Choosing between these four models is less about crowning a universal winner and more about weighting evidence quality. The public sources reviewed here support Claude Opus 4.7 most clearly: Anthropic describes it as a hybrid reasoning model for coding and AI agents with a 1M context window, and its documentation says that 1M context is available at standard API pricing with no long-context premium ^[1][3]. DeepSeek V4 has the clearest cost data in the reviewed sources, because DeepSeek’s pricing page shows 1M context, 384K maximum output, feature support, and concrete token-price rows ^[30]. GPT-5.5 and Kimi K2.6 are real enough to evaluate, but many comparison-critical details remain thinner in the available official snippets ^{[13][22][37][43]}.

Quick verdict

• No defensible overall winner yet. The reviewed snippets do not provide complete apples-to-apples benchmark scores across all four models; Claude benchmark categories are listed without scores in the available Vellum snippet, OpenAI’s release page references evaluations without showing numbers in the snippet, Hugging Face says DeepSeek V4 is competitive but not state of the art, and Kimi’s official blog references benchmark reproduction without showing scores in the snippet ^{[4][22][32][37]}.
• Best-documented model: Claude Opus 4.7. Anthropic gives the clearest primary-source claims around 1M context, coding, agents, vision, multi-step tasks, and knowledge work ^[1][3].
• Best pricing evidence: DeepSeek V4. DeepSeek’s API pricing page gives specific rows for cache-hit input, cache-miss input, and output tokens, alongside 1M context and 384K maximum output ^[30].
• Most under-specified official comparison: GPT-5.5. OpenAI documents the model IDs and API availability, but the reviewed official snippets do not provide enough detail to rank GPT-5.5 on context size, benchmark scores, pricing, modalities, or coding performance ^[13][22].
• Most important verification target: Kimi K2.6. Moonshot positions K2.6 around multimodality, coding, and agents, but exact context, pricing, output, and open-weight claims rely heavily on third-party or user-generated snippets in this source set ^{[38][41][42][43][45]}.

Comparison at a glance

Model	Best-supported facts in the reviewed sources	Main caveats
Claude Opus 4.7	Anthropic says it is a hybrid reasoning model for coding and AI agents with a 1M context window; Anthropic documentation says 1M context has no long-context premium [1][3].	Exact benchmark scores are not present in the reviewed Vellum snippet, although benchmark categories are listed [4].
GPT-5.5	OpenAI API docs list gpt-5.5 and gpt-5.5-2026-04-23, mark the model as long-context, and show tiered rate-limit information; OpenAI’s release page says GPT-5.5 and GPT-5.5 Pro became available in the API after an April 24, 2026 update [13][22].	The reviewed official snippets do not state exact context size, output limit, pricing, modalities, or benchmark numbers. Third-party pages report some of those figures, but they should be treated as secondary evidence [14][20][21].
DeepSeek V4	DeepSeek’s pricing page shows 1M context, 384K maximum output, JSON output, tool calls, beta chat-prefix completion, beta FIM completion, and token-price rows [30].	V4 Flash/Pro naming and architecture details are clearer in third-party summaries than in the pricing snippet alone [27][32].
Kimi K2.6	Moonshot’s site describes K2.6 as natively multimodal with coding capabilities and agent performance; Kimi’s blog says official Kimi-K2.6 benchmark results should be reproduced using the official API [37][43].	Exact context length, output length, pricing, deployment details, and open-weight status are mostly sourced here from third-party or user-generated snippets [38][41][42][45].

Claude Opus 4.7: the strongest primary-source case

Claude Opus 4.7 has the cleanest official documentation among the four. Anthropic presents it as a hybrid reasoning model built for coding and AI agents, featuring a 1M context window ^[3]. Anthropic’s Claude page also says Opus 4.7 brings stronger performance across coding, vision, and complex multi-step tasks, with better results across professional knowledge work ^[3].

The clearest differentiator is long context. Anthropic’s documentation says Claude Opus 4.7 provides a 1M context window at standard API pricing with no long-context premium ^[1]. The same documentation says Opus 4.7 shows meaningful gains on knowledge-worker tasks, especially cases where the model must visually verify its own outputs, including document redlining, slide editing, charts, and figure analysis ^[1].

There are useful third-party details, but they should be labeled as such. A Caylent writeup reports up to 128K output tokens and standard Opus pricing of $5 per million input tokens and $25 per million output tokens ^[5]. That is helpful for planning, but the strongest primary-source pricing claim in this set is Anthropic’s no-long-context-premium statement, not the exact dollar table ^[1].

The main limitation is benchmarks. A Vellum article in the reviewed sources lists Claude Opus 4.7 benchmark categories, including coding, agentic, finance, search, reasoning, multimodal, and safety areas, but the snippet does not include the actual scores needed for a direct model-vs-model ranking ^[4].

GPT-5.5: confirmed, but not yet comparable from official snippets alone

GPT-5.5 is confirmed in OpenAI’s own materials. OpenAI’s API documentation lists gpt-5.5 and the dated version gpt-5.5-2026-04-23, marks the model as long-context, and shows rate-limit tiers ^[13]. OpenAI’s release page is dated April 23, 2026, and says GPT-5.5 and GPT-5.5 Pro became available in the API after an April 24, 2026 update ^[22].

That establishes model status, but not enough to rank it responsibly against Claude Opus 4.7, DeepSeek V4, or Kimi K2.6. The reviewed OpenAI snippets do not provide exact context size, output limit, pricing, benchmark scores, modality details, latency, or coding performance ^[13][22].

Third-party pages fill in some gaps, but they are not equivalent to OpenAI’s own technical documentation. For example, third-party sources in the reviewed list report GPT-5.5 pricing of $5 per million input tokens and $30 per million output tokens, and one comparison page reports a 1M input / 128K output API context window ^[14][20][21]. Those figures may be useful leads for procurement checks, but they should not be treated as the same level of evidence as OpenAI’s API documentation or release page.

The practical read: evaluate GPT-5.5 first if your product is already built around OpenAI’s API, but do not claim that it beats Claude, DeepSeek, or Kimi on benchmarks from these snippets alone ^[13][22].

DeepSeek V4: strongest cost evidence, with some V4 details mediated by third parties

DeepSeek has the most concrete pricing data in this comparison. The DeepSeek API pricing page shows 1M context length, 384K maximum output, JSON output, tool calls, beta chat-prefix completion, and beta FIM completion ^[30]. It also lists token-price rows for 1M input tokens on cache hit, 1M input tokens on cache miss, and 1M output tokens, including values such as $0.028 and $0.03625 for cache-hit input, $0.14 and $0.435 for cache-miss input, and $0.28 and $0.87 for output, with limited-time discount notes and struck-through non-discounted values shown in the snippet ^[30].

The V4-specific naming is supported, but more indirectly. An EvoLink summary says DeepSeek’s official API docs list deepseek-v4-flash and deepseek-v4-pro, publish official pricing, and document 1M context plus 384K maximum output as of April 24, 2026 ^[27]. Hugging Face says DeepSeek released V4 with two mixture-of-experts checkpoints on the Hub: DeepSeek-V4-Pro at 1.6T total parameters with 49B active, and DeepSeek-V4-Flash at 284B total parameters with 13B active; it also says both have a 1M-token context window and that benchmark numbers are competitive but not state of the art ^[32]. OpenRouter’s V4 Pro listing separately describes a 1,048,576-token context and pricing of $0.435 per million input tokens and $0.87 per million output tokens ^[31].

That makes DeepSeek V4 a strong candidate for cost-sensitive evaluation, especially where long context and large outputs matter. It does not, by itself, prove quality, reliability, latency, safety, or tool-use success in your workload. Those still need direct testing.

Kimi K2.6: promising positioning, weaker spec confirmation

Kimi K2.6 has official positioning around the right use cases, but fewer official details in the reviewed snippets. Moonshot’s site says K2.6 is natively multimodal and emphasizes coding capabilities and agent performance ^[43]. Kimi’s own tech-blog snippet says official Kimi-K2.6 benchmark results should be reproduced using the official API, and points third-party providers to Kimi Vendor Verifier ^[37].

The more specific Kimi numbers in this source set are mostly third-party. LLM Stats says Kimi K2.6 has a 262,144-token input context and can generate up to 262,144 output tokens ^[42]. DesignForOnline describes Kimi K2.6 as having 262K context, vision, tool use, function calling, and pricing from $0.7500 per million tokens ^[41]. Atlas Cloud lists Kimi K2.6 API pricing starting from $0.95 per million tokens ^[38]. A LinkedIn snippet describes Kimi K2.6 as open-weight, but that source is user-generated and should be treated as lower-confidence unless Moonshot confirms the terms directly ^[45].

The practical read: Kimi K2.6 is worth evaluating for multimodal coding and agent workflows, but buyers should verify license, context length, output limits, pricing, benchmark methodology, and provider compatibility directly with Moonshot or an official API source ^[37][43].

Why the benchmark crown is unresolved

The reviewed sources do not contain a complete comparable scorecard. Vellum lists many Claude Opus 4.7 benchmark categories, but the snippet does not include exact scores ^[4]. OpenAI’s GPT-5.5 release page includes an evaluations section, but the reviewed snippet does not show the numbers ^[22]. Hugging Face says DeepSeek V4 is competitive but not state of the art, without showing the full benchmark table in the snippet ^[32]. Kimi’s official blog snippet references reproducing official Kimi-K2.6 benchmark results, but does not show those results in the snippet ^[37].

That matters because model rankings can flip by workload. Coding benchmarks, long-context retrieval, multimodal analysis, tool-calling reliability, agentic planning, latency, and cost under cache-hit or cache-miss conditions are different tests. Without the same benchmark set across all four models, a single best overall label would be more marketing than evidence.

Which model should you test first?

• Test Claude Opus 4.7 first if you want the strongest primary-source documentation for 1M context, coding, agents, vision, complex multi-step tasks, and knowledge work ^[1][3].
• Test GPT-5.5 first if your application already depends on OpenAI infrastructure and you mainly need to validate the documented gpt-5.5 API model path ^[13][22].
• Test DeepSeek V4 first if your first screen is cost, long context, maximum output, JSON output, or tool-call support; DeepSeek’s pricing page is the most specific cost source reviewed here ^[30].
• Test Kimi K2.6 first if your priority is Moonshot’s multimodal coding-and-agent direction, while separately confirming exact context, pricing, output, and license details ^{[37][42][43][45]}.

A practical evaluation plan

For production decisions, run a task-specific bake-off instead of relying on broad leaderboard claims. Use the same prompts, tools, context sizes, and scoring rubrics across all candidates. Track at least five dimensions: task success, tool-call reliability, long-context accuracy, latency, and fully loaded token cost. For DeepSeek, separate cache-hit and cache-miss costs because the pricing page splits those rows explicitly ^[30]. For Kimi and GPT-5.5, separate vendor-confirmed details from third-party aggregator claims until official documentation fills the gaps ^{[13][22][37][42]}.

Final assessment

On the evidence reviewed, Claude Opus 4.7 is the most clearly documented flagship model, especially for 1M context, coding, agents, and knowledge-work claims ^[1][3]. DeepSeek V4 has the strongest pricing evidence and credible long-context evidence, but some V4 Flash/Pro architecture and release details come through third-party sources ^[27][30][32]. GPT-5.5 is confirmed in OpenAI’s API documentation, but the reviewed official snippets are too thin for a full performance comparison ^[13][22]. Kimi K2.6 is positioned by Moonshot around multimodal, coding, and agent use cases, but many exact technical and commercial claims need stronger primary confirmation ^[37][43][45].