How to choose between GPT-5.5, Claude Opus 4.7, DeepSeek V4 and Kimi K2.6
There is no clean overall champion: GPT 5.5 leads the visible Artificial Analysis Intelligence Index entries at 60 and 59, while Claude Opus 4.7 leads key reasoning heavy scores such as GPQA Diamond and Humanity’s Las... DeepSeek V4’s clearest advantage is cost: public summaries list $1.74 per 1 million input tokens...
There is no clean overall champion: GPT 5.5 leads the visible Artificial Analysis Intelligence Index entries at 60 and 59, while Claude Opus 4.7 leads key reasoning heavy scores such as GPQA Diamond and Humanity’s Las...
DeepSeek V4’s clearest advantage is cost: public summaries list $1.74 per 1 million input tokens and $3.48 per 1 million output tokens, below GPT 5.5 at $5 / $30 and Claude Opus 4.7 at $5 / $25 on the same basis.[1][17]
A practical first test plan is task based: try GPT 5.5 for agentic browsing and terminal workflows, Claude Opus 4.7 for reasoning and review, DeepSeek V4 for high volume API routing, and Kimi K2.6 for open source codi...
GPT-5.5、Claude Opus 4.7、DeepSeek V4、Kimi K2.6 怎麼選？Benchmark 與價格比較AI 生成配圖：比較 GPT-5.5、Claude Opus 4.7、DeepSeek V4 與 Kimi K2.6 的性能與成本取捨。
AI Prompt
Create a landscape editorial hero image for this Studio Global article: GPT-5.5、Claude Opus 4.7、DeepSeek V4、Kimi K2.6 怎麼選？Benchmark 與價格比較. Article summary: 公開數據不支持一個絕對總冠軍：GPT 5.5 在可見 Intelligence Index 60/59、BrowseComp 84.4% 與 Terminal Bench 2.0 82.7% 最突出；Claude Opus 4.7 在 GPQA Diamond 94.2% 與 HLE no tools 46.9% 領先，Kimi K2.6 則缺少完整四方同場數據。[2][7]. Topic tags: ai, llm benchmarks, openai, anthropic, deepseek. Reference image context from search candidates: Reference image 1: visual subject "[Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison](https://www.youtube.com/watch?v=M90iB4hpenI). . [](https://www.youtube.com" source context "Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison - YouTube" Reference image 2: visual subject "[Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison](https://www.youtube.com/watch?v=M90iB4hpenI). ![Image 4](https://
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Ranking GPT-5.5, Claude Opus 4.7, DeepSeek V4 and Kimi K2.6 as if there were one universal winner would be misleading. The public numbers come from different sources, different reasoning settings and different evaluation harnesses. LLM Stats also warns that some GPT-5.5 and Claude Opus 4.7 benchmark scores are provider-reported at high reasoning tiers, making them comparable in shape but not identical in methodology.
The better question is: what job are you hiring the model to do?
Quick recommendation
If your main need is...
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Why
Agentic web browsing, command-line automation, multi-tool workflows
GPT-5.5
It scores 84.4% on BrowseComp and 82.7% on Terminal-Bench 2.0, ahead of the Claude Opus 4.7 and DeepSeek-V4-Pro-Max figures shown in the cited comparison.
Hard reasoning, review, low-tolerance decisions
Claude Opus 4.7
It scores 94.2% on GPQA Diamond and 46.9% on Humanity’s Last Exam no-tools, ahead of GPT-5.5 and DeepSeek-V4-Pro-Max in that table.
High-volume, cost-sensitive API calls
DeepSeek V4
Public pricing is listed at $1.74 per 1 million input tokens and $3.48 per 1 million output tokens, below the same-basis prices for GPT-5.5 and Claude Opus 4.7.
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What is the short answer to "How to choose between GPT-5.5, Claude Opus 4.7, DeepSeek V4 and Kimi K2.6"?
There is no clean overall champion: GPT 5.5 leads the visible Artificial Analysis Intelligence Index entries at 60 and 59, while Claude Opus 4.7 leads key reasoning heavy scores such as GPQA Diamond and Humanity’s Las...
What are the key points to validate first?
There is no clean overall champion: GPT 5.5 leads the visible Artificial Analysis Intelligence Index entries at 60 and 59, while Claude Opus 4.7 leads key reasoning heavy scores such as GPQA Diamond and Humanity’s Las... DeepSeek V4’s clearest advantage is cost: public summaries list $1.74 per 1 million input tokens and $3.48 per 1 million output tokens, below GPT 5.5 at $5 / $30 and Claude Opus 4.7 at $5 / $25 on the same basis.[1][17]
What should I do next in practice?
A practical first test plan is task based: try GPT 5.5 for agentic browsing and terminal workflows, Claude Opus 4.7 for reasoning and review, DeepSeek V4 for high volume API routing, and Kimi K2.6 for open source codi...
Open-source coding-agent experiments and long-horizon coding tests
Kimi K2.6
DocsBot describes it as Moonshot AI’s open-source native multimodal agentic model with a 256K context, but it lacks a full public same-harness comparison against the other three models.
Benchmark and pricing snapshot
DeepSeek’s naming is not perfectly consistent across public summaries. Pricing sources refer to DeepSeek V4 or DeepSeek V4 Pro, while some benchmark tables use DeepSeek-V4-Pro-Max. The table below keeps the source labels rather than treating every configuration as the same model.
Metric
GPT-5.5
Claude Opus 4.7
DeepSeek V4 / V4-Pro-Max
Kimi K2.6
Artificial Analysis Intelligence Index
xhigh 60; high 59.
Adaptive Reasoning, Max Effort 57.
No same-basis score shown in the provided summary.
No same-basis score shown in the provided summary.
BrowseComp
84.4%.
79.3%.
DeepSeek-V4-Pro-Max 83.4%.
No four-way same-harness score found.
Terminal-Bench 2.0
82.7%.
69.4%.
67.9%.
66.70%, but from a separate Kimi K2.6 vs Claude Opus 4.6 vs GPT-5.4 comparison, not a four-way test against GPT-5.5, Claude Opus 4.7 and DeepSeek V4.
SWE-Bench Pro
58.6%.
64.3%.
DeepSeek V4 Pro 55.4%.
58.60%, but Verdent notes a Moonshot in-house harness and a different comparison set.
GPQA Diamond
93.6%.
94.2%.
DeepSeek-V4-Pro-Max 90.1%.
No four-way same-harness score found.
Humanity’s Last Exam, no tools
41.4%; GPT-5.5 Pro 43.1%.
46.9%.
37.7%.
No four-way same-harness score found.
API price, input / output per 1 million tokens
$5 / $30; 1 million-token context window.
$5 / $25; 1 million-token context window.
$1.74 / $3.48; 1 million-token context window.
No same-basis price in the provided sources; DocsBot lists a 256K context.
1. Overall intelligence: GPT-5.5 leads the visible index, but not by enough to settle everything
Artificial Analysis lists the top visible Intelligence Index entries as GPT-5.5 xhigh at 60, GPT-5.5 high at 59, Claude Opus 4.7 Adaptive Reasoning, Max Effort at 57, followed by Gemini 3.1 Pro Preview and GPT-5.4 xhigh, also at 57.
That supports a narrow conclusion: among the visible entries in that summary, GPT-5.5 ranks ahead of Claude Opus 4.7. It does not support a full four-model leaderboard, because the same visible summary does not provide same-basis Intelligence Index scores for DeepSeek V4 or Kimi K2.6.
2. Agents and tool use: GPT-5.5 is the strongest visible pick
For agentic browsing, GPT-5.5 has the edge in the cited comparison, but DeepSeek is close. VentureBeat lists BrowseComp at 84.4% for GPT-5.5, 83.4% for DeepSeek-V4-Pro-Max and 79.3% for Claude Opus 4.7.
The gap is wider on Terminal-Bench 2.0, which Yahoo / Investing.com describes as testing command-line workflows. GPT-5.5 is listed at 82.7%, compared with 69.4% for Claude Opus 4.7 and 67.9% for DeepSeek in VentureBeat’s table.
Kimi K2.6 has a visible Terminal-Bench 2.0 score of 66.70%, but that number comes from a different comparison involving Kimi K2.6, Claude Opus 4.6 and GPT-5.4, not a same-room comparison with GPT-5.5, Claude Opus 4.7 and DeepSeek V4.
3. Reasoning and review: Claude Opus 4.7 has the clearest advantage
On the reasoning-heavy benchmarks shown in the VentureBeat summary, Claude Opus 4.7 leads. It scores 94.2% on GPQA Diamond, ahead of GPT-5.5 at 93.6% and DeepSeek-V4-Pro-Max at 90.1%. On Humanity’s Last Exam no-tools, Claude Opus 4.7 scores 46.9%, compared with GPT-5.5 at 41.4%, GPT-5.5 Pro at 43.1% and DeepSeek-V4-Pro-Max at 37.7%.
LLM Stats reaches a similar pattern for GPT-5.5 versus Claude Opus 4.7: across the 10 benchmarks both providers report, Claude Opus 4.7 leads six and GPT-5.5 leads four. The split is category-based, with Claude ahead on reasoning-heavy and review-grade tests, while GPT-5.5 is ahead on long-running tool-use tests.
4. Coding: Claude leads SWE-Bench Pro, but workflow matters
For software engineering, the headline depends on what kind of coding work you mean. DataCamp’s DeepSeek V4 comparison lists SWE-Bench Pro at 64.3% for Claude Opus 4.7, 58.6% for GPT-5.5 and 55.4% for DeepSeek V4 Pro. Yahoo / Investing.com also reports GPT-5.5 at 58.6% on SWE-Bench Pro and describes that benchmark as evaluating GitHub issue resolution.
Kimi K2.6 deserves its own test if your team is exploring coding agents. Verdent lists Kimi K2.6 at 58.60% on SWE-Bench Pro, 80.20% on SWE-Bench Verified and 89.60% on LiveCodeBench v6. But the same summary says the Kimi K2.6 figures come from Moonshot AI’s official model card and that SWE-Bench Pro used a Moonshot in-house harness.
So the practical coding takeaway is not one-size-fits-all. Claude Opus 4.7 has the highest visible SWE-Bench Pro score in the DeepSeek / GPT-5.5 / Claude table. GPT-5.5 looks stronger for terminal and tool-heavy workflows. Kimi K2.6 is a credible candidate for an open coding-agent trial, but its published numbers should not be dropped into the same four-way leaderboard without qualification.
5. Pricing and context: DeepSeek V4 is the cost outlier
The strongest case for DeepSeek V4 is price. Mashable lists DeepSeek V4 at $1.74 per 1 million input tokens and $3.48 per 1 million output tokens, with a 1 million-token context window. The same summary lists GPT-5.5 at $5 per 1 million input tokens and $30 per 1 million output tokens, and Claude Opus 4.7 at $5 per 1 million input tokens and $25 per 1 million output tokens, both also with 1 million-token context windows.
DataCamp uses the same pricing basis for DeepSeek V4 Pro, GPT-5.5 and Claude Opus 4.7, and lists each at around a 1 million-token context window. On the numbers available here, DeepSeek V4 is materially cheaper than GPT-5.5 and Claude Opus 4.7.
That makes DeepSeek V4 a natural first candidate for high-volume API routing, especially where its quality is already close enough for the task. In the same set of public data, DeepSeek-V4-Pro-Max scores 83.4% on BrowseComp, just behind GPT-5.5 at 84.4%.
Kimi K2.6 does not have a same-basis API price in the provided sources. DocsBot describes it as an open-source native multimodal agentic model from Moonshot AI with 256K context, aimed at long-horizon coding, coding-driven design, autonomous execution and swarm-based orchestration.
A practical deployment strategy: route by task, then test on your own workload
For most product teams, the best answer is not to standardise on one model immediately. Build a small evaluation suite and route tasks by risk, cost and workflow.
Use GPT-5.5 as the premium agentic baseline. It has strong public numbers on BrowseComp and Terminal-Bench 2.0. OpenAI also lists GPT-5.5 at 84.9% on GDPval, 78.7% on OSWorld-Verified and 98.0% on Tau2-bench Telecom, all benchmarks tied to tool use or knowledge-work workflows.
Use Claude Opus 4.7 for reasoning, review and low-tolerance work. It leads the cited GPQA Diamond and Humanity’s Last Exam no-tools comparisons, and LLM Stats says its advantages cluster around reasoning-heavy and review-grade tests.
Use DeepSeek V4 to reduce high-volume API cost. Its public token prices are below GPT-5.5 and Claude Opus 4.7, while its BrowseComp score is close to GPT-5.5 in the cited table.
Put Kimi K2.6 in the open-source coding-agent test pool. The available data show relevant coding and agentic scores, but not a clean four-way same-harness comparison with GPT-5.5, Claude Opus 4.7 and DeepSeek V4.
Key limitations
Not every model has a same-harness benchmark. GPT-5.5, Claude Opus 4.7 and DeepSeek-V4-Pro-Max appear together in some VentureBeat benchmark tables, while Kimi K2.6 mainly appears in a separate comparison against Claude Opus 4.6 and GPT-5.4.
Model settings vary. Artificial Analysis separates GPT-5.5 xhigh and high, lists Claude Opus 4.7 as Adaptive Reasoning, Max Effort, and VentureBeat uses DeepSeek-V4-Pro-Max.
Self-reported and third-party scores are not identical evidence. LLM Stats explicitly cautions that some GPT-5.5 and Claude Opus 4.7 scores are self-reported at each provider’s high reasoning tier and are comparable in shape, not methodology.
Benchmarks are task proxies, not production guarantees. BrowseComp focuses on agentic web browsing, Terminal-Bench 2.0 on command-line workflows, and SWE-Bench Pro on GitHub issue resolution.
Bottom line
If you are shortlisting from public data, GPT-5.5 is the strongest first test for agentic tool use and visible overall ranking; Claude Opus 4.7 is the strongest first test for reasoning-heavy and review-grade work; DeepSeek V4 is the most compelling cost-performance candidate for high-volume APIs; and Kimi K2.6 belongs in open-source coding-agent experiments, but does not yet have enough same-harness evidence for a fair four-way ranking.
Before buying or deploying at scale, run the models against the same real tasks: same prompts, same tools, same context length and the same success criteria. Public benchmarks should decide your testing order. Your workload, error cost and token budget should decide the final route.
