Based on the evidence I found, the safest ranking is: GPT-5.5 for likely strongest closed-model general capability, Claude Opus 4.7 for the most clearly documented long-context/agentic production use, DeepSeek V4 for potentially high value but with documentation uncertainty, and Kimi K2.6 as not reliably comparable from the high-authority evidence I found. I would not treat this as a clean benchmark leaderboard: GPT-5.5 and Claude Opus 4.7 have stronger primary-source support, DeepSeek V4 has mixed release/methodology evidence, and Kimi K2.6 lacks a direct high-trust source in the retained evidence.

Key findings

Model	Best use case	Evidence confidence	Main caution
GPT-5.5	General frontier reasoning, research, coding, multimodal/productivity workflows	Medium-high	Official docs were found, but the retained snippets did not provide enough benchmark/pricing detail for a rigorous numeric comparison [2].
Claude Opus 4.7	Long-context knowledge work, coding agents, asynchronous agentic workflows	High	Strong documentation for context/pricing, but third-party benchmark rankings should still be independently validated [4][5][8].
DeepSeek V4	Cost-sensitive coding/agent experiments, especially if open or semi-open deployment matters	Low-medium	A recent source said no official V4 training-methodology documentation existed at that point, so benchmark claims need verification [6].
Kimi K2.6	Possible open-weight / agentic experimentation	Low	I found high-trust academic evidence for Kimi K2.5, not Kimi K2.6, so K2.6-specific claims are insufficiently verified [1].

My practical pick:

• Choose GPT-5.5 if you want the strongest general closed-model assistant and can tolerate higher cost and vendor lock-in.
• Choose Claude Opus 4.7 if your work involves very long documents, coding agents, legal/finance/enterprise analysis, or long-running tool use.
• Choose DeepSeek V4 only after running your own benchmark, because its value story may be strong but the evidence base is less mature.
• Do not choose Kimi K2.6 on public benchmark claims alone unless you can verify the model card, license, pricing, serving stack, and task performance yourself.

Confirmed facts

GPT-5.5 appears in an OpenAI API model documentation page, which is the strongest evidence in the retained source set that it is an official OpenAI API model ^[2]. The New York Times also reported that OpenAI unveiled a more powerful GPT-5.5 model, giving independent launch-context support beyond OpenAI’s own documentation ^[3].

Claude Opus 4.7 has much clearer primary-source documentation than the other non-OpenAI models in this comparison. Anthropic’s Claude documentation says Claude Opus 4.7 provides a 1M-token context window at standard API pricing with no long-context premium ^[4]. Anthropic’s pricing documentation also says Claude Opus 4.7, Opus 4.6, Sonnet 4.6, and Claude Mythos Preview include the full 1M-token context window at standard pricing ^[5].

Anthropic describes Claude Opus 4.7 as a hybrid reasoning model focused on frontier coding and AI agents, with a 1M-token context window ^[8]. A third-party API aggregator lists Claude Opus 4.7 as released on April 16, 2026, with 1,000,000-token context, $5 per million input tokens, and $25 per million output tokens ^[7].

For Kimi, the strongest retained academic result concerns Kimi K2.5, not Kimi K2.6. That paper describes Kimi K2.5 as an open-weight model released by Moonshot AI and notes that its technical report lacked an assessment for one evaluation-awareness benchmark ^[1]. This does not validate Kimi K2.6, but it does show that recent Kimi-family models have attracted independent safety evaluation ^[1].

For DeepSeek V4, the retained evidence is more conflicted and less complete. One recent source stated that no official V4 training-methodology documentation existed at the time it was writing, which makes architecture, safety, and benchmark claims harder to audit ^[6].

What remains inference

A direct “which is smartest?” ranking remains partly inference because the retained evidence does not include a single independent benchmark suite that tested GPT-5.5, Claude Opus 4.7, Kimi K2.6, and DeepSeek V4 under the same prompts, sampling settings, tools, latency constraints, and cost accounting.

The likely capability ordering for general closed-model tasks is GPT-5.5 and Claude Opus 4.7 at the top, because both have stronger primary-source or reputable-source confirmation than Kimi K2.6 and DeepSeek V4 ^[2][3][4][8]. Between GPT-5.5 and Claude Opus 4.7, I would not declare a universal winner without task-specific tests, because Claude’s documentation is unusually strong for long-context and agentic workflows while GPT-5.5’s retained evidence is broader but less detailed ^[2][4][8].

The likely value ordering may favor DeepSeek V4 or Kimi K2.6 if their low-cost/open-weight claims are verified, but the retained high-trust evidence is not strong enough to rank them confidently. For Kimi K2.6 specifically, insufficient evidence.

What the evidence suggests

Claude Opus 4.7 is the most defensible production pick from the evidence set if your workload depends on large context windows. Anthropic’s docs explicitly support 1M context at standard pricing and no long-context premium ^[4][5]. That matters because long-context pricing often dominates real enterprise costs, not headline benchmark scores.

GPT-5.5 is likely the strongest default choice for broad general-purpose work if your priority is frontier capability and ecosystem maturity. The retained sources show both an official OpenAI API model page and independent news coverage of the launch ^[2][3]. However, because the retained snippets do not give enough benchmark detail, I would avoid claiming GPT-5.5 “wins” every category.

DeepSeek V4 may be attractive for cost-sensitive engineering teams, but it needs a stricter validation pass before adoption. The key problem is not that DeepSeek V4 is weak; it is that the retained evidence leaves methodology gaps, including a report that no official V4 training-methodology documentation existed at that point ^[6].

Kimi K2.6 should be treated as unverified in this comparison. The strongest retained Kimi-related academic source is about Kimi K2.5, not Kimi K2.6 ^[1]. If Kimi K2.6 is important to your decision, the next step should be to collect its official model card, license, benchmark table, serving requirements, and API pricing before comparing it to GPT-5.5 or Claude Opus 4.7.

Conflicting evidence or uncertainty

The biggest uncertainty is source quality asymmetry. GPT-5.5 and Claude Opus 4.7 have stronger official or near-primary documentation in the retained evidence ^[2][4][5][8]. Kimi K2.6 and DeepSeek V4 have weaker retained evidence for direct model-card-level comparison ^[1][6].

Claude Opus 4.7’s context and pricing claims are relatively well-supported because they appear in Anthropic documentation and are echoed by an API aggregator ^[4][5][7]. Still, aggregator pricing should be treated as secondary because vendor pricing pages are the ground truth ^[5][7].

DeepSeek V4’s uncertainty is methodological. A source saying no official V4 training methodology documentation existed does not mean the model is poor; it means claims about architecture, benchmark reliability, and safety properties are harder to audit ^[6].

Kimi K2.6’s uncertainty is evidentiary. The retained academic result is for Kimi K2.5, so K2.6-specific benchmark or pricing claims remain insufficiently supported ^[1].

Open questions

• What are the official context window, max output, tool-use limits, and pricing for GPT-5.5 across ChatGPT, API, batch, and realtime usage?
• Does Kimi K2.6 have an official Moonshot model card, license, benchmark report, and reproducible evaluation setup?
• Does DeepSeek V4 have a final technical report or only a preview/release note?
• Which model performs best on your actual workload: coding, long-document analysis, tool calling, structured extraction, research, math, multimodal, or customer-support automation?
• How much do latency, privacy, deployment control, and data-residency requirements matter in your use case?

Sources worth trusting most

• OpenAI’s API documentation is the strongest retained source for confirming GPT-5.5’s official API presence ^[2].
• Anthropic’s Claude documentation and pricing pages are the strongest retained sources for Claude Opus 4.7 context and pricing claims ^[4][5].
• Anthropic’s own Claude Opus page is useful for understanding the model’s intended positioning around coding, agents, hybrid reasoning, and 1M context ^[8].
• The New York Times is useful as independent launch-context reporting for GPT-5.5, but not as a technical benchmark source ^[3].
• The Kimi K2.5 arXiv paper is useful for independent safety context around the Kimi model family, but it is not direct evidence for Kimi K2.6 ^[1].
• The DeepSeek V4 secondary source is useful mainly as a caution signal about missing methodology documentation, not as final proof of performance ^[6].

Recommended next step

Run a controlled benchmark on your own workload instead of relying on public leaderboards.

Use this test plan:

1. Pick 20–50 real tasks:

   • 10 coding/debugging tasks
   • 10 long-document reasoning tasks
   • 5 structured extraction tasks
   • 5 tool-calling/agent tasks
   • 5 domain-specific tasks, such as finance, legal, science, or product analysis

2. Test each model under the same settings:

   • Same prompt
   • Same context
   • Same temperature
   • Same tool permissions
   • Same timeout
   • Same scoring rubric

3. Score on:

   • Accuracy
   • Faithfulness to source material
   • Tool-call reliability
   • Long-context retention
   • Latency
   • Cost per successful task
   • Failure recovery
   • Safety/compliance behavior

4. Make the decision by workload:

   • If GPT-5.5 wins accuracy by a large margin, use it for highest-value reasoning tasks.
   • If Claude Opus 4.7 wins reliability and long-context handling, use it for production agents and document-heavy workflows.
   • If DeepSeek V4 or Kimi K2.6 are close enough at much lower cost, use them for batch coding, extraction, or self-hosted/open-weight experimentation.

Summary

GPT-5.5 is the likely premium generalist, but the retained evidence does not support a rigorous numeric benchmark comparison ^[2][3]. Claude Opus 4.7 is the best-documented choice for long-context, coding, and agentic workflows, with official support for a 1M-token context window at standard pricing ^[4][5][8]. DeepSeek V4 may be compelling on cost and openness, but methodology and release-documentation uncertainty make it a “verify before production” option ^[6]. Kimi K2.6 has insufficient high-trust direct evidence in the retained sources, so it should not be treated as benchmark-equivalent to GPT-5.5 or Claude Opus 4.7 without further validation ^[1].