First, match the versions

For this article, DeepSeek V4 means DS-V4-Pro Max, and Kimi K2.6 means K2.6 Thinking, because those are the exact columns in the DeepSeek V4-Pro model card.

That version lock is not a detail. The DeepSeek table’s GPT and Claude columns are GPT-5.4 xHigh and Opus-4.6 Max—not GPT-5.5 or Claude Opus 4.7. So the DeepSeek table cannot be used to call a full DeepSeek V4-Pro Max vs GPT-5.5 or vs Claude Opus 4.7 winner.

Anthropic’s public Claude Opus 4.7 API page, meanwhile, focuses on functionality and usage, including the task budgets beta, rather than providing a benchmark table that can be merged directly with OpenAI and DeepSeek model-card scores.

The cleanest four-way overlap: Terminal-Bench 2.0

The one benchmark in this source set where all four target models have comparable public numbers is Terminal-Bench 2.0. On those published tables, the order is:

That supports a narrow but meaningful statement: on Terminal-Bench 2.0, GPT-5.5 is well ahead; Claude Opus 4.7 is second; DeepSeek V4-Pro Max and Kimi K2.6 Thinking are close to each other. It does not prove GPT-5.5 is best for every workflow, and it does not replace a same-harness retest with equal tool access, context limits and reasoning budgets.

GPT-5.5 and Claude Opus 4.7 inside OpenAI’s table

OpenAI’s release page compares GPT-5.5 with Claude Opus 4.7 on several benchmarks. In every listed item below, GPT-5.5 has the higher score.

The right takeaway is not that Claude Opus 4.7 is weak; it is that, within OpenAI’s published comparison set, GPT-5.5 is ahead. OpenAI’s system card also positions GPT-5.5 for complex real-world work such as coding, online research, information analysis, document and spreadsheet creation, and cross-tool workflows.

Still, this is an OpenAI release-page comparison, not an independent same-configuration retest of all four models.

DeepSeek V4-Pro Max and Kimi K2.6 Thinking inside DeepSeek’s table

DeepSeek’s V4-Pro model card compares DS-V4-Pro Max with K2.6 Thinking across several items. In that table, DS-V4-Pro Max leads on most listed metrics, but Kimi K2.6 Thinking has clear wins too.

The safest read is this: DS-V4-Pro Max is higher than K2.6 Thinking on most DeepSeek model-card entries, while Kimi leads on GPQA Diamond and SWE Pro. Some gaps, including MMLU-Pro and Terminal-Bench 2.0, are small enough that a product team should care more about task fit, evaluation noise and failure modes than the direction of a tiny lead.

Why a single four-model ranking would be overclaiming

The tempting shortcut is to stitch OpenAI’s table, DeepSeek’s table and Anthropic’s API notes into one mega-score. The public evidence here does not support that.

1. Different versions. OpenAI compares GPT-5.5 with Claude Opus 4.7; DeepSeek’s table uses GPT-5.4 xHigh and Opus-4.6 Max in its GPT/Claude columns.
2. Different sources and setups. The material comes from vendor release pages, a system card, API documentation and a model card—not from one independent four-way run under one harness.
3. Different benchmarks measure different things. GDPval, BrowseComp, FrontierMath, CyberGym, MMLU-Pro, GPQA Diamond and SWE Pro are not interchangeable. Without a declared weighting scheme, adding them into a total score can hide the task differences that matter most.

Use the numbers as a first-pass screen, not as a procurement decision by themselves.

How to use the scores in a real model selection

A more defensible process has three layers:

• Shared benchmark layer. The clearest four-way overlap in this source set is Terminal-Bench 2.0, where GPT-5.5 leads.
• Within-vendor table layer. OpenAI’s table supports GPT-5.5 over Claude Opus 4.7 on the listed items; DeepSeek’s model card supports DS-V4-Pro Max over K2.6 Thinking on most listed items.
• Your own retest layer. Re-run representative tasks in coding, agent workflows, reasoning, retrieval, tool use, latency and cost under the same prompts, context, tool permissions and scoring rules.

If your application depends on long agent loops, Claude Opus 4.7’s task budgets beta is worth testing separately. Anthropic says a task budget gives Claude a rough token target for a full agentic loop—including thinking, tool calls, tool results and final output—and shows the model a running countdown so it can prioritize work as the budget is consumed.

If your application is closer to complex coding, online research, document or spreadsheet generation, or moving across tools, GPT-5.5’s system card describes those as core target workloads. But even a public benchmark leader still needs to be tested inside your codebase, toolchain, permissions model and failure-recovery rules.

The safest ranking language

• Terminal-Bench 2.0 only: GPT-5.5 first, Claude Opus 4.7 second, DeepSeek V4-Pro Max third and Kimi K2.6 Thinking fourth.
• Inside OpenAI’s table: GPT-5.5 is higher than Claude Opus 4.7 on the listed benchmarks.
• Inside DeepSeek’s table: DS-V4-Pro Max is higher than K2.6 Thinking on most listed benchmarks, while Kimi leads on GPQA Diamond and SWE Pro.
• Overall four-model champion: not established. The public material here does not provide a same-version, same-harness, independent four-way comparison.

First, match the versions

For this article, DeepSeek V4 means DS-V4-Pro Max, and Kimi K2.6 means K2.6 Thinking, because those are the exact columns in the DeepSeek V4-Pro model card.

That version lock is not a detail. The DeepSeek table’s GPT and Claude columns are GPT-5.4 xHigh and Opus-4.6 Max—not GPT-5.5 or Claude Opus 4.7. So the DeepSeek table cannot be used to call a full DeepSeek V4-Pro Max vs GPT-5.5 or vs Claude Opus 4.7 winner.

Anthropic’s public Claude Opus 4.7 API page, meanwhile, focuses on functionality and usage, including the task budgets beta, rather than providing a benchmark table that can be merged directly with OpenAI and DeepSeek model-card scores.

The cleanest four-way overlap: Terminal-Bench 2.0

The one benchmark in this source set where all four target models have comparable public numbers is Terminal-Bench 2.0. On those published tables, the order is:

That supports a narrow but meaningful statement: on Terminal-Bench 2.0, GPT-5.5 is well ahead; Claude Opus 4.7 is second; DeepSeek V4-Pro Max and Kimi K2.6 Thinking are close to each other. It does not prove GPT-5.5 is best for every workflow, and it does not replace a same-harness retest with equal tool access, context limits and reasoning budgets.

GPT-5.5 and Claude Opus 4.7 inside OpenAI’s table

OpenAI’s release page compares GPT-5.5 with Claude Opus 4.7 on several benchmarks. In every listed item below, GPT-5.5 has the higher score.

The right takeaway is not that Claude Opus 4.7 is weak; it is that, within OpenAI’s published comparison set, GPT-5.5 is ahead. OpenAI’s system card also positions GPT-5.5 for complex real-world work such as coding, online research, information analysis, document and spreadsheet creation, and cross-tool workflows.

Still, this is an OpenAI release-page comparison, not an independent same-configuration retest of all four models.

DeepSeek V4-Pro Max and Kimi K2.6 Thinking inside DeepSeek’s table

DeepSeek’s V4-Pro model card compares DS-V4-Pro Max with K2.6 Thinking across several items. In that table, DS-V4-Pro Max leads on most listed metrics, but Kimi K2.6 Thinking has clear wins too.

The safest read is this: DS-V4-Pro Max is higher than K2.6 Thinking on most DeepSeek model-card entries, while Kimi leads on GPQA Diamond and SWE Pro. Some gaps, including MMLU-Pro and Terminal-Bench 2.0, are small enough that a product team should care more about task fit, evaluation noise and failure modes than the direction of a tiny lead.

Why a single four-model ranking would be overclaiming

The tempting shortcut is to stitch OpenAI’s table, DeepSeek’s table and Anthropic’s API notes into one mega-score. The public evidence here does not support that.

1. Different versions. OpenAI compares GPT-5.5 with Claude Opus 4.7; DeepSeek’s table uses GPT-5.4 xHigh and Opus-4.6 Max in its GPT/Claude columns.
2. Different sources and setups. The material comes from vendor release pages, a system card, API documentation and a model card—not from one independent four-way run under one harness.
3. Different benchmarks measure different things. GDPval, BrowseComp, FrontierMath, CyberGym, MMLU-Pro, GPQA Diamond and SWE Pro are not interchangeable. Without a declared weighting scheme, adding them into a total score can hide the task differences that matter most.

Use the numbers as a first-pass screen, not as a procurement decision by themselves.

How to use the scores in a real model selection

A more defensible process has three layers:

• Shared benchmark layer. The clearest four-way overlap in this source set is Terminal-Bench 2.0, where GPT-5.5 leads.
• Within-vendor table layer. OpenAI’s table supports GPT-5.5 over Claude Opus 4.7 on the listed items; DeepSeek’s model card supports DS-V4-Pro Max over K2.6 Thinking on most listed items.
• Your own retest layer. Re-run representative tasks in coding, agent workflows, reasoning, retrieval, tool use, latency and cost under the same prompts, context, tool permissions and scoring rules.

If your application depends on long agent loops, Claude Opus 4.7’s task budgets beta is worth testing separately. Anthropic says a task budget gives Claude a rough token target for a full agentic loop—including thinking, tool calls, tool results and final output—and shows the model a running countdown so it can prioritize work as the budget is consumed.

If your application is closer to complex coding, online research, document or spreadsheet generation, or moving across tools, GPT-5.5’s system card describes those as core target workloads. But even a public benchmark leader still needs to be tested inside your codebase, toolchain, permissions model and failure-recovery rules.

The safest ranking language

• Terminal-Bench 2.0 only: GPT-5.5 first, Claude Opus 4.7 second, DeepSeek V4-Pro Max third and Kimi K2.6 Thinking fourth.
• Inside OpenAI’s table: GPT-5.5 is higher than Claude Opus 4.7 on the listed benchmarks.
• Inside DeepSeek’s table: DS-V4-Pro Max is higher than K2.6 Thinking on most listed benchmarks, while Kimi leads on GPQA Diamond and SWE Pro.
• Overall four-model champion: not established. The public material here does not provide a same-version, same-harness, independent four-way comparison.