared table as GPT-5.5, Claude Opus 4.7 and DeepSeek-V4-Pro-Max .

Bottom line

If you only trust the shared comparison rows, Claude Opus 4.7 wins GPQA Diamond, HLE without tools, SWE-Bench Pro and MCP Atlas; GPT-5.5 wins Terminal-Bench 2.0; and GPT-5.5 Pro wins HLE with tools and BrowseComp . Kimi K2.6 looks like a strong coding candidate with available weights, but it needs a separate evaluation before being ranked head-to-head against the others . DeepSeek V4 is not the benchmark leader in these rows, but its lower published API pricing makes it worth testing when budget matters .