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

Putting all four models into one scoreboard is tempting, but the safer reading of the available evidence is simple: choose by workload, not by a single overall rank. The strongest head-to-head table compares DeepSeek V4-Pro-Max, GPT-5.5/GPT-5.5 Pro and Claude Opus 4.7. Kimi K2.6 has useful datapoints, but they come from separate context-window comparisons, BrowseComp, SWE-Bench Pro, a Hugging Face model card and a single practical coding benchmark, so it should be treated as a shortlist candidate rather than a clean same-table winner.

The short version: which model should you test first?

Workload	Test first	Why
Hard reasoning and no-tool Q&A	Claude Opus 4.7	It leads the shared table on GPQA Diamond at 94.2% and Humanity's Last Exam with no tools at 46.9%.
Terminal, browser and tool-use agents	GPT-5.5 or GPT-5.5 Pro	GPT-5.5 leads Terminal-Bench 2.0 at 82.7%, and GPT-5.5 Pro leads BrowseComp at 90.1%.
Software engineering	Start with Claude Opus 4.7, then test GPT-5.5 and Kimi K2.6 on your own repo	Claude leads the shared SWE-Bench Pro/SWE Pro row at 64.3%; LLM Stats also lists Claude Opus 4.7 at 0.64, ahead of GPT-5.5 and Kimi K2.6 at 0.59.
Cost-sensitive, high-volume API use	DeepSeek V4	DeepSeek V4-Pro-Max does not lead the shared table, but reports describe DeepSeek as about one-sixth the cost of the latest U.S. models.
Kimi ecosystem or alternative coding-agent route	Kimi K2.6	Kimi K2.6 shows BrowseComp at 83.2% and SWE-Bench Pro at 0.59, but there is no complete same-source four-model table.
Very long-context workflows	Claude Opus 4.7 or GPT-5.5 before Kimi K2.6	Yahoo/Tech lists GPT-5.5 and Claude Opus 4.7 with a 1 million context window; Artificial Analysis lists Kimi K2.6 at 256k tokens and Claude Opus 4.7 at 1,000k tokens.

The cleanest head-to-head data: Claude, GPT-5.5 and DeepSeek V4-Pro-Max

The table below is the most comparable slice in the available source set. It covers DeepSeek V4-Pro-Max, GPT-5.5/GPT-5.5 Pro and Claude Opus 4.7; GPT-5.5 Pro only appears on some rows, and Kimi K2.6 is not included in this same table. Every number in this table comes from the same comparison.

Benchmark	DeepSeek V4-Pro-Max	GPT-5.5	GPT-5.5 Pro	Claude Opus 4.7	Leader in this table
GPQA Diamond	90.1%	93.6%	—	94.2%	Claude Opus 4.7
Humanity's Last Exam, no tools	37.7%	41.4%	43.1%	46.9%	Claude Opus 4.7
Humanity's Last Exam, with tools	48.2%	52.2%	57.2%	54.7%	GPT-5.5 Pro
Terminal-Bench 2.0	67.9%	82.7%	—	69.4%	GPT-5.5
SWE-Bench Pro / SWE Pro	55.4%	58.6%	—	64.3%	Claude Opus 4.7
BrowseComp	83.4%	84.4%	90.1%	79.3%	GPT-5.5 Pro
MCP Atlas / MCPAtlas Public	73.6%	75.3%	—	79.1%	Claude Opus 4.7

Read this table by task family. Claude Opus 4.7 is strongest here on hard reasoning, no-tool problem solving, software engineering and MCP Atlas. GPT-5.5 is stronger on Terminal-Bench 2.0, while GPT-5.5 Pro leads the tool-assisted Humanity's Last Exam row and BrowseComp. DeepSeek V4-Pro-Max does not rank first in this shared table, but its BrowseComp score of 83.4% is close to GPT-5.5 at 84.4% and above Claude Opus 4.7 at 79.3%.

Kimi K2.6: useful evidence, but not a clean four-way ranking

Kimi K2.6 is not missing from the evidence base; the problem is that its numbers come from different sources, modes and comparison sets. That makes Kimi worth testing, but it also means it should not be forced into the same league table as if every score came from one benchmark run.

Metric	Kimi K2.6 data	Comparator data	Practical reading
Context window	256k tokens	Claude Opus 4.7 is listed at 1,000k tokens in the same Artificial Analysis comparison.	Claude has the much larger listed context window.
BrowseComp	83.2% in Thinking mode	DeepSeek-V4 Pro is listed at 83.4% Pass@1, Think Max.	Kimi and DeepSeek are very close in this source, but GPT-5.5 and Claude Opus 4.7 are not included in that same comparison.
AIME 2026 / APEX Agents	AIME 2026 at 96.4%; APEX Agents at 27.9%	DeepSeek-V4 Pro is shown as not available for those rows.	Useful signal for math and agent tasks, but not a four-model comparison.
SWE-Bench Pro	0.59	Claude Opus 4.7 is 0.64, GPT-5.5 is 0.59 and DeepSeek V4-Pro-Max is 0.55.	Kimi ties GPT-5.5 in this leaderboard, trails Claude and leads DeepSeek.
MMLU-Pro / SimpleQA-Verified	MMLU-Pro 87.1; SimpleQA-Verified 36.9	DS-V4-Pro Max is 87.5 and 57.9; the same Hugging Face table uses Opus-4.6 Max and GPT-5.4 xHigh, not the Opus 4.7 and GPT-5.5 versions discussed here.	Helpful for Kimi-vs-DeepSeek context, but not for a clean comparison with the target Claude and GPT versions.
Practical coding benchmark	87	Claude Opus 4.7 is 97, GPT-5.5 xHigh is 96, DeepSeek V4 Flash is 78 and DeepSeek V4 Pro is 69.	Useful real-world coding signal, but it is one benchmark and should not replace your own repo evaluation.

The fair conclusion is that Kimi K2.6 belongs on the shortlist, especially if you want to test the Kimi line or compare alternative coding-agent approaches. The available data does not support calling it the proven overall winner among all four models.

Price, context window and deployment cost

Benchmarks answer a capability question. Production selection also depends on API pricing, output-token cost, context length, latency, reliability and whether you plan to use a hosted API or consider private deployment. A token is the billing and context unit these systems use; output tokens can dominate cost when the model writes long answers or code.

Model	Verifiable data from the source set	What it means for selection
GPT-5.5	$5 per 1 million input tokens, $30 per 1 million output tokens and a 1 million context window.	Same reported input price as Claude Opus 4.7, but a higher reported output price in that article.
Claude Opus 4.7	$5 per 1 million input tokens, $25 per 1 million output tokens and a 1 million context window; Artificial Analysis also lists Claude Opus 4.7 at 1,000k tokens.	Lower reported output-token price than GPT-5.5 in the Yahoo/Tech article, and a larger listed context window than Kimi K2.6 in the Artificial Analysis comparison.
Kimi K2.6	256k context window.	The listed context window is shorter than Claude Opus 4.7's 1,000k tokens in the same comparison; the provided source set does not give enough complete token-pricing data to compare API cost fairly.
DeepSeek V4	Yahoo/Tech reports DeepSeek at about one-sixth the cost of the latest U.S. models; DataCamp lists DeepSeek V4 Pro as a mixture-of-experts model with 1.6 trillion total parameters, 49 billion active parameters and an 865GB download, while the Flash model has 284 billion total parameters, 13 billion active parameters and a 160GB download.	DeepSeek's appeal is cost if you are using an API. If you are considering private deployment, model size, hardware, inference cost and operations effort matter as much as headline benchmark scores.

The clearest pricing signal is that GPT-5.5 and Claude Opus 4.7 are both reported at $5 per 1 million input tokens, but GPT-5.5 is reported at $30 per 1 million output tokens while Claude Opus 4.7 is reported at $25 per 1 million output tokens. DeepSeek's market pitch, in the same reporting, is roughly one-sixth the cost of the latest U.S. models.

Task-by-task guidance

1. Hard reasoning: start with Claude Opus 4.7

If your workload looks like academic reasoning, complex analysis, high-stakes Q&A or solving problems without external tools, Claude Opus 4.7 has the strongest shared-benchmark case. It scores 94.2% on GPQA Diamond, ahead of GPT-5.5 at 93.6% and DeepSeek V4-Pro-Max at 90.1%. It also leads Humanity's Last Exam with no tools at 46.9%.

2. Terminal, browser and tool-use agents: start with GPT-5.5 or GPT-5.5 Pro

If the workload is an agent that has to operate in a terminal, use a browser, call tools or manage a tool chain, GPT-5.5 looks stronger in the shared table. GPT-5.5 scores 82.7% on Terminal-Bench 2.0, compared with Claude Opus 4.7 at 69.4% and DeepSeek V4-Pro-Max at 67.9%. GPT-5.5 Pro also leads BrowseComp at 90.1%.

3. Software engineering: Claude leads, but do not skip your own repo eval

On the shared benchmark table, Claude Opus 4.7 scores 64.3% on SWE-Bench Pro/SWE Pro, ahead of GPT-5.5 at 58.6% and DeepSeek V4-Pro-Max at 55.4%. LLM Stats points in the same direction: Claude Opus 4.7 is listed at 0.64 on SWE-Bench Pro, while GPT-5.5 and Kimi K2.6 are both 0.59 and DeepSeek V4-Pro-Max is 0.55.

Still, coding benchmarks can move with the repository, language, test harness, prompt style and agent setup. One practical coding benchmark lists Claude Opus 4.7 at 97, GPT-5.5 xHigh at 96, Kimi K2.6 at 87, DeepSeek V4 Flash at 78 and DeepSeek V4 Pro at 69; that is useful signal, but it should not be the only basis for a production decision.

4. Cost-sensitive high-volume use: DeepSeek V4 deserves a serious trial

If your bottleneck is cost per call rather than winning every frontier benchmark, DeepSeek V4 is a rational candidate. In the shared table, DeepSeek V4-Pro-Max is near the frontier on several tasks but does not rank first; separately, reporting describes DeepSeek as roughly one-sixth the cost of the latest U.S. models.

That cost story needs to be separated from deployment reality. DeepSeek V4 Pro is listed by DataCamp as a large mixture-of-experts model with 1.6 trillion total parameters, 49 billion active parameters and an 865GB download. If you are only buying API calls, that may be someone else's infrastructure problem. If you are evaluating private deployment, it becomes part of the total cost.

5. Kimi K2.6: put it on the shortlist and rerun your own tasks

Kimi K2.6 has enough evidence to be interesting. DocsBot lists Kimi K2.6 at 83.2% on BrowseComp, very close to DeepSeek-V4 Pro at 83.4% in the same comparison. LLM Stats lists Kimi K2.6 at 0.59 on SWE-Bench Pro, tied with GPT-5.5. A practical coding benchmark lists Kimi K2.6 at 87.

What Kimi lacks in this source set is a complete, same-source, same-setting benchmark table against Claude Opus 4.7, GPT-5.5 and DeepSeek V4-Pro-Max. Until that exists, Kimi K2.6 is best viewed as a high-potential candidate, not a proven four-model champion.

Why you should not over-read the rankings

1. Kimi K2.6 does not have a complete same-table comparison here. The strongest shared table covers DeepSeek V4-Pro-Max, GPT-5.5/GPT-5.5 Pro and Claude Opus 4.7, while Kimi has to be pieced together from Artificial Analysis, DocsBot, LLM Stats, Hugging Face and a practical coding benchmark.
2. Version and mode labels vary. The source set includes labels such as GPT-5.5 Pro, GPT-5.5 xHigh, DeepSeek-V4 Pro, DeepSeek V4-Pro-Max, Kimi Thinking and Claude Opus 4.7 Adaptive Reasoning/Max Effort. Those should not be treated as identical settings.
3. Score formats are not always comparable. The shared table reports SWE-Bench Pro/SWE Pro as percentages, while LLM Stats reports SWE-Bench Pro in a 0.xx format. Compare models inside the same source first, then verify with your own tests.
4. Pricing evidence is uneven. GPT-5.5 and Claude Opus 4.7 have clear reported input and output token prices in the Yahoo/Tech article, DeepSeek has the one-sixth-cost claim, and Kimi K2.6 does not have enough complete token-pricing evidence in the provided source set.

Bottom line

If you need a one-sentence decision rule: Claude Opus 4.7 is the strongest first pick for hard reasoning and software engineering benchmarks; GPT-5.5/GPT-5.5 Pro is the strongest first pick for terminal, browser and tool-use work; DeepSeek V4-Pro-Max is the cost-performance candidate; and Kimi K2.6 is promising enough to test, but not backed by the same complete four-way benchmark evidence.

For a real deployment, do not stop at public benchmarks. Run the same evaluation set across all four models: your repository, your bug tickets, your research workflow, your tool permissions, your context length, your latency target, your failure tolerance and your token budget. That is where a benchmark comparison becomes a product decision.