GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6: Which AI Model Wins?

Frontier-model comparisons are easiest to misread when a single benchmark is treated as a universal verdict. The better conclusion from the available evidence is more practical: GPT-5.5 has the strongest aggregate ranking signal, Claude Opus 4.7 wins several hard reasoning and software-engineering rows, DeepSeek V4 has the clearest API cost advantage, and Kimi K2.6 is credible for coding and agentic work but has thinner direct evidence against GPT-5.5 and Opus 4.7.

Quick verdict

If you care most about…	Best-supported pick	Why
Highest aggregate intelligence signal	GPT-5.5	Artificial Analysis lists GPT-5.5 xhigh at 60 and GPT-5.5 high at 59, ahead of Claude Opus 4.7 Adaptive Reasoning Max Effort at 57.
Hard reasoning and software-engineering rows	Claude Opus 4.7, with GPT-5.5 close behind	In VentureBeat’s shared table, Claude leads GPQA Diamond, HLE no-tools, SWE-Bench Pro, and MCP Atlas; GPT-5.5 leads Terminal-Bench 2.0 and base BrowseComp, while GPT-5.5 Pro leads HLE with tools and BrowseComp where that variant is shown.
Lowest listed flagship API cost	DeepSeek V4	Mashable lists DeepSeek V4 at $1.74 per 1M input tokens and $3.48 per 1M output tokens, below GPT-5.5 at $5/$30 and Claude Opus 4.7 at $5/$25.
Disclosed coding and competitive-programming metrics	DeepSeek V4 Pro	Together AI lists DeepSeek V4 Pro at 93.5% LiveCodeBench, Codeforces 3206, 80.6% SWE-Bench Verified, and 76.2% SWE-Bench Multilingual.
Kimi K2.6 evaluation	Promising, but not settled	Kimi K2.6 has useful coding and agentic numbers, but much of the available Kimi-focused evidence compares it with GPT-5.4 and Claude Opus 4.6 rather than GPT-5.5 and Claude Opus 4.7.

Aggregate rankings favor GPT-5.5

The cleanest aggregate signal in the available sources comes from Artificial Analysis. It lists GPT-5.5 xhigh first with an Intelligence Index of 60 and GPT-5.5 high second at 59; Claude Opus 4.7 Adaptive Reasoning Max Effort is listed at 57.

Kimi K2.6 appears below that GPT-5.5/Claude tier in the available composite snippets. OpenRouter lists Kimi K2.6 at 53.9 Intelligence, 47.1 Coding, and 66.0 Agentic, while LLMBase’s DeepSeek V4 Flash High vs Kimi K2.6 comparison lists Kimi at 53.9 Intelligence and 47.1 Coding. That LLMBase comparison lists DeepSeek V4 Flash High at 44.9 Intelligence and 39.8 Coding, but that is the Flash variant, not DeepSeek V4 Pro or Pro-Max.

The caveat is important: the available aggregate ranking gives a clear GPT-5.5-versus-Claude signal, but it does not provide one complete four-way leaderboard row for GPT-5.5, Claude Opus 4.7, DeepSeek V4 Pro-Max, and Kimi K2.6 together.

Shared benchmarks: Claude and GPT-5.5 split the wins

VentureBeat’s shared benchmark table is the most useful source for comparing DeepSeek-V4-Pro-Max, GPT-5.5, GPT-5.5 Pro where shown, and Claude Opus 4.7 on the same rows.

Benchmark	DeepSeek-V4-Pro-Max	GPT-5.5	GPT-5.5 Pro, where shown	Claude Opus 4.7	Best result in this source
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 as a split decision, not a sweep. Claude Opus 4.7 has the stronger case in this table on GPQA Diamond, HLE no-tools, SWE-Bench Pro, and MCP Atlas. GPT-5.5 has the stronger base-model results on Terminal-Bench 2.0 and BrowseComp, and GPT-5.5 Pro is higher where VentureBeat includes it for HLE with tools and BrowseComp.

DeepSeek-V4-Pro-Max is competitive in several rows but does not beat the best GPT-5.5 or Claude Opus 4.7 result in VentureBeat’s shared table. Its closest row is BrowseComp, where it scores 83.4% versus GPT-5.5 at 84.4% and Claude Opus 4.7 at 79.3%.

Coding benchmarks are workload-dependent

For repository-style software engineering, Claude Opus 4.7 has the strongest shared SWE-Bench Pro result in VentureBeat’s table: 64.3%, compared with GPT-5.5 at 58.6% and DeepSeek-V4-Pro-Max at 55.4%.

DeepSeek V4 Pro, however, has the richest disclosed coding profile in the available model listings. Together AI lists DeepSeek V4 Pro at 93.5% LiveCodeBench, Codeforces 3206, 80.6% SWE-Bench Verified, and 76.2% SWE-Bench Multilingual. NVIDIA’s model card also breaks out DeepSeek V4 Flash and V4 Pro variants across benchmarks including GPQA Diamond, HLE, LiveCodeBench, and Codeforces, with V4-Pro Max shown at 93.5 on LiveCodeBench and 3206 on Codeforces.

Kimi K2.6 also has meaningful coding evidence, but the strongest Kimi-focused tables in the available sources mostly compare it with earlier-generation competitors. Lorka lists Kimi K2.6 at 58.6% on SWE-Bench Pro, 54.0% on HLE-Full with tools, 90.5% on GPQA-Diamond, and 79.4% on MMMU-Pro in a table comparing it with GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. Verdent lists Kimi K2.6 at 80.2% on SWE-Bench Verified, 66.7% on Terminal-Bench 2.0, 54.0% on HLE with tools, and 89.6% on LiveCodeBench v6, while also noting that Opus 4.7 leads SWE-Bench Verified at 87.6%.

That makes Kimi K2.6 worth evaluating for coding and agentic workflows, but the available evidence does not support calling it the overall winner against GPT-5.5 or Claude Opus 4.7.

Pricing: DeepSeek V4 has the clearest cost advantage

If API cost is central, DeepSeek V4 has the strongest price argument in the available sources. Mashable lists DeepSeek V4 at $1.74 per 1M input tokens and $3.48 per 1M output tokens, compared with GPT-5.5 at $5 per 1M input tokens and $30 per 1M output tokens, and Claude Opus 4.7 at $5 per 1M input tokens and $25 per 1M output tokens.

Model or variant	Listed input price	Listed output price	Notes
GPT-5.5	$5 per 1M tokens	$30 per 1M tokens	Mashable lists a 1M context window for this comparison.
Claude Opus 4.7	$5 per 1M tokens	$25 per 1M tokens	Mashable lists a 1M context window for this comparison.
DeepSeek V4	$1.74 per 1M tokens	$3.48 per 1M tokens	Mashable lists a 1M context window for this comparison.
DeepSeek V4 Flash	$0.14 per 1M tokens	$0.28 per 1M tokens	LLMBase lists a $0.18 blended price in its DeepSeek V4 Flash High vs Kimi K2.6 comparison.
Kimi K2.6	$0.95 per 1M tokens	$4.00 per 1M tokens	LLMBase lists a $1.71 blended price in the same comparison.

Do not assume every endpoint has the same context limit. Mashable lists 1M context windows for DeepSeek V4, GPT-5.5, and Claude Opus 4.7 in its pricing comparison, while an OpenRouter DeepSeek V4 Pro listing shows 256K max tokens and 66K max output tokens. For production use, verify the exact provider, model variant, and reasoning mode you plan to call.

Best use cases by model

GPT-5.5: best default when aggregate ranking matters

GPT-5.5 is the safest pick if your decision is driven by the available aggregate ranking. Artificial Analysis lists GPT-5.5 xhigh at 60 and GPT-5.5 high at 59, the top two Intelligence Index positions in the provided snippet.

It also performs especially well on two shared task rows in VentureBeat’s table: 82.7% on Terminal-Bench 2.0 and 84.4% on BrowseComp for base GPT-5.5, with GPT-5.5 Pro shown at 90.1% on BrowseComp where that variant appears.

Claude Opus 4.7: strongest fit for several hard reasoning and SWE tasks

Claude Opus 4.7 is close behind GPT-5.5 on the aggregate ranking, with an Artificial Analysis Intelligence Index score of 57 for the Adaptive Reasoning Max Effort setting. In VentureBeat’s shared table, it leads GPT-5.5 and DeepSeek-V4-Pro-Max on GPQA Diamond, HLE no-tools, SWE-Bench Pro, and MCP Atlas.

Anthropic’s own launch material also reports internal research-agent results, including a tied top overall score of 0.715 across six modules and a General Finance score of 0.813 versus 0.767 for Opus 4.6. Because those are internal benchmark claims, they are best treated as supporting context rather than neutral leaderboard evidence.

DeepSeek V4: best value case if the variant fits your workload

DeepSeek V4’s most obvious advantage is price. In Mashable’s comparison, its listed input and output prices are far below GPT-5.5 and Claude Opus 4.7: $1.74 input and $3.48 output per 1M tokens versus GPT-5.5 at $5/$30 and Claude Opus 4.7 at $5/$25.

DeepSeek V4 Pro also has strong disclosed coding metrics, including 93.5% LiveCodeBench, Codeforces 3206, 80.6% SWE-Bench Verified, and 76.2% SWE-Bench Multilingual in Together AI’s listing. The tradeoff is that DeepSeek-V4-Pro-Max trails the top GPT-5.5 or Claude Opus 4.7 result on the shared VentureBeat rows, even when it is close on BrowseComp.

Kimi K2.6: credible for coding and agents, but less proven in this four-way race

Kimi K2.6 is harder to place in a direct four-way ranking because the available Kimi-focused benchmark tables mostly compare it with GPT-5.4 and Claude Opus 4.6 rather than GPT-5.5 and Claude Opus 4.7. Still, the signals are not weak: OpenRouter lists Kimi K2.6 at 53.9 Intelligence, 47.1 Coding, and 66.0 Agentic, while Verdent lists 80.2% SWE-Bench Verified and 89.6% LiveCodeBench v6.

The practical conclusion is not that Kimi K2.6 is outclassed. It is that the direct evidence is thinner. If Kimi’s pricing, deployment route, or agentic behavior fits your stack, it deserves evaluation, but the sources here do not support naming it the overall winner against GPT-5.5 or Claude Opus 4.7.

Caveats before you choose

• Variant names matter. DeepSeek V4 appears in the sources as V4, V4 Flash, V4 Pro, and DeepSeek-V4-Pro-Max, and the reported prices, limits, and benchmark results differ by variant and reasoning setting.
• Kimi comparisons are less direct. The strongest Kimi K2.6 benchmark tables in the available evidence often compare it with GPT-5.4 and Claude Opus 4.6, not GPT-5.5 and Claude Opus 4.7.
• Humanity’s Last Exam no-tools is inconsistent across snippets. LLM Stats and VentureBeat report GPT-5.5 at 41.4% and Claude Opus 4.7 at 46.9%, while Mashable’s GPT-versus-Claude snippet reports GPT-5.5 at 40.6% and Opus 4.7 at 31.2%.
• Internal benchmarks are not the same as independent leaderboards. Anthropic’s Opus 4.7 launch post reports internal research-agent gains, but those results should be read differently from cross-provider public comparisons.
• Pricing and context limits are provider-specific. The same model family can appear with different context windows, token limits, and output caps depending on endpoint and listing.

Bottom line

Pick GPT-5.5 if the available aggregate intelligence ranking is your top criterion. Pick Claude Opus 4.7 if your workload resembles the shared hard reasoning and software-engineering rows where it leads, including GPQA Diamond, HLE no-tools, SWE-Bench Pro, and MCP Atlas. Pick DeepSeek V4 if price-performance is central and you can validate the exact V4 variant you plan to use; its listed API pricing is far lower than GPT-5.5 and Claude Opus 4.7, and DeepSeek V4 Pro has strong disclosed coding metrics. Treat Kimi K2.6 as a credible coding and agentic candidate, but not as a proven overall winner against GPT-5.5 or Claude Opus 4.7 based on the available direct evidence.