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

AI model benchmarks are tempting to compress into a single leaderboard. For Claude Opus 4.7, GPT-5.5, DeepSeek V4/V4-Pro and Kimi K2.6, that would be too neat. The available references compare different model pairs, different variants, and different benchmark setups; some are structured benchmark pages, while others are community posts, videos or commentary .

The useful conclusion is not that one model wins everything. It is that Claude Opus 4.7 and GPT-5.5 are the strongest baseline candidates in the evidence here, DeepSeek V4/V4-Pro deserves a cost-focused evaluation, and Kimi K2.6 should be treated as an experimental coding candidate until stronger independent data appears.

The short answer

There is no solid basis for a final 1-to-4 ranking.

The most concrete public evidence puts Claude Opus 4.7 and GPT-5.5 in the frontier conversation. Artificial Analysis reports Claude Opus 4.7 Adaptive Reasoning, Max Effort at 57 on its Intelligence Index, while another Artificial Analysis page says GPT-5.5 xhigh leads the index with a score of 60 across 356 evaluated models . LLM Stats, however, does not show one model sweeping the other; it reports that GPT-5.5 and Claude Opus 4.7 trade wins across different benchmark categories .

DeepSeek V4/V4-Pro is interesting for value and flexibility, but the labels matter. Mashable discusses DeepSeek V4 Preview as an open-source model available under an MIT license, while Artificial Analysis and Lushbinary discuss DeepSeek V4 Pro in comparison and pricing contexts . Those should not be merged into one claim without checking the exact model version.

Kimi K2.6 is worth watching, especially for coding and agentic workflows, but the evidence base in the provided references is thinner: Substack, Reddit, YouTube and public commentary appear more often than uniform independent benchmark data .

Which benchmarks are most trustworthy here?

The safest sources are the ones that clearly identify the model, test setting and metric.

Anthropic is useful for verifying that Claude Opus 4.7 exists as an API model: the company says developers can use claude-opus-4-7 through the Claude API . Artificial Analysis is useful for intelligence, speed, price and comparison pages, including Claude Opus 4.7 and DeepSeek V4 Pro versus Claude Opus 4.7 . LLM Stats is useful because it compares GPT-5.5 and Claude Opus 4.7 directly across the same set of ten benchmarks .

Community material can still be valuable as an early signal, especially for real-world coding workflows. But it should not be the final basis for procurement, architecture or production routing. For Kimi K2.6, the available references include Substack, Reddit, YouTube and public articles; the Artificial Analysis page in this set is about Kimi K2 versus Claude 4 Opus, not Kimi K2.6 versus Claude Opus 4.7 . That distinction matters.

Evidence by model

Model	Strongest evidence in these references	Safe takeaway	Main caveat
Claude Opus 4.7	Anthropic lists claude-opus-4-7 for the Claude API; Artificial Analysis gives Claude Opus 4.7 a score of 57 and reports 48.6 output tokens per second on Anthropic’s API .	A strong candidate for reasoning, academic-style evals and some coding benchmarks.	Not automatically the fastest: Artificial Analysis says 48.6 tokens per second is below the 61.5 tokens-per-second median for similar-price reasoning models .
GPT-5.5	LLM Stats compares it directly with Claude Opus 4.7; Artificial Analysis says GPT-5.5 xhigh leads its Intelligence Index with a score of 60 out of 356 models .	A strong candidate for agentic workloads involving terminals, browsing, operating-system tasks or security-style evals.	In this source set, the concrete benchmark evidence is third-party, not an official OpenAI page.
DeepSeek V4 / V4-Pro	Mashable reports DeepSeek V4 Preview as MIT-licensed open source; Artificial Analysis compares DeepSeek V4 Pro with Claude Opus 4.7; Lushbinary reports V4-Pro output cost at $3.48 per 1 million tokens .	A value candidate worth testing for high-volume workloads, routing or fallback.	V4 Preview and V4 Pro are labels from different sources; do not assume they are identical.
Kimi K2.6	The available evidence comes mainly from Substack, Reddit, YouTube and public commentary; Artificial Analysis covers Kimi K2, not Kimi K2.6 .	Interesting for coding and agentic experiments.	The weakest evidence base for a general-purpose ranking.

Claude Opus 4.7: strong reasoning, but check latency

Claude Opus 4.7 has the clearest vendor verification in this set: Anthropic says claude-opus-4-7 is available through the Claude API . On structured benchmark data, Artificial Analysis reports Claude Opus 4.7 Adaptive Reasoning, Max Effort at 57 on the Artificial Analysis Intelligence Index, above the 33 median cited for comparable reasoning models in a similar price tier .

LLM Stats reports that Claude Opus 4.7 leads GPT-5.5 on GPQA, HLE, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1 . That makes it a serious shortlist model for deep reasoning, domain analysis and certain coding evaluations.

The caveat is speed. Artificial Analysis reports Claude Opus 4.7 output at 48.6 tokens per second via Anthropic’s API, below the 61.5 tokens-per-second median for reasoning models in a similar price tier . If your product is latency-sensitive, benchmark quality alone is not enough.

GPT-5.5: strong in agentic and environment-based tasks

LLM Stats does not show GPT-5.5 beating Claude Opus 4.7 everywhere. It reports GPT-5.5 ahead on Terminal-Bench 2.0, BrowseComp, OSWorld and CyberGym, while Claude leads on several other benchmarks . That pattern matters because those GPT-5.5 wins are closer to tool-heavy, environment-based work: terminals, browsers, operating-system interactions and security-style tasks.

Artificial Analysis also says GPT-5.5 xhigh leads its Intelligence Index with a score of 60 out of 356 evaluated models . The careful conclusion is not that GPT-5.5 is always better. It is that GPT-5.5 belongs in the first test batch if your product depends on tool orchestration, browsing, terminal use or multi-step agent behavior.

DeepSeek V4/V4-Pro: the strongest argument is value

DeepSeek needs careful wording because the sources use different labels. Mashable discusses DeepSeek V4 Preview as an open-source model that can be downloaded and modified under an MIT license . Artificial Analysis, by contrast, compares DeepSeek V4 Pro Reasoning, High Effort with Claude Opus 4.7 Adaptive Reasoning, Max Effort across intelligence, price, speed, context window and related metrics .

The most striking DeepSeek V4-Pro claim in this source set is cost. Lushbinary reports DeepSeek V4-Pro output pricing at $3.48 per 1 million tokens, compared with $25 for Claude Opus 4.7 and $30 for GPT-5.5 . If that pricing holds for your use case, DeepSeek V4-Pro is worth testing for routing, fallback or batch processing.

But because that price claim comes from a secondary source, it should be verified against official vendor pricing before it drives a contract or production architecture decision.

Kimi K2.6: promising, but not leaderboard-proof

Kimi K2.6 appears in discussions about coding models and agentic workflows, but the public evidence here is not as robust as it is for Claude Opus 4.7 or GPT-5.5. The available references include Substack, Reddit, YouTube and public articles comparing or discussing Kimi K2.6 with Claude Opus 4.7 . That can help identify a model worth testing, but it is not enough to declare a general winner.

The biggest trap is treating Kimi K2 data as Kimi K2.6 data. Artificial Analysis has a page comparing Kimi K2 with Claude 4 Opus, but that is not Kimi K2.6 and not a direct Kimi K2.6 versus Claude Opus 4.7 comparison . For serious decisions, Kimi K2.6 needs to be tested on the same repositories, prompts, toolchains and evaluation harnesses as the other candidates.

Price, context and production fit

LLM Stats reports GPT-5.5 pricing at $5 input and $30 output per 1 million tokens, and Claude Opus 4.7 at $5 input and $25 output per 1 million tokens, with a 2x long-prompt surcharge above 200K tokens for Claude Opus 4.7 . The same source says GPT-5.5 and Claude Opus 4.7 both ship with a 1 million-token context window .

A large context window is useful, but it is not a quality guarantee. For long-context workloads, test retrieval accuracy, instruction following, token cost and answer degradation as prompts grow. For high-volume workloads, run the same traffic sample through each model and compare not just the invoice, but also retries, failures, human review time and downstream corrections.

How to choose from the evidence available

• Start with Claude Opus 4.7 and GPT-5.5 as the quality baseline. Claude scores 57 on Artificial Analysis, GPT-5.5 xhigh is reported at 60, and LLM Stats shows the two models winning different benchmarks .
• Weight GPT-5.5 more heavily for agentic workloads. LLM Stats reports GPT-5.5 ahead on Terminal-Bench 2.0, BrowseComp, OSWorld and CyberGym .
• Weight Claude Opus 4.7 more heavily for certain reasoning and coding evals. LLM Stats reports Claude ahead on GPQA, HLE, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1 .
• Add DeepSeek V4-Pro for value testing. Its reported output price is much lower, but pricing and quality should be validated on your own workload before production use .
• Treat Kimi K2.6 as an experiment, not a proven all-rounder. The public evidence in this set is more heterogeneous and community-heavy than the evidence for Claude Opus 4.7 or GPT-5.5 .

Bottom line

The most trustworthy answer is not a single winner. It is a testing plan.

Use Anthropic to verify Claude Opus 4.7 availability, Artificial Analysis and LLM Stats for structured benchmark signals, Mashable for the DeepSeek V4 Preview open-source context, and community sources only as early indicators for Kimi K2.6 .

For an operational decision, make Claude Opus 4.7 and GPT-5.5 your frontier baseline, add DeepSeek V4-Pro for cost-sensitive trials, and keep Kimi K2.6 in the experimental lane until an independent benchmark tests all four models with the same methodology .