Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6

Frontier-model comparisons are often framed as a horse race. A better way to compare Claude Opus 4.7, GPT-5.5, DeepSeek V4, and Kimi K2.6 is to ask a simpler question: which claims are actually well supported?

The public evidence is uneven. Anthropic gives the clearest official documentation for Claude Opus 4.7, including a 1M context window and no long-context premium in its model documentation . DeepSeek provides the clearest concrete pricing and spec rows, including 1M context, 384K maximum output, tool calls, JSON output, and token-price rows . OpenAI confirms GPT-5.5 in its API documentation and release page, but the available official snippets do not expose enough pricing, context, and benchmark detail for a complete comparison . Moonshot positions Kimi K2.6 around multimodality, coding, and agent performance, but many exact technical and commercial details in this source set come from third-party or user-generated pages .

Bottom line

• No single model is proven to be best overall. The accessible benchmark evidence is not apples-to-apples: Vellum lists Claude Opus 4.7 benchmark categories without visible scores in the snippet, OpenAI’s GPT-5.5 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 points readers to the official API for reproducing benchmark results .
• Claude Opus 4.7 has the strongest primary-source case. 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 .
• DeepSeek V4 has the clearest cost evidence. DeepSeek’s pricing page provides concrete cache-hit, cache-miss, and output token-price rows, alongside 1M context and 384K maximum output .
• GPT-5.5 is confirmed but under-specified in the available official snippets. OpenAI lists gpt-5.5 and gpt-5.5-2026-04-23 in API documentation and says GPT-5.5 and GPT-5.5 Pro became available in the API after an April 24, 2026 update, but the snippets reviewed here do not provide enough detail to rank it across all dimensions .
• Kimi K2.6 is promising but needs more direct verification. Moonshot’s site highlights native multimodality, coding capabilities, and agent performance, while Kimi’s blog recommends using the official API to reproduce official benchmark results .

Comparison at a glance

Model	Best-supported facts	Main caveats
Claude Opus 4.7	Anthropic describes it as a hybrid reasoning model for coding and AI agents with a 1M context window; Anthropic documentation says the 1M context window is available at standard API pricing with no long-context premium .	The accessible Vellum summary lists benchmark categories but not the exact scores needed for a direct ranking; third-party claims about 128K output and $5/$25 per million token pricing should be treated as secondary evidence .
GPT-5.5	OpenAI’s 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 .	The available official snippets do not state exact context size, output limit, pricing, modality details, or benchmark numbers. Third-party sources report some of those figures, but they are lower-confidence than OpenAI’s own docs .
DeepSeek V4	DeepSeek’s pricing page shows 1M context, 384K maximum output, JSON output, tool calls, beta chat-prefix completion, beta FIM completion, and concrete token-price rows . Hugging Face says DeepSeek released V4 Pro and V4 Flash checkpoints, both with 1M-token context .	Some V4 Flash/Pro naming and architecture details are clearer in third-party summaries than in DeepSeek’s pricing snippet alone; Hugging Face describes the benchmark numbers as competitive but not state of the art .
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 .	Exact context length, output length, pricing, and open-weight status are mostly supported here by third-party or user-generated snippets rather than primary vendor documentation .

Claude Opus 4.7: the strongest official documentation

Claude Opus 4.7 has the cleanest primary-source story in this comparison. Anthropic describes it as a hybrid reasoning model that pushes the frontier for coding and AI agents, and its product page says it features a 1M context window . Anthropic also says Opus 4.7 brings stronger performance across coding, vision, and complex multi-step tasks, with better results across professional knowledge work .

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 . The same documentation says the model shows meaningful gains on knowledge-worker tasks, especially cases where it needs to visually verify its own outputs, such as document redlining, slide editing, chart analysis, and figure analysis .

There are useful third-party details, but they should be labeled as such. Caylent reports that Opus 4.7 supports up to 128K output tokens and standard Opus pricing of $5 per million input tokens and $25 per million output tokens . That is helpful for planning, but the strongest primary-source pricing claim in the reviewed materials is Anthropic’s no-long-context-premium statement .

The benchmark caveat matters. Vellum’s Claude Opus 4.7 article lists categories such as coding, agentic capabilities, finance, reasoning, multimodal and vision capabilities, search, and safety, but the accessible snippet does not include the actual scores needed to compare Claude directly against GPT-5.5, DeepSeek V4, or Kimi K2.6 .

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

GPT-5.5 is real enough to include in a procurement shortlist. 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 . 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 .

That confirms API status, but not enough to rank GPT-5.5 responsibly against the other three models. The available official snippets do not provide exact context size, output limit, pricing, benchmark scores, modality details, coding performance, or latency .

Third-party pages fill in some of those gaps, but they are not equivalent to OpenAI’s own documentation. DesignForOnline reports GPT-5.5 pricing at $5 per million input tokens and $30 per million output tokens . LLM Stats reports a 1M input and 128K output API context window, as well as text and image input with text output . Those figures are useful leads for vendor checks, not definitive primary-source evidence.

The practical read: test GPT-5.5 early if your product already depends on OpenAI infrastructure, but do not claim from these sources alone that it beats Claude, DeepSeek, or Kimi on benchmarks, cost, or agentic performance .

DeepSeek V4: the clearest pricing and output evidence

DeepSeek has the most concrete cost table in this comparison. Its API pricing page shows 1M context length, 384K maximum output, JSON output, tool calls, beta chat-prefix completion, and beta FIM completion . It also lists token-price rows for cache-hit input, cache-miss input, and output tokens, including $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 .

The V4-specific picture is supported, but more indirectly. EvoLink 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 . Hugging Face says DeepSeek released V4 with two mixture-of-experts checkpoints: DeepSeek-V4-Pro at 1.6T total parameters with 49B active, and DeepSeek-V4-Flash at 284B total parameters with 13B active . Hugging Face also says both have a 1M-token context window and describes the benchmark numbers as competitive but not state of the art .

OpenRouter’s V4 Pro listing separately describes a 1,048,576-token context window and pricing of $0.435 per million input tokens and $0.87 per million output tokens . That helps triangulate the V4 Pro commercial picture, but teams should still confirm current pricing directly because DeepSeek’s own pricing page includes limited-time discount language .

The practical read: DeepSeek V4 deserves an early test when cost, long context, large outputs, JSON output, or tool-call support are the first filters. It does not automatically win on quality, reliability, safety, latency, or tool-use success; those still need direct workload testing.

Kimi K2.6: strong positioning, weaker spec confirmation

Kimi K2.6 is positioned around the right frontier-model use cases, but its exact specs are less firmly supported by primary sources in the available record. Moonshot’s site says K2.6 is natively multimodal and highlights coding capabilities and agent performance . 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 .

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

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 through Moonshot or an official API source before making production decisions .

Why the benchmark crown is unresolved

A single leaderboard-style winner would be misleading because the sources do not provide a complete, comparable scorecard. The accessible Vellum summary lists Claude Opus 4.7 benchmark areas but not the exact results . OpenAI’s GPT-5.5 release page includes an evaluations section in the page structure, but the snippet does not show the numbers . Hugging Face says DeepSeek V4’s benchmark numbers are competitive but not state of the art . Kimi’s official blog snippet refers to reproducing Kimi-K2.6 benchmark results using the official API but does not show the results in the snippet .

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

Which model should you test first?

• Test Claude Opus 4.7 first if you want the strongest official documentation for 1M context, coding, AI agents, vision, complex multi-step work, and knowledge-work improvements .
• 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 path .
• 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 .
• Test Kimi K2.6 first if your priority is Moonshot’s multimodal coding-and-agent direction, while separately confirming exact context, pricing, output, license, and provider details .

A practical evaluation plan

For production decisions, run a task-specific bake-off instead of relying on broad claims. Use the same prompts, tools, context sizes, file inputs, 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 . For GPT-5.5, separate OpenAI-confirmed details from third-party context and pricing claims until official documentation fills the gaps . For Kimi K2.6, treat provider listings and user-generated open-weight claims as leads to verify, not as final procurement evidence .

Final verdict

On evidence rather than hype, Claude Opus 4.7 is the most clearly documented flagship in this comparison, especially for 1M context, coding, AI agents, and knowledge-work claims . DeepSeek V4 has the strongest pricing evidence and credible long-context evidence, but some V4 Flash/Pro architecture and naming details are clearer in third-party summaries than in the pricing snippet alone . GPT-5.5 is confirmed in OpenAI’s own API and release materials, but the available official snippets are too thin for a full performance comparison . Kimi K2.6 has credible official positioning around multimodal, coding, and agent use cases, but many exact technical and commercial claims still require stronger primary confirmation .